Announcement: New Book by Luis Serrano! Grokking Machine Learning. bit.ly/grokkingML
40% discount code: serranoyt
A friendly journey into the process of evaluating and improving machine learning models.
- Training, Testing
- Evaluation Metrics: Accuracy, Precision, Recall, F1 Score
- Types of Errors: Overfitting and Underfitting
- Cross Validation and K-fold Cross Validation
- Model Evaluation Graphs
- Grid Search
For a code implementation, check out this repo:
0:00 Introduction
0:37 Which model is better
1:31 Why Testing?
3:27 Golden Rule # 1
4:21 How do we not 'lose' the training data?
4:38 K-Fold Cross Validation
5:20 Randomizing in Cross Validation
5:38 Evaluation Metrics
7:53 Medical Model
8:05 Spam Classifier Model
9:25 Confusion Matrix Diagnosis
11:50 Accuracy
19:47 Precision and Recall
20:54 Credit Card Fraud
22:36 Harmonic mean
24:08 F1 Score
27:16 Types of Errors
27:56 Classification
30:03 Error due to variance (overfitting)
30:18 Error due to bias (underfitting)
31:45 Tradeoff
37:55 Solution: Cross Validation Testing
39:16 Training a Logistic Regression Model
40:04 Training a Decision Tree
40:49 Training a Support Vector Machine
41:14 Grid Search Cross Validation
41:59 Parameters and Hyperparameters
42:56 How to solve a problem
43:20 How to use machine learning
44:04 Thank you!