Part 3 - Classification

Unlike regression where we predict a continuous number, you use classification to Predict a Category. There is a wide variety of classification applications from medicine to marketing. Classification models include linear models like

• Logistic Regression, SVM
• K-Nearest Neighbors (K-NN)
• Support Vector Machine (SVM)
• Kernel SVM
• Naive Bayes
• Decision Tree Classification
• Random Forest Classification

Which model to choose for my problem?
 - For Linear Problem - Choose Logistic Regression or SVM
 - For Non-Linear Problem - K-NN, Naive Bayes, Decision Tree or Random Forest

- Logistic Regression
- When you want to rank your predictions by their probability.
- E.g. To rank your customers from the highest probability that they buy a certain product, to the lowest probability.

- SVM
- When you want to predict to which segment your customers belong to. (Male/Female, Orange/Apple etc)

- Naive Bayes
- When you want to rank your predictions by their probability.
- E.g. To rank your customers from the highest probability that they buy a certain product, to the lowest probability. 
- Decision Tree when you want to have clear interpretation of your model results,
- Random Forest when you are just looking for high performance with less need for interpretation.


Hope this helps!!

Regards,

Arun Manglick