Learning Naive Bayes

Sunil Ray explains the Naive Bayes algorithm:

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

Related Posts

Bias Correction In Standard Deviation Estimates

John Mount explains how to perform bias correction and explains why it happens so rarely in practice: The bias in question is falling off at a rate of 1/n (where n is our sample size). So the bias issue loses what little gravity it ever may have ever had when working with big data. Most sources of noise will […]

Read More

Explaining Neural Networks With H2O

Shirin Glander explains some of the concepts behind neural networks using H2O as a guide: Before, when describing the simple perceptron, I said that a result is calculated in a neuron, e.g. by summing up all the incoming data multiplied by weights. However, this has one big disadvantage: such an approach would only enable our neural net […]

Read More

Categories

September 2017
MTWTFSS
« Aug Oct »
 123
45678910
11121314151617
18192021222324
252627282930