Boosting and XGBoost

Classification problems in machine learning

What you should know

Defining terms

The importance of binary classification

Binary vs. multinomial

So-called “black box” techniques

One task, many algorithms

Statistics vs. machine learning

Model assessment vs. business evaluation

Training and test partitions

Lift Charts

Gains tables

Confusion matrix

Overview

Discriminant with three categories

Discriminant with two categories

Stepwise discriminant

Logistic regression

Stepwise logistic regression

Decision Trees

KNN

Linear SVM

Neural nets

Bayesian networks

Heterogenous ensembles

Bagging and random forest

Imbalanced target categories

Interactions

Missing data

Bias-variance trade-off and overfitting

Data reduction

AutoML

Next steps