Supervised Classification

Models

• Logistic Regression

  High explainability, reasonable computation cost.

• Decision Tree

  Performs classification, regression, and multi-output tasks. Good at finding orthogonal decision boundaries.

  But very sensitive to small changes in the data, which make them hard to train.

• Random Forest

  Very powerful model. Uses an ensemble method to combine multiple decision trees.

• Support Vector Machine (SVM)

  Popular model that performs linear and non-linear classification, regression, and outlier detection.

  Works well with small to medium sized datasets.

• K-Nearest Neighbors (KNN)

• Naive Bayes

• Multi Layer Perceptron (MLP)

  A neural network model that can learn non-linear decision boundaries.

  Good for large datasets.