Machine Learning and AI Foundations: Classification Modeling

Introduction

• Classification problems in machine learning
• What you should know
• Defining terms

1. The Big Picture: Defining Your Classification Strategy

• 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

2. How Do I Choose a "Winner"?

• Training and test partitions
• Lift Charts
• Gains tables
• Confusion matrix

3. Algorithms on Parade

• 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
• Boosting and XGBoost

4. Common Modeling Challenges

• Imbalanced target categories
• Interactions
• Missing data
• Bias-variance trade-off and overfitting
• Data reduction
• AutoML

Conclusion

• Next steps