# Learning Naive Bayes

2017-09-05

### What are the Pros and Cons of Naive Bayes?

Pros:

• It is easy and fast to predict class of test data set. It also perform well in multi class prediction
• When assumption of independence holds, a Naive Bayes classifier performs better compare to other models like logistic regression and you need less training data.
• It perform well in case of categorical input variables compared to numerical variable(s). For numerical variable, normal distribution is assumed (bell curve, which is a strong assumption).

Cons:

• If categorical variable has a category (in test data set), which was not observed in training data set, then model will assign a 0 (zero) probability and will be unable to make a prediction. This is often known as “Zero Frequency”. To solve this, we can use the smoothing technique. One of the simplest smoothing techniques is called Laplace estimation.

• On the other side naive Bayes is also known as a bad estimator, so the probability outputs from predict_proba are not to be taken too seriously.

• Another limitation of Naive Bayes is the assumption of independent predictors. In real life, it is almost impossible that we get a set of predictors which are completely independent.

Read the whole thing.  Naive Bayes is such an easy algorithm, yet it works remarkably well for categorization problems.  It’s typically not the best solution, but it’s a great first solution.  H/T Data Science Central

## Reinforcement Learning with R

2019-07-18

Holger von Jouanne-Diedrich takes us through concepts in reinforcement learning: At the core this can be stated as the problem a gambler has who wants to play a one-armed bandit: if there are several machines with different winning probabilities (a so-called multi-armed bandit problem) the question the gambler faces is: which machine to play? He could “exploit” one […]

## Biases in Tree-Based Models

2019-07-12

Nina Zumel looks at tree-based ensembling models like random forest and gradient boost and shows that they can be biased: In our previous article , we showed that generalized linear models are unbiased, or calibrated: they preserve the conditional expectations and rollups of the training data. A calibrated model is important in many applications, particularly when financial data […]