Regularization Techniques in Regression

Regularization

• Ridge Regression (L2 Regularization) adds a penalty equal to the sum of the squares of the coefficients to the loss function
• Lasso Regression (L1 Regularization) adds a penalty equal to the sum of the absolute values of the coefficients to the loss function
• Elastic Net combines L1 and L2 regularization

Implementing Regularization

• To add L1 regularization to a logistic regression, select "L1" under "Regularization type" in the "Logistic Regression" module

Causes of Overfitting

• Using complex models with too many parameters relative to the size of the dataset
• Having insufficient training data, making the model susceptible to noise
• Presence of noise in the data, leading the model to learn patterns that do not generalize

Consequences of Overfitting

• Poor generalization, where the model performs well on the training data but poorly on new, unseen data
• Model becomes unreliable for practical use due to lack of generalization

Supervised Learning

• A key concept in machine learning and AI, where an algorithm learns from labeled training data to make predictions or decisions
• Involves training a model on a labeled dataset to learn a mapping from inputs to outputs
• Goal is for the model to make predictions or decisions on new, unseen data

Types of Supervised Learning

• Regression: used for predicting continuous values
• Examples: predicting stock prices, estimating portfolio returns
• Classification: used for predicting discrete categories
• Examples: credit scoring, predicting loan defaults