dummy classifier - rubix ml

dummy classifier - rubix ml

A classifier that makes predictions using a user-defined guessing strategy that disregards the information contained in the features of each sample. Dummy Classifier is useful to provide a sanity check and to compare performance with an actual classifier.

dummy classifiers using sklearn in python - codespeedy

dummy classifiers using sklearn in python - codespeedy

Fellow coders, in this tutorial we will learn about the dummy classifiers using the scikit-learn library in Python. Scikit-learn is a library in Python that provides a range of supervised and unsupervised learning algorithms and also supports Pythons numerical and scientific libraries like NumPy and SciPy. The scikit-learn librarys functionality includes regression, classification, clustering, model section and preprocessing.What are dummy classifiers in sklearn:A DummyClassifier is a classifier in the sklearn library that makes predictions using simple rules and does not generate any valuable insights about the data. As the name suggests, dummy classifiers are used as a baseline and can be compared to real classifiers and thus we must not use it for actual problems. All the other (real) classifiers are expected to perform better on any dataset when compared to the dummy classifier. The classifier does not take into account the training data and instead uses one of the strategies to predict the class label. Stratified, most frequent, constant, and uniform are a few of the strategies used by dummy classifiers. We will implement all these strategies in our code below and check out the results.Working with the code:Let us implement dummy classifiers using the sklearn library:Create a new Python file and import all the required libraries:from sklearn.dummy import DummyClassifier import numpy as npNow, lets start writing our code for implementing dummy classifiers:a = np.array([-1, 1, 1, 1]) b = np.array([0, 1, 1, 1]) strat = ["most_frequent", "stratified", "constant", "uniform"] for s in strat: if s == "constant": dummy_clf = DummyClassifier(strategy=s,random_state=None,constant=1) else: dummy_clf = DummyClassifier(strategy=s,random_state=None) dummy_clf.fit(a,b) print(s) dummy_clf.predict(a) dummy_clf.score(a,b) print("----------------------xxxxxxx----------------------") After running the code, here is the output:DummyClassifier(constant=None, random_state=None, strategy='most_frequent') most_frequent array([1, 1, 1, 1]) 0.75 --------------------------------xxxxxxx-------------------------------- DummyClassifier(constant=None, random_state=None, strategy='stratified') stratified array([1, 1, 0, 1]) 0.25 --------------------------------xxxxxxx-------------------------------- DummyClassifier(constant=1, random_state=None, strategy='constant') constant array([1, 1, 1, 1]) 0.75 --------------------------------xxxxxxx-------------------------------- DummyClassifier(constant=None, random_state=None, strategy='uniform') uniform array([0, 0, 1, 0]) 1.0 --------------------------------xxxxxxx------------------------------- Also learn:Sequential forward selection with Python and Scikit learn

Fellow coders, in this tutorial we will learn about the dummy classifiers using the scikit-learn library in Python. Scikit-learn is a library in Python that provides a range of supervised and unsupervised learning algorithms and also supports Pythons numerical and scientific libraries like NumPy and SciPy. The scikit-learn librarys functionality includes regression, classification, clustering, model section and preprocessing.

A DummyClassifier is a classifier in the sklearn library that makes predictions using simple rules and does not generate any valuable insights about the data. As the name suggests, dummy classifiers are used as a baseline and can be compared to real classifiers and thus we must not use it for actual problems. All the other (real) classifiers are expected to perform better on any dataset when compared to the dummy classifier. The classifier does not take into account the training data and instead uses one of the strategies to predict the class label. Stratified, most frequent, constant, and uniform are a few of the strategies used by dummy classifiers. We will implement all these strategies in our code below and check out the results.

python - dummy classifier - no skill line on a binary classification precision recall curve using a stratified approach - how calculated? - cross validated

python - dummy classifier - no skill line on a binary classification precision recall curve using a stratified approach - how calculated? - cross validated

Stack Exchange network consists of 177 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers.

I understand that because of the imbalanced data set that it is best to use a 'stratified' strategy in the classifier which makes random guesses in proportion to the 1s and zeros. So given my set, the stratified strategy should make 16 positive guesses out of every 100.

My question pertains to the "No Skill" line that appears on the precision recall figure when using the dummy classifer with a stratified strategy. It is a horizonal line at 1.6% precision as seen in the chart below.

Can someone explain why this is and perhaps some of the underlying theory for using a stratified approach? Why wouldn't I use a uniform distribution to generate guesses which, when reviewing my confusion matrix seems to do better.

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python examples of

python examples of

The following are 30 code examples for showing how to use sklearn.dummy.dummyclassifier(). These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example.You may check out the related API usage on the sidebar.You may also want to check out all available functions/classes of the module sklearn.dummy , or try the search function .

machine learning classifiers - the algorithms & how they work

machine learning classifiers - the algorithms & how they work

A classifier in machine learning is an algorithm that automatically orders or categorizes data into one or more of a set of classes. One of the most common examples is an email classifier that scans emails to filter them by class label: Spam or Not Spam.

A classifier is the algorithm itself the rules used by machines to classify data. A classification model, on the other hand, is the end result of your classifiers machine learning. The model is trained using the classifier, so that the model, ultimately, classifies your data.

There are both supervised and unsupervised classifiers. Unsupervised machine learning classifiers are fed only unlabeled datasets, which they classify according to pattern recognition or structures and anomalies in the data. Supervised and semi-supervised classifiers are fed training datasets, from which they learn to classify data according to predetermined categories.

Sentiment analysis is an example of supervised machine learning where classifiers are trained to analyze text for opinion polarity and output the text into the class: Positive, Neutral, or Negative. Try out this pre-trained sentiment analysis model to see how it works.

Machine learning classifiers are used to automatically analyze customer comments (like the above) from social media, emails, online reviews, etc., to find out what customers are saying about your brand.

Other text analysis techniques, like topic classification, can automatically sort through customer service tickets or NPS surveys, categorize them by topic (Pricing, Features, Support, etc.), and route them to the correct department or employee.

SaaS text analysis platforms, like MonkeyLearn, give easy access to powerful classification algorithms, allowing you to custom-build classification models to your needs and criteria, usually in just a few steps.

Machine learning classifiers go beyond simple data mapping, allowing users to constantly update models with new learning data and tailor them to changing needs. Self-driving cars, for example, use classification algorithms to input image data to a category; whether its a stop sign, a pedestrian, or another car, constantly learning and improving over time.

A decision tree is a supervised machine learning classification algorithm used to build models like the structure of a tree. It classifies data into finer and finer categories: from tree trunk, to branches, to leaves. It uses the if-then rule of mathematics to create sub-categories that fit into broader categories and allows for precise, organic categorization.

Naive Bayes is a family of probabilistic algorithms that calculate the possibility that any given data point may fall into one or more of a group of categories (or not). In text analysis, Naive Bayes is used to categorize customer comments, news articles, emails, etc., into subjects, topics, or tags to organize them according to predetermined criteria, like this:

K-nearest neighbors (k-NN) is a pattern recognition algorithm that stores and learns from training data points by calculating how they correspond to other data in n-dimensional space. K-NN aims to find the k closest related data points in future, unseen data.

In text analysis, k-NN would place a given word or phrase within a predetermined category by calculating its nearest neighbor: k is decided by a plurality vote of its neighbors. If k = 1, it would be tagged into the class nearest 1.

Take a look at this visual representation to understand how SVM algorithms work. We have two tags: red and blue, with two data features: X and Y, and we train our classifier to output an X/Y coordinate as either red or blue.

The SVM assigns a hyperplane that best separates (distinguishes between) the tags. In two dimensions this is simply a straight line. Blue tags fall on one side of the hyperplane and red on the other. In sentiment analysis these tags would be Positive and Negative.

SVM algorithms make excellent classifiers because, the more complex the data, the more accurate the prediction will be. Imagine the above as a 3-dimensional output, with a Z-axis added, so it becomes a circle.

Artificial neural networks are designed to work much like the human brain does. They connect problem-solving processes in a chain of events, so that once one algorithm or process has solved a problem, the next algorithm (or link in the chain) is activated.

Artificial neural networks or deep learning models require vast amounts of training data because their processes are highly advanced, but once they have been properly trained, they can perform beyond other, individual, algorithms.

There are a variety of artificial neural networks, including convolutional, recurrent, feed-forward, etc., and the machine learning architecture best suited to your needs depends on the problem youre aiming to solve.

Classification algorithms enable the automation of machine learning tasks that were unthinkable just a few years ago. And, better yet, they allow you to train AI models to the needs, language, and criteria of your business, performing much faster and with a greater level of accuracy than humans ever could.

MonkeyLearn is a machine learning text analysis platform that harnesses the power of machine learning classifiers with an exceedingly user-friendly interface, so you can streamline processes and get the most out of your text data for valuable insights.

python - random classifier using sklearn dummy classifier - stack overflow

python - random classifier using sklearn dummy classifier - stack overflow

What is the meaning of: "generates predictions uniformly"? is there a difference than predicting the classes using a totally random way, ex. using numpy. random method to generate a list of predictions?

The uniform option will tend to predict the same number of cases in each class. It is different from the stratified option, for instance, which takes into account the classes populations in the training sample.

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how to automatically create base line estimators using scikit learn. - data science central

how to automatically create base line estimators using scikit learn. - data science central

For any machine learning problem, say a classifier in this case, its always handy to create quickly a base line classifier against which we can compare our new models. You dont want to spend a lot of time creating these base line classifiers; you would rather spend that time in building and validating new features for your final model. In this post we will see how we can rapidly create base line classifier using scikit learn package for any dataset.

Scikit provides the classDummyClassifierto help us create our base line model rapidly. Modulesklearn.dummyhas theDummyClassifierclass. Its api interfaces are very similar to any other model in scikit learn, use thefitfunction to build the model andpredictfunction to perform classification.

Let us look at the parameters while initializingDummyClassifier. The first parameterstrategyis used to define the modus operandi of our Dummy Classifier. In the example above we have selectedstratifiedas the strategy. According to this strategy, the classifier looks at the class distribution in our target variable to make its predictions.

Oh la.. our predictions are ready. Ouroutputvariable is a matrix of size (150,3), one dimension for each class. In the next line we use the argmax function to get the index which is set to 1. This is our predicted class. Now that we know what is happening under the hood, let us call thepredictfunction and print some accuracy metrics for our dummy classifier.

Let us look a the models generated when our dataset is imbalanced. The most_frequent strategy we discussed will return a biased classifier, as they will tend to pick up the majority class. The accuracy score in this case will be proportional to the majority class ratio. Let us simulate an imbalanced dataset and create our dummy classifiers with different strategies.

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Free Book and Resources for DSC Members New Perspectives on Statistical Distributions and Deep Learning Time series, Growth Modeling and Data Science Wizardy Statistical Concepts Explained in Simple English Machine Learning Concepts Explained in One Picture Comprehensive Repository of Data Science and ML Resources Advanced Machine Learning with Basic Excel Difference between ML, Data Science, AI, Deep Learning, and Statistics Selected Business Analytics, Data Science and ML articles How to Automatically Determine the Number of Clusters in your Data Fascinating New Results in the Theory of Randomness Hire a Data Scientist | Search DSC | Find a Job Post a Blog | Forum Questions

zeror: the simplest possible classifier or: why high accuracy can be misleading | r-bloggers

zeror: the simplest possible classifier or: why high accuracy can be misleading | r-bloggers

[This article was first published on R-Bloggers Learning Machines, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

In one of my most popular posts So, what is AI really? I showed that Artificial Intelligence (AI) basically boils down to autonomously learned rules, i.e. conditional statements or simply, conditionals.

In this post, I create the simplest possible classifier, called ZeroR, to show that even this classifier can achieve surprisingly high values for accuracy (i.e. the ratio of correctly predicted instances) and why this is not necessarily a good thing, so read on!

In the above-mentioned post, I gave an example of a classifier that was able to give you some guidance on whether a certain mushroom is edible or not. The basis for this was rules, which separated the examples based on the given attributes:

Obviously, the more rules the more complex a classifier is. In the example above we used the so-called OneR classifier which bases its decision on one attribute alone. Here, I will give you an even simpler classifier! The ZeroR classifier bases its decision on no attribute whatsoever zero, zilch, zip, nada! How can this be? Easy: it just takes the majority class of the target attribute! I will give you an example.

We see that 700 customers have a good credit risk while 300 have a bad one. The ZeroR classifier now takes the majority class (good credit risk) and uses it as the prediction every time! You have read correctly, it just predicts that every customer is a good credit risk!

Seems a little crazy, right? Well, it illustrates an important point: many of my students, as well as some of my consulting clients, often ask me what a good classifier is and how long it does take to build one. Many people in the area of data science (even some experts) will give you something like the following answer (source: A. Burkov):

Well, to be honest with you: this is not a very good answer. Why? Because it very much depends on the share of the majority class! To understand that, let us have a look at how the ZeroR classifier performs on our dataset:

So, because 70% of the customers are good risks we get an accuracy of about 70%! You can take this example to extremes: for example, if you have a dataset with credit card transactions where 0.1% of the transactions are fraudulent (which is about the actual number) you will get an accuracy of 99.9% just by using the ZeroR classifier! Concretely, just by saying that no fraud exists (!) you get an accuracy even beyond the one year (or never) bracket (according to the above scheme)!

Another example even concerns life and death: the probability of dying within one year lies at about 0.8% (averaged over all the people worldwide, according to The World Factbook by the CIA). So by declaring that we are all immortal, we are in more than 99% of all cases right! Many medical studies have a much higher error rate

Here, we see that we get an out-of-sample accuracy of 75%, which is more than 7 percentage points better than what we got with the ZeroR classifier, here called base rate. Yet, this is not statistically significant (for an introduction to statistical significance see From Coin Tosses to p-Hacking: Make Statistics Significant Again!).

Because the concept of error rate reduction compared to ZeroR (= base rate) and its statistical significance is so relevant it is displayed by default in the eval_model() function of the OneR package.

To end this post, we build a random forest model with the randomForest package (on CRAN) on the dataset (for some more information on random forests see Learning Data Science: Predicting Income Brackets):

The out-of-sample accuracy is over 80% here and the error rate reduction (compared to ZeroR) of about one third is statistically significant. Yet 80% is still not that impressive when you keep in mind that 70% is the base rate!

You should now be able to spot why this is one of the worst scientific papers I have ever seen: Applications of rule based Classification Techniques for Thoracic Surgery (2015). This also shows one of the more general problems: although this is a medical topic not many medical professionals would be able to spot the elephant in the room here this will be true for most other areas too, where machine learning will be used ever more frequently. (Just as an aside: this type of blunder wouldnt have happened had the authors used the OneR package: One Rule (OneR) Machine Learning Classification in under One Minute.)

As you can imagine, there are many strategies to deal with the above challenges of imbalanced/unbalanced data, e.g. other model metrics (like recall or precision) and other sampling strategies (like undersampling the majority class or oversampling the minority class) but that are topics for another post, so stay tuned!

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build your first random forest classifier and predict heart disease across patients

build your first random forest classifier and predict heart disease across patients

We will start by downloading data set from Kaggle, after that, we will do some basic data cleaning, and finally, we will fit the model and evaluate it. On the way, we will also create a baseline model that will be used for evaluation.

We can see here that columns 10, 11, and 12 have a lot of nulls. 'Ca' and 'thal' are actually almost empty and 'slope' has only 104 entries. This is too many missing values to fill in so let's drop them.

There is one more thing we need to do before we can proceed. I have noticed that the last column 'num' has some trailing spaces in its name (you cannot see this with a bare eye) so let's have a look at the list of column names.

What we can notice straight away is the fact that some variables are not continuous. Actually, only five features are continuous:''age', 'chol', 'oldpeak', 'thalach', 'trestbps' whereas the other are categorical variables.

* Note that we had to make the 'num' variable a string in order to use it as a hue parameter. We did it by mapping 0s to 'no' meaning healthy patients, and 1s to 'yes' meaning patients with heart disease.

The last two are ordered categorical variables as encoded by the data set authors. I am not sure if we should treat them like that or change them to dummy encodings. This would need further investigation and we could change the approach in the future. For now, we will leave them ordered.

Actually we are not doing bad at all. We only have five False Positives, and also eight False Negatives. Additionally, we have predicted heart disease for eighteen people out of twenty-six people that had heart problems.

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