Financial Risk Manager Part 1

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Explanation:

Explanation

Regularization techniques are used to prevent overfitting in machine learning models by adding a penalty term to the loss function. The three main types mentioned are:

A. Ridge regression (L2 regularization): Adds a penalty equal to the square of the magnitude of coefficients
B. LASSO (L1 regularization): Adds a penalty equal to the absolute value of the magnitude of coefficients
C. A hybrid of Ridge and LASSO (Elastic Net): Combines both L1 and L2 penalties

Since all three options (A, B, and C) are valid regularization techniques, the correct answer is D. All the above.

Explanation:

Explanation

Regularization techniques are used to prevent overfitting in machine learning models by adding a penalty term to the loss function. The three main types mentioned are:

A. Ridge regression (L2 regularization): Adds a penalty equal to the square of the magnitude of coefficients
B. LASSO (L1 regularization): Adds a penalty equal to the absolute value of the magnitude of coefficients
C. A hybrid of Ridge and LASSO (Elastic Net): Combines both L1 and L2 penalties

Since all three options (A, B, and C) are valid regularization techniques, the correct answer is D. All the above.

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Q-109. Regularization is an approach for ensuring that models do not become too large or complex, and it is particularly useful when there is a considerable number of highly correlated features. Regularization techniques including:

Exam-Like

Ridge regression.

38.5%

LASSO.

46.2%

A hybrid of the Ridge regression and LASSO.

7.7%

All the above.

7.7%