Notes around Machine Learning

Machine Learning Notes

Multicollinearity: when two predictors tell the same story

Linear Regression, Assumptions

Homoscedasticity: when the noise grows with the data

Linear Regression, Assumptions

Probability Calibration: when a good score is not a probability

Calibration, Classification

Class Imbalance: when accuracy lies

Classification, Imbalanced Data

Bagging vs Boosting: two ways to beat a single tree

Decision Trees, Random Forest, Gradient Boosting

Decision Trees: how a machine asks questions

Decision Trees, Bias-Variance