Hyperparameter Tuning

Question 1

Permutation set

Answer 1

A set of elements arranged in a specific order. In machine learning, permutation is important in evaluating the effect of each feature on model performance by shuffling its values.

Question 2

Polynomial Features

Answer 2

Used to convert features to a higher degree.
If there are 2 features [a, b], then degree-2 polynomial features are [1, a, b, a^2, ab, b^2].

Question 3

When should polynomial features be used?

Answer 3

Use when the relationship between the target variable and independent variables is nonlinear. This can be determined with a negative R2score

Question 4

T/F: Hypberperameters are learned from the data?

Answer 4

False: Hyperperameters are used explicitly to tune the model.

Question 5

Data leakage can be caused by

Answer 5

Treating the training and test data together before splitting.