Administration (LN, MC, MU, NC, PS, RP, YN) Aviation (ABE, ABF, ABH, AC, AD, AE, AG, AM, AME, AO, AS, AT, AW, AZ, PR) Cryptology (CTI, CTM, CTN, CTR, CTT, IT) Intelligence Master at Arms Medical Nuclear (EM, ET, MM)
Construction (BU, CE, CM, EA, EO, SW, UT) Special Operations (EOD, ND, SB, SO) Security Submarine Supply (CS, LS, PC, RS, SH, SK) Surface Operations (BM, ET, FC, GM, MN, OS, QM, STG) Surface Engineering (DC, EM, EN, GSE, GSM, HT, IC, MM, and MR)
Special Operations (EOD, ND, SB, SO) Security Submarine Supply (CS, LS, PC, RS, SH, SK) Surface Operations (BM, ET, FC, GM, MN, OS, QM, STG) Surface Engineering (DC, EM, EN, GSE, GSM, HT, IC, MM, and MR)
It's an unfortunate fact of Navy life that no matter how hard you work or how many hours you put in, all your efforts will have been wasted if they aren't documented in your annual evaluation. That annual eval is the single biggest factor in whether you get promoted or not and affects your chances for promotion, your assignment options, training opportunities, and your entire future in the military. It's important that you take the time to find out what you need to do in order to meet and exceed standards and then make sure those achievements get recorded in your Eval. To help make your Eval as effective as possible, below are instructions, phrases, examples, and other guidance.
The effectiveness and quality of your annual evaluation depends on much more than the Comments on Performance block. Your actions and preparation over the previous year are the most critical component because without them, there would be nothing to write about! But this short tutorial is only focused on filling in the Comments on Performance block. Squeezing an entire year of performance into a mere 16 lines is a challenge but if you break down the task into its basic parts, it makes the job logical, less subjective, and easier. The Comments on Performance block has three main areas: the Introduction, the Accomplishments section, and the Summary. Each of these areas has its own format and purpose. Note that these "sections" within the Comments on Performance block are not labeled as such. The requirement to follow this format and address the topics required by each section derives from evolving Navy tradition and methodology rather than published guidance.
The purpose and goal of the Navy Eval is to accurately and fully describe performance. This is often accomplished by listing the notable achievements of the person being rated. While listing significant achievements does provide some insight into a person's capabilities, it doesn't provide the whole picture. A list of achievements doesn't give any indication as to the character or personality of the ratee. A Seaman might have a long list of impressive accomplishments but be an unruly Sailor with a bad attitude. He or she might be an expert in their field but the absolute worst at sharing experience or getting along with others. And those social qualities are very important, fully as important as a person's technical skills! So, in addition to describing a person's professional and technical skills, we need a way to convey to the EVAL's reader his social skills, his integrity, his character, his loyalty and other qualities which are not apparent when merely reading a list of accomplishments. The introduction is where this is done. Incidentally, these are the only lines where descriptive, subjective terms and adjectives are tolerated and even encouraged. More...
As stated above, the goal of the Eval is to accurately and fully describe performance. While the Introduction provides information on the subject's personality and character, most of the Comments on Performance block is reserved for recording the subjects accomplishments during the reporting period. A list of accomplishments provide a factual window onto a person's abilities, skills, and productivity. The ratio of the number of lines devoted to accomplishments to the number of lines set aside for the introduction give you an idea of the importance of achievements to the Navy. Technical and professional qualities are critically important to achieving the Navy's mission and for advancement, fully as important as a person's social and leadership skills. The quickest and most effective way to come up with material for your Eval is to brainstorm first. List all the ratee's accomplishments on a separate sheet of paper. Write down everything that might qualify as a bullet. Nothing is too small or off-limits. More...
Just as the introduction was a masterpiece of concise expression, the summary must sum up the impression the writer is trying to convey about the subject of the Eval. Normally, one to three lines are used at the bottom of the Comments on Performance block to summarize the tone of the report and provide a promotion statement. More...
The Navy Enlisted Classification (NEC) system supplements the enlisted rating structure in identifying personnel on active or inactive duty and billets in manpower authorizations. NEC codes identify a non-rating wide skill, knowledge, aptitude, or qualification that must be documented to identify both people and billets for management purposes.
For example, if a Navy police officer (MA Master at Arms) receives specialized training as a K-9 Dog Handler, he/she would be awarded NEC MA-2005. From that point on, the sailor could be assigned to Navy law enforcement duties involving military working dogs.
The Aviation Structural Mechanic - Hydraulics (AM), maintains all aircraft main and auxiliary hydraulic power systems, actuating subsystems and landing gear. Responsible for maintenance on the aircraft fuselage (mainframe) wings airfoils, and associated fixed and moveable surfaces and flight controls. These technicians may also volunteer to fly as Naval aircrew. Aircrew performs numerous in-flight duties and operates aircraft systems in turbojet, helicopter, or propeller aircraft. The duties performed by AMs include:
Aviation structural mechanics may be assigned to sea or shore duty any place in the world, so their working environment varies considerably. They may work in hangars or hangar decks, or outside on flight decks or flight lines at air stations. A high noise level is a normal part of their work environment. AMs work closely with others, do mostly physical work and require little supervision. AMs may also serve as flight engineersaboard certain aircraft.
Note: Advancement (promotion) opportunity and career progression are directly linked to a rating's manning level (i.e., personnel in undermanned ratings have greater promotion opportunity than those in overmanned ratings).
Suppose you are a product manager, you want to classify customer reviews in positive and negative classes. Or As a loan manager, you want to identify which loan applicants are safe or risky? As a healthcare analyst, you want to predict which patients can suffer from diabetes disease. All the examples have the same kind of problem to classify reviews, loan applicants, and patients.
Naive Bayes is the most straightforward and fast classification algorithm, which is suitable for a large chunk of data. Naive Bayes classifier is successfully used in various applications such as spam filtering, text classification, sentiment analysis, and recommender systems. It uses Bayes theorem of probability for prediction of unknown class.
Whenever you perform classification, the first step is to understand the problem and identify potential features and label. Features are those characteristics or attributes which affect the results of the label. For example, in the case of a loan distribution, bank manager's identify customers occupation, income, age, location, previous loan history, transaction history, and credit score. These characteristics are known as features which help the model classify customers.
The classification has two phases, a learning phase, and the evaluation phase. In the learning phase, classifier trains its model on a given dataset and in the evaluation phase, it tests the classifier performance. Performance is evaluated on the basis of various parameters such as accuracy, error, precision, and recall.
Naive Bayes is a statistical classification technique based on Bayes Theorem. It is one of the simplest supervised learning algorithms. Naive Bayes classifier is the fast, accurate and reliable algorithm. Naive Bayes classifiers have high accuracy and speed on large datasets.
Naive Bayes classifier assumes that the effect of a particular feature in a class is independent of other features. For example, a loan applicant is desirable or not depending on his/her income, previous loan and transaction history, age, and location. Even if these features are interdependent, these features are still considered independently. This assumption simplifies computation, and that's why it is considered as naive. This assumption is called class conditional independence.
Lets understand the working of Naive Bayes through an example. Given an example of weather conditions and playing sports. You need to calculate the probability of playing sports. Now, you need to classify whether players will play or not, based on the weather condition.
For simplifying prior and posterior probability calculation you can use the two tables frequency and likelihood tables. Both of these tables will help you to calculate the prior and posterior probability. The Frequency table contains the occurrence of labels for all features. There are two likelihood tables. Likelihood Table 1 is showing prior probabilities of labels and Likelihood Table 2 is showing the posterior probability.
First, you need to convert these string labels into numbers. for example: 'Overcast', 'Rainy', 'Sunny' as 0, 1, 2. This is known as label encoding. Scikit-learn provides LabelEncoder library for encoding labels with a value between 0 and one less than the number of discrete classes.
Till now you have learned Naive Bayes classification with binary labels. Now you will learn about multiple class classification in Naive Bayes. Which is known as multinomial Naive Bayes classification. For example, if you want to classify a news article about technology, entertainment, politics, or sports.
In model building part, you can use wine dataset which is a very famous multi-class classification problem. "This dataset is the result of a chemical analysis of wines grown in the same region in Italy but derived from three different cultivars." (UC Irvine)
Dataset comprises of 13 features (alcohol, malic_acid, ash, alcalinity_of_ash, magnesium, total_phenols, flavanoids, nonflavanoid_phenols, proanthocyanins, color_intensity, hue, od280/od315_of_diluted_wines, proline) and type of wine cultivar. This data has three type of wine Class_0, Class_1, and Class_3. Here you can build a model to classify the type of wine.
It's a good idea to always explore your data a bit, so you know what you're working with. Here, you can see the first five rows of the dataset are printed, as well as the target variable for the whole dataset.
Suppose there is no tuple for a risky loan in the dataset, in this scenario, the posterior probability will be zero, and the model is unable to make a prediction. This problem is known as Zero Probability because the occurrence of the particular class is zero.
The solution for such an issue is the Laplacian correction or Laplace Transformation. Laplacian correction is one of the smoothing techniques. Here, you can assume that the dataset is large enough that adding one row of each class will not make a difference in the estimated probability. This will overcome the issue of probability values to zero.
For Example: Suppose that for the class loan risky, there are 1000 training tuples in the database. In this database, income column has 0 tuples for low income, 990 tuples for medium income, and 10 tuples for high income. The probabilities of these events, without the Laplacian correction, are 0, 0.990 (from 990/1000), and 0.010 (from 10/1000)
In this tutorial, you learned about Nave Bayes algorithm, it's working, Naive Bayes assumption, issues, implementation, advantages, and disadvantages. Along the road, you have also learned model building and evaluation in scikit-learn for binary and multinomial classes.
Naive Bayes is the most straightforward and most potent algorithm. In spite of the significant advances of Machine Learning in the last couple of years, it has proved its worth. It has been successfully deployed in many applications from text analytics to recommendation engines.
2. Rate 6. UIC Type of Report 17. Regular 22. Reporting Senior Last FI MI Promotion / 12. Frocking RCS BUPERS 1610-1 3. Desig 7. Ship/Station 4. SSN 8. Promotion Status 13. Special Clear Form Period of Report 14. From 20. Physical Readiness 9. Date Reported 15. To 21. Billet Subcategory if any 18. Concurrent 23. Grade 24. Desig 25. Title 27. SSN 28. Command employment and command achievements 29. Primary/Collateral/Watchstanding duties. Enter Primary duty abbreviation in box. For Mid-term...
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