Which model selection technique helps to prevent overfitting by penalizing compl

₹0.0

Question: Which model selection technique helps to prevent overfitting by penalizing complex models?

Options:

Correct Answer: Lasso Regression

Solution:

Lasso Regression includes a penalty term that discourages complexity in the model, helping to prevent overfitting.

Which model selection technique helps to prevent overfitting by penalizing complex models?

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.

No concepts available.