Which model selection technique helps to prevent overfitting by penalizing compl
Practice Questions
Q1
Which model selection technique helps to prevent overfitting by penalizing complex models?
Grid Search
Lasso Regression
K-Fold Cross-Validation
Random Search
Questions & Step-by-Step Solutions
Which model selection technique helps to prevent overfitting by penalizing complex models?
Step 1: Understand what overfitting means. Overfitting happens when a model learns the training data too well, including noise, and performs poorly on new data.
Step 2: Learn about model complexity. A complex model has many parameters and can fit the training data very closely.
Step 3: Know that we want to avoid overfitting by keeping our model simple.
Step 4: Discover Lasso Regression. Lasso Regression is a technique that adds a penalty to the model for being too complex.
Step 5: Understand the penalty term in Lasso Regression. This term discourages the model from using too many parameters, which helps keep it simple.
Step 6: Conclude that Lasso Regression helps prevent overfitting by penalizing complex models.
