Machine Learning models# This section covers the specifics about machine learning models. Linear models Linear Regression Linear regression Fit a simple linear regression to 2D data Multilinear regression Adapting linear regression to nonlinear relationships Polynomial Regression Bayesian Regression Ridge Regression LASSO Regression Logistic Regression ML Models Linear models: Nonlinear models: k-Nearest Neighbors kNN for classification kNN for regression Selection of the k value Strengths of kNN Weaknesses of kNN Decision Tree Decision Trees for classification Decision Trees for regression Building a Decision Tree Strengths of Decision Trees Weaknesses of Decision Trees Gradient Boosting Function to train a model with GB and hyperparameter search Hyperparameters - Gradient Boosting Tree-specific hyperparameters Minimum samples for splitting Minimum samples per leaf Minimum samples per leaf as a fraction of total samples Maximum depth of a tree Maximum number of leaves in a tree Maximum number of features Boosting hyperparameters Learning rate Number of sequential trees Fraction of observations to be selected for each tree Other hyperparameters Loss function Hyperparameters grid in scikit-learn
