Supervised Learning - Regression & Regularization (Weeks 4)
This module explores the critical concepts of supervised learning, focusing on regression techniques and their robustness. It emphasizes the importance of regularization methods such as L1 (Lasso) and L2 (Ridge) to prevent overfitting and improve model generalization. Additionally, the chapter introduces cross-validation methods, including K-Fold and Stratified K-Fold, to assess model performance effectively on unseen data.
Sections
Navigate through the learning materials and practice exercises.
What we have learnt
- The concepts of overfitting and underfitting are vital for deploying effective machine learning models.
- Regularization techniques improve a model's ability to generalize by mitigating overfitting.
- K-Fold and Stratified K-Fold cross-validation methods provide reliable methods for performance evaluation.
Key Concepts
- -- Overfitting
- Overfitting occurs when a model learns the training data too well, including noise, leading to poor performance on unseen data.
- -- Underfitting
- Underfitting happens when a model is too simplistic to capture the underlying patterns in the training data.
- -- Regularization
- Regularization techniques add a penalty to the loss function to discourage overly complex models and improve generalization.
- -- CrossValidation
- Cross-validation is a systematic method for evaluating a model's performance by splitting data into multiple training and validation sets.
- -- KFold CrossValidation
- K-Fold Cross-Validation involves splitting data into K subsets and training the model K times, each time using a different subset as the validation set.
- -- Stratified KFold
- Stratified K-Fold maintains the proportion of classes in each fold, ensuring balanced representation for classification tasks.
Additional Learning Materials
Supplementary resources to enhance your learning experience.