THE NAIVE BAYES GUIDE

Why use Naive Bayes?

Section 4: Evaluating the Model Tradeoffs

Reference How to Improve Naive Bayes? Section 3: Tuning the Model in Python, prior to continuing…

A D V A N T A G E S

Q1: Is Naive Bayes a simple or difficult classifier to understand?

Answer: Simple

Q2: Is Naive Bayes an interpretable classifier or not an interpretable classifier?

Answer: Interpretable

Q3: Is Naive Bayes a fast or slow classifier?

Answer: Fast

Q4: Can Naive Bayes handle missing data or sensitive to missing data?

Answer: Handle Missing Data

Q5: Does Naive Bayes increase in error as the number of features increases?

Answer: No Curse of Dimensionality

Q6: Is Naive Bayes more prone to overfitting or less prone to overfitting?

Answer: Less Prone to Overfitting

Q7: Can Naive Bayes handle multicollinearity among independent variables or is sensitive to multicollinearity?

Answer: Handle Multicollinearity in Independent Variables

D I S A D V A N T A G E S

Q8: Can Naive Bayes solve linear problems or non-linear problems?

Answer: Linear Problems

Q9: Can Naive Bayes handle outliers or is sensitive to outliers?

Answer: Sensitive to Outliers

Q10: Can Naive Bayes handle imbalanced data or sensitive to imbalanced data?

Answer: Sensitive to Imbalanced Data