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Introduction
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Classroom Contents
Machine Learning - Testing and Error Metrics
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- 1 Introduction
- 2 Which model is better
- 3 Why Testing?
- 4 Golden Rule # 1
- 5 How do we not 'lose' the training data?
- 6 K-Fold Cross Validation
- 7 Randomizing in Cross Validation
- 8 Evaluation Metrics
- 9 Medical Model
- 10 Spam Classifier Model
- 11 Confusion Matrix Diagnosis
- 12 Accuracy
- 13 Precision and Recall
- 14 Credit Card Fraud
- 15 Harmonic mean
- 16 F1 Score
- 17 Types of Errors
- 18 Classification
- 19 Error due to variance overfitting
- 20 Error due to bias underfitting
- 21 Tradeoff
- 22 Solution: Cross Validation Testing
- 23 Training a Logistic Regression Model
- 24 Training a Decision Tree
- 25 Training a Support Vector Machine
- 26 Grid Search Cross Validation
- 27 Parameters and Hyperparameters
- 28 How to solve a problem
- 29 How to use machine learning
- 30 Thank you!
