detecting parkinson's disease with opencv, computer vision, and the spiral/wave test - pyimagesearch

detecting parkinson's disease with opencv, computer vision, and the spiral/wave test - pyimagesearch

While I am familiar with Parkinsons disease, I had not heard of the geometric drawing test a bit of research led me to a 2017 paper, Distinguishing Different Stages of Parkinsons Disease Using Composite Index of Speed and Pen-Pressure of Sketching a Spiral, by Zham et al.

The researchers found that the drawing speed was slower and the pen pressure lower among Parkinsons patients this was especially pronounced for patients with a more acute/advanced forms of the disease.

Originally, Joao wanted to apply deep learning to the project, but after consideration, I carefully explained that deep learning, while powerful, isnt always the right tool for the job! You wouldnt want to use a hammer to drive in a screw, for instance.

While Parkinsons cannot be cured, early detection along with proper medication can significantly improve symptoms and quality of life, making it an important topic as computer vision and machine learning practitioners to explore.

The researchers found that the drawing speed was slower and the pen pressure lower among Parkinsons patients this was especially pronounced for patients with a more acute/advanced forms of the disease.

While it would be challenging, if not impossible, for a person to classify Parkinsons vs. healthy in some of these drawings, others show a clear deviation in visual appearance our goal is to quantify the visual appearance of these drawings and then train a machine learning model to classify them.

Well be reviewing a single Python script today: detect_parkinsons.py . This script will read all of the images, extract features, and train a machine learning model. Finally, results will be displayed in a montage.

To start, we dont have much training data, only 72 images for training. When confronted with a lack of tracking data we typically apply data augmentation but data augmentation in this context is also problematic.

HOG is a structural descriptor that will capture and quantify changes in local gradient in the input image. HOG will naturally be able to quantify how the directions of a both spirals and waves change.

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In this tutorial, you learned how to detect Parkinsons disease in geometric drawings (specifically spirals and waves) using OpenCV and computer vision. We utilized the Histogram of Oriented Gradients image descriptor to quantify each of the input images.

Its also interesting to note that the Random Forest trained on the spiral dataset obtained 76.00% sensitivity, meaning that the model was capable of predicting a true positive (i.e., Yes, the patient has Parkinsons) nearly 76% of the time.

Enter your email address below to get a .zip of the code and a FREE 17-page Resource Guide on Computer Vision, OpenCV, and Deep Learning. Inside you'll find my hand-picked tutorials, books, courses, and libraries to help you master CV and DL!

Hi there, Im Adrian Rosebrock, PhD. All too often I see developers, students, and researchers wasting their time, studying the wrong things, and generally struggling to get started with Computer Vision, Deep Learning, and OpenCV. I created this website to show you what I believe is the best possible way to get your start.

Hmmm.. I understand. By the way great tutorial and thank you for providing such great learning resources specially for CV and ML freshers like me, you really are an inspiration. Will download the code right away and will try to study other feature training methods with it.

Hi Adrian, Myself Sanchit, First of all, thank you for all the awesome tutorials, I am new to ML and AI, and I got into it because I saw real-world problems which needed to be solved and I see Computer Vision is a toolkit for solving it, I want to know how to learn to identify whats needed and whats not, I mean this tutorial starts with a list of libraries required to make this tutorial work, I understand you have years of experience in CV and that may be the factor, but is a general rule of thumb that can be followed by inexperienced people like me to know which libraries to pick up, whats really required ? Can you suggest me a road map to follow to reach this point? I am a total rookie please take that into consideration.

I would strongly encourage you to read through Practical Python and OpenCV. That book will help you learn the fundamentals and enable you to be successful with basic computer vision projects. From there I can provide you with further suggestions.

Yes, it is indeed a very valid point that DL is not the answer to everything. However, the post also seems to be cultivating the myth that DL needs a lot of data, while in reality this is not always the case!

Keep in mind that youre performing transfer learning via feature extraction. Youre not actually training or fine-tuning the network. By definition that requires less data and enables you to get around not having enough data to reasonably train a model from scratch.

The number of dimensions produced from a HoG feature could mount up in the array for a larger dataset. Would you have any recommendations for memory management when there is a chance you could be memory constrained?

Hi Adrian, An unintentional opportunity I came into contact with PyImageSearch and learned a lot from it in just 4 days. I am a student from Nanjing, China. I have a deeper understanding of deep learning and convolutional networks in one of your blog posts. I intend to go through all of your blog posts (this seems like a big project).Finally, my sincere thanks (With Google translationhelp)

Hai Adrian great tutorial. I am planning to make an interface for this idea. Can I use JavaScript and html canvas to build same thing(Train and test images)? If I do so is it better to use HoG feature descriptor or anything else? The problem with canvas is thickness of the lines never going to be changed. Is there is any chances for problem if I do so? I expect your reply for clarifying my doubts. Thanks in advance.

At the time I was receiving 200+ emails per day and another 100+ blog post comments. I simply did not have the time to moderate and respond to them all, and the sheer volume of requests was taking a toll on me.

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PyImageSearch University is really the best Computer Visions "Masters" Degree that I wish I had when starting out. Being able to access all of Adrian's tutorials in a single indexed page and being able to start playing around with the code without going through the nightmare of setting up everything is just amazing. 10/10 would recommend.

how to develop your first xgboost model in python

how to develop your first xgboost model in python

An alternate way to install XGBoost if you cannot use pip or you want to run the latest code from GitHub requires that you make a clone of the XGBoost project and perform a manual build and installation.

This is a good dataset for a first XGBoost model because all of the input variables are numeric and the problem is a simple binary classification problem. It is not necessarily a good problem for the XGBoost algorithm because it is a relatively small dataset and an easy problem to model.

Finally, we must split the X and Ydata into a training and test dataset. The training set will be used to prepare the XGBoost model and the test set will be used to make new predictions, from which we can evaluate the performance of the model.

For this we will use the train_test_split() function from the scikit-learn library. We also specify a seed for the random number generator so that we always get the same split of data each time this example is executed.

By default, the predictions made by XGBoost are probabilities. Because this is a binary classification problem, each prediction is the probability of the input pattern belonging to the first class. We can easily convert them to binary class values by rounding them to 0 or 1.

Now that we have used the fit model to make predictions on new data, we can evaluate the performance of the predictions by comparing them to the expected values. For thiswe will use the built in accuracy_score() function in scikit-learn.

Note: Your results may vary given the stochastic nature of the algorithm or evaluation procedure, or differences in numerical precision. Consider running the example a few times and compare the average outcome.

Note: Your results may vary given the stochastic nature of the algorithm or evaluation procedure, or differences in numerical precision. Consider running the example a few times and compare the average outcome.

Thanks a lot for your quick reply. It is my mistake as I am confused with 0:8 because I am also learning R recently. In R, the last number of 0:8 is included while it is excluded in Python. I should have checked the shape.

I have learned the basics of machine learning through online courses, but there is still a gap between what I learned in the courses and the practical problems such as the competitions on Kaggle. Can you share some insights?

I have a list of things to try in the following post, it talks about deep learning but the techniques are general enough for most methods: http://machinelearningmastery.com/improve-deep-learning-performance/

hello, thanks for the fantastic explanation!! I have a query. Can we get the list of significant variables that entered in the model? How do we read the feature_importances_? Also, how to fin-tune the xgboost model? Thanks again!

Hello. Thanks for the explanation! Can you tell me if I can see the list of variables entering in the model. Also, how do we fine tune the model further?? Once we have the xgboost model..how do we productionise it? In logistic regression we get an equation which can be automated to run in real time production, what do we get in xgboost?

I am using predict_proba to create predicted probabilities by xgboost model. Can I save these probs in the same train data on which model is built so that I can further create reports to show management about validations of the scorecard.

Thankyou for your post. It was really helpful.But can you tell me why do I get ImportError: cannot import name XGBClassifier when I run this code?i have installed XG Boost successfully and I still have this error. Please help me.

Hello Dr Jason, thanks for the quick cool tutorial. It is fundamental and very beneficial. one question, how do I use GPU for training and prediction purposes in XGBoost? I am working on large dataset. thanks a lot in advance.

Gee, the 20 or so lines of code is the basic recipe for almost all supervised learning tasks and XGBoost is like the default algorithm. I wish there is a way I could double bookmark this page. Well done!

XGBClassifiers default objective is binary:logisitc. For binary:logistic, is its objective function the summation of logloss? If so, why XGBoost use error(accuracy score) as the default evaluation metric instead of logloss?

Thanks for very nice tutorial. I would appreciate, if you give me advice. I have vibration data (structured format). I am using deep learning Keras using tensorflow. But I read that Specifically, gradient boosting is used for problems where structured data is available, whereas deep learning is used for perceptual problems such as image classification.

I am getting correct prediction but how can I get the score of the prediction correctly. Even I used predict_proba of xgboost & getting all the scores but is this the way to get the score of my prediction or some other way is there?

Hi! Im currently experimenting with XGBoost for an important project and have uploaded a question on StackOverflow. I just read this post and it is clearer to me now, but you do not use the xgboost.train method. It this included in the XGBRegressor wrapper? I did use xgboost.train, which gave me an error, while xgboost.fit does not produce this error. Could you maybe take a look at it? https://stackoverflow.com/questions/50426680/xgboost-gives-keyerror-best-msg

I am using XGBRegressor wrapper to predict the sales of a product, there are 50 products, I want to know the coefficient as in linear regression to see which product sales is affecting how much to the dependent sales variable. Let say Y = B1X1 + B2X2 + .. BnXn + C , i want the values of B1,B2,.Bn from tree regressor(XGBRegressor).

Jason, thanks for the great article (and site) I have a text classification problem that I normally use Logistic Regression to solve. So Im used to transforming the features in order to fit a model, but I normally dont have to do anything to the text labels. The labels are text categories (e.g. labels = [cancel, change, contact support, etc]. I am now receiving error

cm = confusion_matrix(Y_Testshaped, predictions) print(F1 : + str(f1_score(Y_Testshaped, predictions,average=None)) ) print(Precision : + str(precision_score(Y_Testshaped, predictions,average=None)) ) print(Recall : + str(recall_score(Y_Testshaped, predictions,average=None)) )

When I put test-size = 0.2, then the model accuracy increases. It shows the accuracy_score = 81.17% and when I take test-size = 0.15 then accuracy_score = 81.90% and if I take test-size = 0.1 then accuracy_score = 80.52%. So, is it good to take the test-size = 0.15 as it increases the accuracy_score? I normally see the test-size = 0.2 or 0.3 or in-between. So, for good model should I select that model which gives me higher model accuracy_score? If not, why?

I am new to machine learning, but have a familiarity w/ regression. So what i take from the output of this model is that these variables (X), are 77.95% accurate in predicting Y. My question is how would i apply this data? Can in create a function that i can input these variables (X), to predict the probability for someone to become stricken with diabetes Y?

Thanks for the tutorial, I ran my train/test data with the default param on the xgboost and GradientBoostingClassifier from sklearn, they have same results but xgboost is slower than GB in terms of training and testing ( around 30% difference ).

Hi, I am trying to convert my X and y into xgb,DMatix to make computation faster. My X has dimensions (1020, 421) and my y (1020,1). I get an error and dont know where my problem is. Id appreciate if you could help.

I get this error: File C:\Users\AU529763\AppData\Local\Continuum\anaconda3\lib\site-packages\sklearn\utils\validation.py, line 797, in column_or_1d raise ValueError(bad input shape {0}.format(shape))

# load data # split data into (X_train, X_test, y_train, y_test) from xgboost import XGBClassifier model = XGBClassifier(learnin_rate=0.2, max_depth= 8,) eval_set = [(X_test, y_test)] model.fit(X_train, y_train, eval_metric=auc, early_stopping_rounds=50, eval_set=eval_set, verbose=True) y_pred = model.predict(X_test)

# load data # split data into (X_train, X_test, y_train, y_test) import xgboost as xgb dtrain = xgb.DMatrix(X_train,y_train) dtest = xgb.DMatrix(X_test,y_test) eval_set = [(X_test, y_test)] param = {learnin_rate:0.2,max_depth: 8, eval_metric:auc, boost:gbtree, objective: binary:logistic, } num_round = 300 bst = xgb.train(param, dtrain, num_round)

I heard we can use xgboost to extract the most important features and fit the logistic regression with those features. For example if we have a dataset of 1000 features and we can use xgboost to extract the top 10 important features to improve the accuracy of another model. such Logistic regression, SVM, the way we use RFE.

First of all, thank u so much of such great content. Actually, Ive trying to implement a multi-class text classification, for that, Ive tried to generate the word embeddings using the Word2Vec model, have u got any other suggestions to generate word embeddings ??

Hi im working with a dataset with a shape of (7026,63) i tried to run xgboost, gradientboosting and adaboost classifiers on it however it returns a low accuracy rate i tried to tune the parameters a bit but stil ada gave me 60% and xgboost gave me 45% as for the gradient boosting it gave me 0.023 i would very much appreciate it if you coulx answer as to why its not working well.

so, lets say that our researchers go back and acquire new data from this population, and now want you to feed that new data into your model to predict the risk of diabetes on the current population. Would you just split new_data in the same manner (z_train and z_test) and feed it into your refit your model?

Hi Jason, I am trying to build a simple XGBoost binary classifier using your model with my own dataset. The dataset I am working with has about 18000 inputs, 30 features, and 1 label for classes. By making use of your code, when trying to compile predictions = [round(value) for value in y_pred], I get the error: type bytes doesnt define __round__ method.

Another issue is that when I run the model I always get the error: You appear to be using a legacy multi-label data representation. Sequence of sequences are no longer supported; use a binary array or sparse matrix instead the MultiLabelBinarizer transformer can convert to this format.

Does this have to do with the way I am defining the features and targets for the training and testing samples? I am doing this by defining them as features = df.drop(class, axis=1) and targets = df[target_class] and then I am defining the train and test sample size with X_train, X_test, y_train, y_test = train_test_split(features, targets, test_size=0.33, random_state=7).

IndexError Traceback (most recent call last) in 1 # fit model on training data 2 model = XGBClassifier() -> 3 model.fit(X_train, y_train,sample_weight=None) 4 print(model)

~\Anaconda2\envs\mypython3\lib\site-packages\xgboost\sklearn.py in fit(self, X, y, sample_weight, eval_set, eval_metric, early_stopping_rounds, verbose, xgb_model, sample_weight_eval_set, callbacks) 717 evals = () 718 > 719 self._features_count = X.shape[1] 720 721 if sample_weight is not None:

I played around with variables for learning and changing parameters of XGBClassifier did not improve accuracy, however, I decreased test_size to 0.14 (I was trying different values) and accuracy peaked at 84%. I used Python 3.6.8 with 0.9 XGBoost lib.

What if I want to label a single row with XGB ? Ive trained my XGB model on a dataset (cardiovascular disease from Kaggle) with 13 features +1 target (0/1). I have an array with 13 values which I want to be predicted (1 row x 13 columns)

Is it possible to use support vector machines as base learners in the xgbclassifier? I tried out gbtree and gblinear and surprisingly gblinear beats gbtree in several metrics for my breast cancer classification dataset. Is that possible since gblinear can only make linea relationships, while gbtrees can also consider non-linear relationships?

I want to predict percentages, so I have target values in the range [0,1]. The problem is reg:linear gives output out of the range. I saw in stackoverflow, somebody suggested use reg:logistic with XGBRegressor() class. I tried reg:logistic and the results are really promising! But I dont have a valid ground to do that. Do you think it is okay to apply reg:logistic or is it non-sense? Thanks a lot!

def norm_under(self, normalizar, under): if normalizar & under: steps = [(Norma, StandardScaler()), (over, SMOTE(sampling_strategy=0.1)), (under, RandomUnderSampler(sampling_strategy=0.5)), (Class, self.classifier)] elif normalizar: steps = [(Norma, StandardScaler()), (over, SMOTE(sampling_strategy=0.1)), (Class, self.classifier)] elif under: steps = [(over, SMOTE(sampling_strategy=0.1)), (under, RandomUnderSampler(sampling_strategy=0.5)), (Class, self.classifier)] else: steps = [(over, SMOTE(sampling_strategy=0.1)), (Class, self.classifier)] return steps

steps = self.norm_under(normalizar, under) pipeline = Pipeline(steps=steps) pipeline.fit(X_train, y_train) pred = pipeline.predict(X_test) print(Acuracia do {}: {} .format(self.name, accuracy_score(y_test, pred))) print(Mdia da curva ROC_AUC do {}: {} .format(self.name, mean(roc_auc_score(y_test, pred)))) print(F1 score do {}: {} .format(self.name, f1_score(y_test, pred, average=macro))) return pred

steps = self.norm_under(normalizar, under) pipeline = Pipeline(steps=steps) kfold = StratifiedKFold(n_splits=10, random_state=42) scorers = {accuracy_score: make_scorer(accuracy_score), roc_auc_score: make_scorer(roc_auc_score), f1_score: make_scorer(f1_score, average=macro) } resultado = cross_validate(pipeline, X_train, y_train, scoring=scorers, cv=kfold) for name in resultado.keys(): media_scorers = np.average(resultado[name]) print({} do {}: {} .format(name, self.name, media_scorers))

KeyError Traceback (most recent call last) /usr/local/lib/python3.6/dist-packages/sklearn/metrics/_scorer.py in _cached_call(cache, estimator, method, *args, **kwargs) 54 try: > 55 return cache[method] 56 except KeyError:

ValueError Traceback (most recent call last) 19 frames /usr/local/lib/python3.6/dist-packages/xgboost/core.py in _validate_features(self, data) 1688 1689 raise ValueError(msg.format(self.feature_names, -> 1690 data.feature_names)) 1691 1692 def get_split_value_histogram(self, feature, fmap=, bins=None, as_pandas=True):

ValueError: feature_names mismatch: [f0, f1, f2, f3, f4, f5, f6] [step, amount, oldbalanceOrg, newbalanceOrig, oldbalanceDest, newbalanceDest, TRANSFER] expected f1, f6, f3, f2, f0, f4, f5 in input data training data did not have the following fields: oldbalanceDest, amount, oldbalanceOrg, step, TRANSFER, newbalanceOrig, newbalanceDest

Thanks for the clear explaination. i am new to Machine learning. I created a model with XGBRegressor and trained it. I would like to get the optimal bias and residual for each feature and use it in the front end of my app as linear regression. will that be possible? if so, How can I achieve it. Thanks again for your help.

Im using XGboost to train a multi-class dataset and Im getting very poor overall accuracy (70%), However, when using SVM+TFIDF I got a better accuracy of 79%. Is it because of my high vector dimensions ( using tri-grams) ? or maybe parameter tuning? Isnt XGBoost supposed to perform better or even the same as SVM? but not worse

I explored a lot on the web and came across options such as RankSVM, LamdaRank, XGBRanker, etc. but only to find that they dont actually work either resulting in errors or are hard to implement(i.e., cant directly adapt to my problem).

However, upon re-running the same classifier multiple times, the accuracy were varying from 77% to 79% and that is because of the random selection of observations to build a boosting tree based on the subsample value. Correct me if I am wrong here.

ensemble machine learning algorithms in python with scikit-learn

ensemble machine learning algorithms in python with scikit-learn

A standard classification problem used to demonstrate each ensemble algorithm is the Pima Indians onset of diabetes dataset. It is a binary classification problem where all of the input variables are numeric and have differing scales.

Note: Your results may vary given the stochastic nature of the algorithm or evaluation procedure, or differences in numerical precision. Consider running the example a few times and compare the average outcome.

Note: Your results may vary given the stochastic nature of the algorithm or evaluation procedure, or differences in numerical precision. Consider running the example a few times and compare the average outcome.

Samples of the training dataset are taken with replacement, but the trees are constructed in a way that reduces the correlation between individual classifiers. Specifically, rather than greedily choosing the best split point in the construction of the tree, only a random subset of features are considered for each split.

Note: Your results may vary given the stochastic nature of the algorithm or evaluation procedure, or differences in numerical precision. Consider running the example a few times and compare the average outcome.

Note: Your results may vary given the stochastic nature of the algorithm or evaluation procedure, or differences in numerical precision. Consider running the example a few times and compare the average outcome.

Note: Your results may vary given the stochastic nature of the algorithm or evaluation procedure, or differences in numerical precision. Consider running the example a few times and compare the average outcome.

Note: Your results may vary given the stochastic nature of the algorithm or evaluation procedure, or differences in numerical precision. Consider running the example a few times and compare the average outcome.

AdaBoost was perhaps the first successful boosting ensemble algorithm. It generally works by weighting instances in the dataset by how easy or difficult they are to classify, allowing the algorithm to pay or or less attention to them in the construction of subsequent models.

Note: Your results may vary given the stochastic nature of the algorithm or evaluation procedure, or differences in numerical precision. Consider running the example a few times and compare the average outcome.

Note: Your results may vary given the stochastic nature of the algorithm or evaluation procedure, or differences in numerical precision. Consider running the example a few times and compare the average outcome.

Stochastic Gradient Boosting (also called Gradient Boosting Machines) are one of the most sophisticated ensemble techniques. It is also a technique that is proving to be perhaps of the the best techniques available for improving performance via ensembles.

Note: Your results may vary given the stochastic nature of the algorithm or evaluation procedure, or differences in numerical precision. Consider running the example a few times and compare the average outcome.

Note: Your results may vary given the stochastic nature of the algorithm or evaluation procedure, or differences in numerical precision. Consider running the example a few times and compare the average outcome.

It works by first creating two or more standalone models from your training dataset. A Voting Classifier can then be used to wrap your models and average the predictions of the sub-models when asked to make predictions for new data.

The predictions of the sub-models can be weighted, but specifying the weights for classifiers manually or even heuristically is difficult. More advanced methods can learn how to best weight the predictions from submodels, but this is called stacking (stacked generalization) and is currently not provided in scikit-learn.

Note: Your results may vary given the stochastic nature of the algorithm or evaluation procedure, or differences in numerical precision. Consider running the example a few times and compare the average outcome.

Note: Your results may vary given the stochastic nature of the algorithm or evaluation procedure, or differences in numerical precision. Consider running the example a few times and compare the average outcome.

Would you use something like the pickle package? Or is there a way to spell out the scoring algorithm (IF-ELSE rules for decision tree, or the actual formula for logistic regression) and use the formula for future scoring purposes?

I wrote the code below. However, in your snippet, I see that you did not specify base_estimator in the AdaBoostClassifier. Any particular reason? Is there a default value for this parameter (CART??)?

#Boosting AdaBoost algo num_trees4 = 30 cart2 = DecisionTreeClassifier() model4 = AdaBoostClassifier(base_estimator=cart2, n_estimators=num_trees4,random_state=seed) results4 = cross_val_score(model4, X, Y, cv=kfold, scoring=scoring) print(AdaBoost Accuracy: %f)%(results4.mean())

The performance of any machine learning algorithm is stochastic, we estimate performance in the range. It is best practice to run a give configuration many times and take the mean and standard deviation reporting the range of expected performance on unseen data.

What I understand is that ensembles improve the result if they make different mistakes. In my below result of two models. The first model performs well in one class while the second model performs well on the other class. When I ensemble them, I get lower accuracy. Is that possible or I am doing something wrong.

I would like to use voting with SVM as you did, however scaling data SVM gives me better results and its simply much faster. And from here comes the question: How can I scale just parto of the data for algorithms such as SVM, and leave non-slcaed data for XGB/Random forest and on top of it use ensembles. I have tried using Pipeline to first scale the data for SVM and then use Voting but it seams not working. Any comment would be helpful

1: I have 2 different training datasets to train my networks on: vectors of prosodic data, and word embeddings of textual data. The 2 training sets are stored in two different np.arrays with different dimensionality. Is there any way to make VotingClassifier accept X1,and X2 except of a single X? (y is the same for both X1 and X2, and naturally they are of the same length)

If the original inputs are high-dimensional (images and sequences), you could try training a neural net to combine the predictions as part of training each sub-model. You can merge each network using a Merge layer in Keras (deep learning library), if your sub-models were also developed in Keras.

from sklearn import model_selection from sklearn.ensemble import BaggingClassifier from sklearn.tree import DecisionTreeClassifier kfold=model_selection.KFold(n_splits=10) dt=DecisionTreeClassifier() model=BaggingClassifier(base_estimator=dt,n_estimators=10,random_state=5) result=model_selection.cross_val_score(model,x,y,cv=kfold)

Machine learning algorithms are stochastic, meaning they give different results each time they are run. This is a feature, not a bug. See this post for more details: https://machinelearningmastery.com/randomness-in-machine-learning/

AGE Haemoglobin RBC Hct Mcv Mch Mchc Platelets WBC Granuls Lymphocytes Monocytes disese 3 9.6 4.2 28.2 67 22.7 33.9 3.75 5800 44 50 6 Positive 11 12.1 4.3 33.7 78 28.2 36 2.22 6100 73 23 4 Positive 2 9.5 4.1 27.9 67 22.8 34 3.64 5100 64 32 4 Positive 4 9.9 3.9 27.8 71 25.3 35.6 2.06 4900 65 32 3 Positive 14 10.7 4.4 31.2 70 24.2 34.4 3 7600 50 44 6 Negative 7 9.8 4.2 28 66 23.2 35.1 1.95 3800 28 63 9 Negative 8 14.6 5 39.2 77 28.7 37.2 3.06 4400 58 36 6 Negative 4 12 4.5 33.3 74 26.5 35.9 5.28 9500 40 54 6 Negative 2 11.2 4.6 32.7 70 24.1 34.3 2.98 8800 38 58 4 Negative 1 9.1 4 27.2 67 22.4 33.3 3.6 5300 40 55 5 Negative 11 14.8 5.8 42.5 72 25.1 34.8 4.51 17200 75 20 5 Negative

# Define some color for the plotting almost_black = #262626 palette = sns.color_palette() data = (mdata.csv) dataframe = pandas.read_csv(data) array = dataframe.values X = array[:,0:12] y = array[:,12]

# Generate the dataset #X, y = make_classification(n_classes=2, class_sep=2, weights=[0.1, 0.9], # n_informative=3, n_redundant=1, flip_y=0, # n_features=10, n_clusters_per_class=1, # n_samples=500, random_state=10) print (X, y) plt.show()

ax1.scatter(X_vis[y == 0, 0], X_vis[y == 0, 1], label=Class #0, alpha=0.5, edgecolor=almost_black, facecolor=palette[0], linewidth=0.15) ax1.scatter(X_vis[y == 1, 0], X_vis[y == 1, 1], label=Class #1, alpha=0.5, edgecolor=almost_black, facecolor=palette[2], linewidth=0.15) ax1.set_title(Original set)

ax2.scatter(X_res_vis[y_resampled == 0, 0], X_res_vis[y_resampled == 0, 1], label=Class #0, alpha=.5, edgecolor=almost_black, facecolor=palette[0], linewidth=0.15) ax2.scatter(X_res_vis[y_resampled == 1, 0], X_res_vis[y_resampled == 1, 1], label=Class #1, alpha=.5, edgecolor=almost_black, facecolor=palette[2], linewidth=0.15) ax2.set_title(SMOTE ALGORITHM Malaria regular) daata after resample print (X_resampled, y_resampled) plt.show()

File /usr/local/lib/python2.7/dist-packages/imblearn/over_sampling/smote.py, line 360, in _sample_regular nns = self.nn_k_.kneighbors(X_class, return_distance=False)[:, 1:] File /home/sajana/.local/lib/python2.7/site-packages/sklearn/neighbors/base.py, line 347, in kneighbors (train_size, n_neighbors) ValueError: Expected n_neighbors <= n_samples, but n_samples = 5, n_neighbors = 6

and code : import pandas import matplotlib.pyplot as plt from sklearn import model_selection from sklearn.ensemble import GradientBoostingClassifier from sklearn.metrics import accuracy_score data = (dataset160.csv) dataframe = pandas.read_csv(data) array = dataframe.values X = array[:,0:12] Y = array[:,12] print (X, Y) plt.show() seed = 7 num_trees = 100 kfold = model_selection.KFold(n_splits=10, random_state=seed) model = GradientBoostingClassifier(n_estimators=num_trees, random_state=seed) results = model_selection.cross_val_score(model, X, Y, cv=kfold) print(results.mean()) print(results)

AGE Haemoglobin RBC Hct Mcv Mch Mchc Platelets WBC Granuls Lymphocytes Monocytes disese 3 9.6 4.2 28.2 67 22.7 33.9 3.75 5800 44 50 6 Positive 11 12.1 4.3 33.7 78 28.2 36 2.22 6100 73 23 4 Positive 2 9.5 4.1 27.9 67 22.8 34 3.64 5100 64 32 4 Positive 4 9.9 3.9 27.8 71 25.3 35.6 2.06 4900 65 32 3 Positive 14 10.7 4.4 31.2 70 24.2 34.4 3 7600 50 44 6 Negative 7 9.8 4.2 28 66 23.2 35.1 1.95 3800 28 63 9 Negative 8 14.6 5 39.2 77 28.7 37.2 3.06 4400 58 36 6 Negative 4 12 4.5 33.3 74 26.5 35.9 5.28 9500 40 54 6 Negative 2 11.2 4.6 32.7 70 24.1 34.3 2.98 8800 38 58 4 Negative 1 9.1 4 27.2 67 22.4 33.3 3.6 5300 40 55 5 Negative 11 14.8 5.8 42.5 72 25.1 34.8 4.51 17200 75 20 5 Negative

model = AdaBoostClassifier(n_estimators=num_trees, random_state=seed) results = model_selection.cross_val_score(model, X, Y, cv=kfold) model.fit(X, Y) print(learning accuracy) print(results.mean()) predictions = model.predict(A) print(predictions) accuracy1 = accuracy_score(B, predictions) print(Accuracy % is ) print(accuracy1*100)

Its not clear. Perhaps try a suite of algorithms and see what works best on your problem. I recommend this process: http://machinelearningmastery.com/machine-learning-performance-improvement-cheat-sheet/

For me, the VotingClassifier took more time than the others. If there is a metric could you please help identify which is faster and has the least performance implications when working with larger datasets?

2. Since random forest is used to lower the correlation between individual classifiers as we have in bagging approach. So, is this also leads to reduce the overfitting in our model by reducing correlation?

2) I read in your post on stacking that it works better if the predictions of submodels are weakly correlated. Does it mean that it is better to train submodels from different families? (for example: a SVM model, a RF and a neural net) Can I build an Aggregated model using stacking with Xgboost, LigthGBM, GBM?

I have a question with regards to a specific hyperparameter the base_estimator of AdaBoostClassifier. Is there a way for me to ensemble several models (For instance: DecisionTreeClassifier, KNeighborsClassifier, and SVC) into the base_estimator hyperparameter? Cause I have seen most people implementing only one model but the main concept of AdaBoostClassifiers is to train different classifiers into an ensemble giving more weigh to incorrect classifications and correct prediction models through the use of bagging. Basically, I just want to know if this is possible to add several classifiers into the base_estimator hyperparameter. Thanks for the help and nice post!

I am using a simple backpropagation NN with time delays for time series forecasting. My data is heavily skewed with only a few extreme values. When I run e.g. 20 identical models (not ensembles), with random weights for each run, I choose the model with lowest validation error. But the problem then is that the error using the test set for that model may not be the lowest.

So I suppose ensembles might help, but what is the best approach for NN? Perhaps you have already answered this somewhere. I suppose I can e.g. just take the average or median or some other measures for my 20 models, but will this count as ensembles?

Thank you Jason for your helpful tutorial. Ive a question about Voting ensembles, I mean what is the difference between average voting and majrity voting (I know how it works), but I want to know in which situation we apply majority voting and the same thing about average voting. Another question: By applying majority voting, is it obliged to train classifiers on the same training set? Iam working on enhancing predictions accuracy of models by updating the training dataset in each iteration (by selecting relevant feautures).

If we have both a classification and regression problem that rely on the same input data, is it possible to successfully architect a neural network that gives both classification and regression outputs?

Hello, Jason. Thanks you are doing a great work, I am working on my Master research project in which I am using Random Forest with Sklearn but have to cite this paper 1. Breiman, L., Random Forests, Machine Learning. Vol. 45, No. 1, pp. 532, 2001. i.e. base paper of Random Forest and he used Voting method but in sklearn documentation they given In contrast to the original publication [B2001], the scikit-learn implementation combines classifiers by averaging their probabilistic prediction, instead of letting each classifier vote for a single class. and I implemented RandomForestClassifier() in my program and works very well. Now, my question is as I have to write some details of Random Forest in my research paper and will explain about voting method too so, should I use your above Voting Ensemble method or simple sklearn implementaiton is fine.?

Im trying to use the GradientBoostingRegressor function to combine the predictions of two machine learning algorithms ( linear regression and SVR algorithms) to predict the popularity of the image. As I know In case of regression it takes an average of results instead of voting. I wrote the following code :

# coding: utf-8 import numpy import pandas from keras.models import Sequential from keras.layers import Dense from keras.wrappers.scikit_learn import KerasRegressor from sklearn.model_selection import cross_val_score,cross_val_predict from sklearn.model_selection import KFold from sklearn.preprocessing import StandardScaler from sklearn.pipeline import Pipeline from sklearn import model_selection

import numpy as np from sklearn.metrics import mean_squared_error from sklearn.datasets import make_friedman1 from sklearn.ensemble import GradientBoostingRegressor from sklearn.linear_model import LinearRegression from sklearn.svm import SVR from sklearn.model_selection import train_test_split import matplotlib.pyplot as plt

model1 = GradientBoostingRegressor( lr ,n_estimators=100, learning_rate=0.1, max_depth=1, random_state=seed, loss=ls) result1 = model_selection.cross_val_score(model1, X, Y, cv=kfold) print(result1.mean())

model2 = GradientBoostingRegressor( svr_lin ,n_estimators=100, learning_rate=0.1, max_depth=1, random_state=seed, loss=ls) result2 = model_selection.cross_val_score(model2, X, Y, cv=kfold) print(result2.mean())

Is this really necessary for regression estimators, as cross_val_score and cross_val_predict already use KFold by default for regression and other cases. Is there an advantage to your implementation of KFold?

I have the following task and do not know how to accomplish it: 1. I have legacy code which is not well-done looks like this: clf = BaggingRegressor(svm.SVR(C=10.0), n_estimators=64, max_samples=0.9, max_features=0.8)

I have the MLP-models (done in TF). However, I do not know how to compare them because in my TF models I do not use CrossValidation and in order to compare the results, I need to use the same training and validation sets, which from this function before looks like are created randomly. I do not know if you understand better my question now.

sir, instead of directly using extratreeclassifier, i want to call it as user defined bulit in function, but it wont works. can u please suggest me how to write or use extratreeclassfier as user own defined function

First of all thank you for these awesome tutorials. I have used the pima indians diabetes dataset and applied modeling using MLP neural networks, and got an accuracy of around 73%. Now I want to boost my accuracy using ensembles, so shall I discard MLP and depend only on either Trees, Random Forests, etc. ?

May I ask you that after we did the ensembles and got better accuracy, how could we get again this accuracy in the initial models we used before doing ensembles ? (Sorry if my question seems dumb Im still a beginner)

~\Anaconda3\lib\site-packages\imblearn\base.py in fit_resample(self, X, y) 83 self.sampling_strategy, y, self._sampling_type) 84 > 85 output = self._fit_resample(X, y) 86 87 if binarize_y:

~\Anaconda3\lib\site-packages\imblearn\over_sampling\_smote.py in _fit_resample(self, X, y) 794 def _fit_resample(self, X, y): 795 self._validate_estimator() > 796 return self._sample(X, y) 797 798 def _sample(self, X, y):

~\Anaconda3\lib\site-packages\imblearn\over_sampling\_smote.py in _sample(self, X, y) 810 811 self.nn_k_.fit(X_class) > 812 nns = self.nn_k_.kneighbors(X_class, return_distance=False)[:, 1:] 813 X_new, y_new = self._make_samples(X_class, y.dtype, class_sample, 814 X_class, nns, n_samples, 1.0)

~\Anaconda3\lib\site-packages\sklearn\neighbors\base.py in kneighbors(self, X, n_neighbors, return_distance) 414 Expected n_neighbors 416 (train_size, n_neighbors) 417 ) 418 n_samples, _ = X.shape

I came across this article as am trying to implement a voting classifier, I have three questions that I wish you have the time to answer: I am sort of new to this so excuse me if any of my questions sounded silly. _________________________________________________________________ =============================================================== Question#1- I am regarding the ensembler as a new classifier now with a higher score than the others. I tried to implement the voting classifier to have a (yhat) but it failed.

The last step gave the following error: ((NotFittedError: This VotingClassifier instance is not fitted yet. Call fit with appropriate arguments before using this method)) _________________________________________________________________ ============================================================== Question#2- is there any way to find the probabilities using the ensembler(with soft voting=True)?

This also gave me the same (NotFittedError) error as above. _________________________________________________________________ ============================================================== Question#3 is it normal to have a classifier with a higher cross-validation score than the ensembler?

I have a Logistic regression (score 0.8) Naive Bayesian (0.73), and Decision Tree (0.71) while the ensemblers score is (0.74). _________________________________________________________________ ==============================================================

as I said earlier, Please execuse my silly questions, I just solved questions 1 and 2 by fitting the new ensembler again.. My previous understanding is that fitting was already done (with the original classifiers) thus we can not do it again. the missing line was:

I am working on a machine learning project. My data is all about trading(open,high,cos,low) etc. I have constructed some techincal indicators based on those columns. My main goal is to predict the market phase (bullish,bearish,lateral). I have about 15k rows to train the model. I think this problem comes under classification. So I have been using all types of classsification algorithms but they result in 40-50% of accuracy. I want to increase them upto 70%. please let me know about how to increase the accuracy.

Respected Sir, Thank you very much for this tutorial. I use your code for my dataset. It works well and gives 100% accuracy while implementing all classifiers. Is it a over fitting problem? Kindly clarify me. Thank you.

One question: If I have different data with the same length and label and use various classifiers for each data and finally want to fuse their result can I use a similar way? If yes how, do you have a documents for it?

svm algorithm tutorial: steps for building models using python and sklearn

svm algorithm tutorial: steps for building models using python and sklearn

In this support vector machine algorithm tutorial blog, we will discuss the support vector machine algorithm with examples. We will also talk about the advantages and disadvantages of the SVM algorithm. We will build support vector machine models with the help of the support vector classifier function. Also, we will implement Kernel SVM in Python and Sklearn, a trick used to deal with non-linearly separable datasets.

Support Vector Machine or SVM algorithm is a simple yet powerful Supervised Machine Learning algorithm that can be used for building both regression and classification models. SVM algorithm can perform really well with both linearly separable and non-linearly separable datasets. Even with a limited amount of data, the support vector machine algorithm does not fail to show its magic.

Support vector machine or SVM algorithm is based on the concept of decision planes, where hyperplanes are used to classify a set of given objects. Let us start off with a few pictorial examples of support vector machine algorithm. As we can see in Figure 2, we have two sets of data. These datasets can be separated easily with the help of a line, called a decision boundary.

But there can be several decision boundaries that can divide the data points without any errors. For example, in Figure 3, all decision boundaries classify the datasets correctly. But how do we pick the best decision boundary?

The region that the closest points define around the decision boundary is known as the margin. That is why the decision boundary of a support vector machine model is known as the maximum margin classifier or the maximum margin hyperplane.

What does Kernel SVM do? How does it find the classifier? Well, the Kernel SVM projects the non-linearly separable datasets of lower dimensions to linearly separable data of higher dimensions. Kernel SVM performs the same in such a way that datasets belonging to different classes are allocated to different dimensions. Interesting, isnt it? Well, before exploring how to implement SVM in Python programming language, let us take a look at the pros and cons of support vector machine algorithm.

SVM libraries are packed with some popular kernels such as Polynomial, Radial Basis Function or rbf, and Sigmoid. The classification function used in SVM in Machine Learning is SVC. The SVC function looks like this: sklearn.svm.SVC (C=1.0, kernel= rbf, degree=3)

Kernel transforms the input data into any first as per the user requirements. The Kernels used in SVM could be linear, polynomial, radial basis functions(RBFs), and non-linear hyperplanes, created using the polynomial and RBF functions. You can obtain accurate classifiers by separating non-linear classes through an advanced kernel.

The C parameters in Scikit-learn denote the error or penalty representing any miscalculation. You can maintain regularization by understanding the miscalculation and changing the decision boundary through tweaking the C parameters.

Gamma parameters determine their influence over a single training example. There are two types of gamma parameters, low meaning far and high meaning close values. The low or far values define a Gaussian function with a large variance. Whereas, high or close values define it with small variance.

Problem Statement: Use Machine Learning to predict cases of breast cancer using patient treatment history and health data Dataset: Breast Cancer Wisconsin (Diagnostic) Dataset Let us have a quick look at the dataset: Classification Model Building: Support Vector Machine in Python Let us build the classification model with the help of a Support Vector Machine algorithm. Step 1: Load Pandas library and the dataset using Pandas Let us have a look at the shape of the dataset: Step 2: Define the features and the target Have a look at the features:

Have a look at the target: Step 3: Split the dataset into train and test using sklearn before building the SVM algorithm model Step 4: Import the support vector classifier function or SVC function from Sklearn SVM module. Build the Support Vector Machine model with the help of the SVC function Step 5: Predict values using the SVM algorithm model Step 6: Evaluate the Support Vector Machine model

In this SVM tutorial blog, we answered the question, what is SVM? Some other important concepts such as SVM full form, pros and cons of SVM algorithm, and SVM examples, are also highlighted in this blog . We also learned how to build support vector machine models with the help of the support vector classifier function. Additionally, we talked about the implementation of Kernel SVM in Python and Sklearn, which is a very useful method while dealing with non-linearly separable datasets.

naive bayes classifier tutorial in python and scikit-learn

naive bayes classifier tutorial in python and scikit-learn

This is the second article in a series of two about the Naive Bayes Classifier and it will deal with the implementation of the model in Scikit-Learn with Python. For a detailed overview of the math and the principles behind the model, please check the other article: Naive Bayes Classifier Explained.

The purpose of this data is, given 3 facts about a certain moment(the weather, whether it is a weekend or a workday or whether it is morning, lunch or evening), can we predict if there's a traffic jam in the city?

Now let's get to work. We need only one dependency installed for this, and that is the scikit-learn python library. It is one of the most powerful librarie for machine learning and data science and it is free to use. So let's install the library.

The sklearn library contains more than one Naive Bayes classifiers and each is different by means of implementation. Not every classifier implementation is recommended for one type of problem. That's why we have more than one implementation, because some classifiers perform better on some types of data, while others don't. The types of classifiers that the library contains are:

No let's try gathering this data and building our model. But first, we need to do a little bit of preprocessing. Computers are generally bad at understanding text, but they are very good with numbers. So we need to transform our text data into numbers so that our model can better understand it.

This is a encoder provided by scikit that transforms categorical data from text to number. If we have n possible values in our dataset, then LabelEncoder model will transform it into numbers from 0 to n-1 so that each textual value has a number representation. For example, our weather value will be encoded like this.

That's great! For me, it looks that we managed to build a simple classifier using so little data. For building a more realistic model, we would need more features and more entries. But still, for learning purposes, I think we did a really good job.

gaussian processes for classification with python

gaussian processes for classification with python

Gaussian Processes are a generalization of the Gaussian probability distribution and can be used as the basis for sophisticated non-parametric machine learning algorithms for classification and regression.

They are a type of kernel model, like SVMs, and unlike SVMs, they are capable of predicting highly calibrated class membership probabilities, although the choice and configuration of the kernel used at the heart of the method can be challenging.

Gaussian probability distribution functions summarize the distribution of random variables, whereas Gaussian processes summarize the properties of the functions, e.g. the parameters of the functions. As such, you can think of Gaussian processes as one level of abstraction or indirection above Gaussian functions.

A Gaussian process is a generalization of the Gaussian probability distribution. Whereas a probability distribution describes random variables which are scalars or vectors (for multivariate distributions), a stochastic process governs the properties of functions.

Gaussian processes require specifying a kernel that controls how examples relate to each other; specifically, it defines the covariance function of the data. This is called the latent function or the nuisance function.

The latent function f plays the role of a nuisance function: we do not observe values of f itself (we observe only the inputs X and the class labels y) and we are not particularly interested in the values of f

It also requires a link function that interprets the internal representation and predicts the probability of class membership. The logistic function can be used, allowing the modeling of a Binomial probability distribution for binary classification.

For the binary discriminative case one simple idea is to turn the output of a regression model into a class probability using a response function (the inverse of a link function), which squashes its argument, which can lie in the domain (inf, inf), into the range [0, 1], guaranteeing a valid probabilistic interpretation.

This is controlled via setting an optimizer, the number of iterations for the optimizer via the max_iter_predict, and the number of repeats of this optimization process performed in an attempt to overcome local optima n_restarts_optimizer.

We can fit and evaluate a Gaussian Processes Classifier model using repeated stratified k-fold cross-validation via the RepeatedStratifiedKFold class. We will use 10 folds and three repeats in the test harness.

In this case, we can see that the RationalQuadratic kernel achieved a lift in performance with an accuracy of about 91.3 percent as compared to 79.0 percent achieved with the RBF kernel in the previous section.

Here you have shown a classification problem using gaussian process regression module of scikit learn. Could you please elaborate a regression project including code using same module sklearn of python.

python decision tree classification with scikit-learn decisiontreeclassifier - datacamp

python decision tree classification with scikit-learn decisiontreeclassifier - datacamp

As a marketing manager, you want a set of customers who are most likely to purchase your product. This is how you can save your marketing budget by finding your audience. As a loan manager, you need to identify risky loan applications to achieve a lower loan default rate. This process of classifying customers into a group of potential and non-potential customers or safe or risky loan applications is known as a classification problem. Classification is a two-step process, learning step and prediction step. In the learning step, the model is developed based on given training data. In the prediction step, the model is used to predict the response for given data. Decision Tree is one of the easiest and popular classification algorithms to understand and interpret. It can be utilized for both classification and regression kind of problem.

A decision tree is a flowchart-like tree structure where an internal node represents feature(or attribute), the branch represents a decision rule, and each leaf node represents the outcome. The topmost node in a decision tree is known as the root node. It learns to partition on the basis of the attribute value. It partitions the tree in recursively manner call recursive partitioning. This flowchart-like structure helps you in decision making. It's visualization like a flowchart diagram which easily mimics the human level thinking. That is why decision trees are easy to understand and interpret.

Decision Tree is a white box type of ML algorithm. It shares internal decision-making logic, which is not available in the black box type of algorithms such as Neural Network. Its training time is faster compared to the neural network algorithm. The time complexity of decision trees is a function of the number of records and number of attributes in the given data. The decision tree is a distribution-free or non-parametric method, which does not depend upon probability distribution assumptions. Decision trees can handle high dimensional data with good accuracy.

Attribute selection measure is a heuristic for selecting the splitting criterion that partition data into the best possible manner. It is also known as splitting rules because it helps us to determine breakpoints for tuples on a given node. ASM provides a rank to each feature(or attribute) by explaining the given dataset. Best score attribute will be selected as a splitting attribute (Source). In the case of a continuous-valued attribute, split points for branches also need to define. Most popular selection measures are Information Gain, Gain Ratio, and Gini Index.

Shannon invented the concept of entropy, which measures the impurity of the input set. In physics and mathematics, entropy referred as the randomness or the impurity in the system. In information theory, it refers to the impurity in a group of examples. Information gain is the decrease in entropy. Information gain computes the difference between entropy before split and average entropy after split of the dataset based on given attribute values. ID3 (Iterative Dichotomiser) decision tree algorithm uses information gain.

Information gain is biased for the attribute with many outcomes. It means it prefers the attribute with a large number of distinct values. For instance, consider an attribute with a unique identifier such as customer_ID has zero info(D) because of pure partition. This maximizes the information gain and creates useless partitioning.

C4.5, an improvement of ID3, uses an extension to information gain known as the gain ratio. Gain ratio handles the issue of bias by normalizing the information gain using Split Info. Java implementation of the C4.5 algorithm is known as J48, which is available in WEKA data mining tool.

The Gini Index considers a binary split for each attribute. You can compute a weighted sum of the impurity of each partition. If a binary split on attribute A partitions data D into D1 and D2, the Gini index of D is:

In case of a discrete-valued attribute, the subset that gives the minimum gini index for that chosen is selected as a splitting attribute. In the case of continuous-valued attributes, the strategy is to select each pair of adjacent values as a possible split-point and point with smaller gini index chosen as the splitting point.

In the decision tree chart, each internal node has a decision rule that splits the data. Gini referred as Gini ratio, which measures the impurity of the node. You can say a node is pure when all of its records belong to the same class, such nodes known as the leaf node.

criterion : optional (default=gini) or Choose attribute selection measure: This parameter allows us to use the different-different attribute selection measure. Supported criteria are gini for the Gini index and entropy for the information gain.

splitter : string, optional (default=best) or Split Strategy: This parameter allows us to choose the split strategy. Supported strategies are best to choose the best split and random to choose the best random split.

max_depth : int or None, optional (default=None) or Maximum Depth of a Tree: The maximum depth of the tree. If None, then nodes are expanded until all the leaves contain less than min_samples_split samples. The higher value of maximum depth causes overfitting, and a lower value causes underfitting (Source).

In Scikit-learn, optimization of decision tree classifier performed by only pre-pruning. Maximum depth of the tree can be used as a control variable for pre-pruning. In the following the example, you can plot a decision tree on the same data with max_depth=3. Other than pre-pruning parameters, You can also try other attribute selection measure such as entropy.

In this tutorial, you covered a lot of details about Decision Tree; It's working, attribute selection measures such as Information Gain, Gain Ratio, and Gini Index, decision tree model building, visualization and evaluation on diabetes dataset using Python Scikit-learn package. Also, discussed its pros, cons, and optimizing Decision Tree performance using parameter tuning.

radius neighbors classifier algorithm with python

radius neighbors classifier algorithm with python

It is based on the k-nearest neighbors algorithm, or kNN. kNN involves taking the entire training dataset and storing it. Then, at prediction time, the k-closest examples in the training dataset are located for each new example for which we want to predict. The mode (most common value) class label from the k neighbors is then assigned to the new example.

Instead of locating the k-neighbors, the Radius Neighbors Classifier locates all examples in the training dataset that are within a given radius of the new example. The radius neighbors are then used to make a prediction for the new example.

The radius-based approach to locating neighbors is appropriate for those datasets where it is desirable for the contribution of neighbors to be proportional to the density of examples in the feature space.

Given a fixed radius, dense regions of the feature space will contribute more information and sparse regions will contribute less information. It is this latter case that is most desirable and it prevents examples very far in feature space from the new example from contributing to the prediction.

Given that the radius is fixed in all dimensions of the feature space, it will become less effective as the number of input features is increased, which causes examples in the feature space to spread further and further apart. This property is referred to as the curse of dimensionality.

Another important hyperparameter is the weights argument that controls whether neighbors contribute to the prediction in a uniform manner or inverse to the distance (distance) from the example. Uniform weight is used by default.

We can fit and evaluate a Radius Neighbors Classifier model using repeated stratified k-fold cross-validation via the RepeatedStratifiedKFold class. We will use 10 folds and three repeats in the test harness.

Note that we are grid searching the radius hyperparameter of the RadiusNeighborsClassifier within the Pipeline where the model is named model and, therefore, the radius parameter is accessed via model->radius with a double underscore (__) separator, e.g. model__radius.

In this case, we can see that we achieved better results using a radius of 0.8 that gave an accuracy of about 87.2 percent compared to a radius of 1.0 in the previous example that gave an accuracy of about 75.4 percent.

Another key hyperparameter is the manner in which examples in the radius contribute to the prediction via the weights argument. This can be set to uniform (the default), distance for inverse distance, or a custom function.

In this case, we can see an additional lift in mean classification accuracy from about 87.2 percent with uniform weights in the previous example to about 89.3 percent with distance weights in this case.

logistic regression in python - quick guide - tutorialspoint

logistic regression in python - quick guide - tutorialspoint

One such example of machine doing the classification is the email Client on your machine that classifies every incoming mail as spam or not spam and it does it with a fairly large accuracy. The statistical technique of logistic regression has been successfully applied in email client. In this case, we have trained our machine to solve a classification problem.

Logistic Regression is just one part of machine learning used for solving this kind of binary classification problem. There are several other machine learning techniques that are already developed and are in practice for solving other kinds of problems.

If you have noted, in all the above examples, the outcome of the predication has only two values - Yes or No. We call these as classes - so as to say we say that our classifier classifies the objects in two classes. In technical terms, we can say that the outcome or target variable is dichotomous in nature.

There are other classification problems in which the output may be classified into more than two classes. For example, given a basket full of fruits, you are asked to separate fruits of different kinds. Now, the basket may contain Oranges, Apples, Mangoes, and so on. So when you separate out the fruits, you separate them out in more than two classes. This is a multivariate classification problem.

Consider that a bank approaches you to develop a machine learning application that will help them in identifying the potential clients who would open a Term Deposit (also called Fixed Deposit by some banks) with them. The bank regularly conducts a survey by means of telephonic calls or web forms to collect information about the potential clients. The survey is general in nature and is conducted over a very large audience out of which many may not be interested in dealing with this bank itself. Out of the rest, only a few may be interested in opening a Term Deposit. Others may be interested in other facilities offered by the bank. So the survey is not necessarily conducted for identifying the customers opening TDs. Your task is to identify all those customers with high probability of opening TD from the humongous survey data that the bank is going to share with you.

Fortunately, one such kind of data is publicly available for those aspiring to develop machine learning models. This data was prepared by some students at UC Irvine with external funding. The database is available as a part of UCI Machine Learning Repository and is widely used by students, educators, and researchers all over the world. The data can be downloaded from here.

We will be using Jupyter - one of the most widely used platforms for machine learning. If you do not have Jupyter installed on your machine, download it from here. For installation, you can follow the instructions on their site to install the platform. As the site suggests, you may prefer to use Anaconda Distribution which comes along with Python and many commonly used Python packages for scientific computing and data science. This will alleviate the need for installing these packages individually.

We will use the bank.csv file for our model development. The bank-names.txt file contains the description of the database that you are going to need later. The bank-full.csv contains a much larger dataset that you may use for more advanced developments.

Here we have included the bank.csv file in the downloadable source zip. This file contains the comma-delimited fields. We have also made a few modifications in the file. It is recommended that you use the file included in the project source zip for your learning.

Fortunately, the bank.csv does not contain any rows with NaN, so this step is not truly required in our case. However, in general it is difficult to discover such rows in a huge database. So it is always safer to run the above statement to clean the data.

Whenever any organization conducts a survey, they try to collect as much information as possible from the customer, with the idea that this information would be useful to the organization one way or the other, at a later point of time. To solve the current problem, we have to pick up the information that is directly relevant to our problem.

The output shows the names of all the columns in the database. The last column y is a Boolean value indicating whether this customer has a term deposit with the bank. The values of this field are either y or n. You can read the description and purpose of each column in the banks-name.txt file that was downloaded as part of the data.

Examining the column names, you will know that some of the fields have no significance to the problem at hand. For example, fields such as month, day_of_week, campaign, etc. are of no use to us. We will eliminate these fields from our database. To drop a column, we use the drop command as shown below

Now, we have only the fields which we feel are important for our data analysis and prediction. The importance of Data Scientist comes into picture at this step. The data scientist has to select the appropriate columns for model building.

For example, the type of job though at the first glance may not convince everybody for inclusion in the database, it will be a very useful field. Not all types of customers will open the TD. The lower income people may not open the TDs, while the higher income people will usually park their excess money in TDs. So the type of job becomes significantly relevant in this scenario. Likewise, carefully select the columns which you feel will be relevant for your analysis.

As the comment says, the above statement will create the one hot encoding of the data. Let us see what has it created? Examine the created data called data by printing the head records in the database.

Now, we will explain how the one hot encoding is done by the get_dummies command. The first column in the newly generated database is y field which indicates whether this client has subscribed to a TD or not. Now, let us look at the columns which are encoded. The first encoded column is job. In the database, you will find that the job column has many possible values such as admin, blue-collar, entrepreneur, and so on. For each possible value, we have a new column created in the database, with the column name appended as a prefix.

Thus, we have columns called job_admin, job_blue-collar, and so on. For each encoded field in our original database, you will find a list of columns added in the created database with all possible values that the column takes in the original database. Carefully examine the list of columns to understand how the data is mapped to a new database.

It says that this customer has not subscribed to TD as indicated by the value in the y field. It also indicates that this customer is a blue-collar customer. Scrolling down horizontally, it will tell you that he has a housing and has taken no loan.

If we examine the columns in the mapped database, you will find the presence of few columns ending with unknown. For example, examine the column at index 12 with the following command shown in the screenshot

This indicates the job for the specified customer is unknown. Obviously, there is no point in including such columns in our analysis and model building. Thus, all columns with the unknown value should be dropped. This is done with the following command

We have about forty-one thousand and odd records. If we use the entire data for model building, we will not be left with any data for testing. So generally, we split the entire data set into two parts, say 70/30 percentage. We use 70% of the data for model building and the rest for testing the accuracy in prediction of our created model. You may use a different splitting ratio as per your requirement.

Before we split the data, we separate out the data into two arrays X and Y. The X array contains all the features (data columns) that we want to analyze and Y array is a single dimensional array of boolean values that is the output of the prediction. To understand this, let us run some code.

This will create the four arrays called X_train, Y_train, X_test, and Y_test. As before, you may examine the contents of these arrays by using the head command. We will use X_train and Y_train arrays for training our model and X_test and Y_test arrays for testing and validating.

It is not required that you have to build the classifier from scratch. Building classifiers is complex and requires knowledge of several areas such as Statistics, probability theories, optimization techniques, and so on. There are several pre-built libraries available in the market which have a fully-tested and very efficient implementation of these classifiers. We will use one such pre-built model from the sklearn.

Once the classifier is created, you will feed your training data into the classifier so that it can tune its internal parameters and be ready for the predictions on your future data. To tune the classifier, we run the following statement

We need to test the above created classifier before we put it into production use. If the testing reveals that the model does not meet the desired accuracy, we will have to go back in the above process, select another set of features (data fields), build the model again, and test it. This will be an iterative step until the classifier meets your requirement of desired accuracy. So let us test our classifier.

The output indicates that the first and last three customers are not the potential candidates for the Term Deposit. You can examine the entire array to sort out the potential customers. To do so, use the following Python code snippet

The output shows the indexes of all rows who are probable candidates for subscribing to TD. You can now give this output to the banks marketing team who would pick up the contact details for each customer in the selected row and proceed with their job.

It shows that the accuracy of our model is 90% which is considered very good in most of the applications. Thus, no further tuning is required. Now, our customer is ready to run the next campaign, get the list of potential customers and chase them for opening the TD with a probable high rate of success.

As you have seen from the above example, applying logistic regression for machine learning is not a difficult task. However, it comes with its own limitations. The logistic regression will not be able to handle a large number of categorical features. In the example we have discussed so far, we reduced the number of features to a very large extent.

However, if these features were important in our prediction, we would have been forced to include them, but then the logistic regression would fail to give us a good accuracy. Logistic regression is also vulnerable to overfitting. It cannot be applied to a non-linear problem. It will perform poorly with independent variables which are not correlated to the target and are correlated to each other. Thus, you will have to carefully evaluate the suitability of logistic regression to the problem that you are trying to solve.

There are many areas of machine learning where other techniques are specified devised. To name a few, we have algorithms such as k-nearest neighbours (kNN), Linear Regression, Support Vector Machines (SVM), Decision Trees, Naive Bayes, and so on. Before finalizing on a particular model, you will have to evaluate the applicability of these various techniques to the problem that we are trying to solve.

Logistic Regression is a statistical technique of binary classification. In this tutorial, you learned how to train the machine to use logistic regression. Creating machine learning models, the most important requirement is the availability of the data. Without adequate and relevant data, you cannot simply make the machine to learn.

Once you have data, your next major task is cleansing the data, eliminating the unwanted rows, fields, and select the appropriate fields for your model development. After this is done, you need to map the data into a format required by the classifier for its training. Thus, the data preparation is a major task in any machine learning application. Once you are ready with the data, you can select a particular type of classifier.

In this tutorial, you learned how to use a logistic regression classifier provided in the sklearn library. To train the classifier, we use about 70% of the data for training the model. We use the rest of the data for testing. We test the accuracy of the model. If this is not within acceptable limits, we go back to selecting the new set of features.

Once again, follow the entire process of preparing data, train the model, and test it, until you are satisfied with its accuracy. Before taking up any machine learning project, you must learn and have exposure to a wide variety of techniques which have been developed so far and which have been applied successfully in the industry.

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