Machine Learning - Testing and Error Metrics

This introductory course explains how to test and improve classification models using train/test splits, cross-validation, confusion matrices, accuracy, precision, recall, and F1 score. It also covers overfitting, underfitting, and grid search for model and hyperparameter selection.

Syllabus

Introduction
Which model is better
Why Testing?
Golden Rule # 1
How do we not 'lose' the training data?
K-Fold Cross Validation
Randomizing in Cross Validation
Evaluation Metrics
Medical Model
Spam Classifier Model
Confusion Matrix Diagnosis
Accuracy
Precision and Recall
Credit Card Fraud
Harmonic mean
F1 Score
Types of Errors
Classification
Error due to variance overfitting
Error due to bias underfitting
Tradeoff
Solution: Cross Validation Testing
Training a Logistic Regression Model
Training a Decision Tree
Training a Support Vector Machine
Grid Search Cross Validation
Parameters and Hyperparameters
How to solve a problem
How to use machine learning
Thank you!