MS-DS Master of Data science Supervised Learning Algorithms 4

Question 1

What distinguishes Ridge regression from ordinary least squares?

Accepted Answer

Ridge adds an L2 penalty that shrinks coefficients toward zero without eliminating them

Answer

Ridge regression (L2 regularization) adds a penalty equal to the sum of squared coefficients, shrinking them uniformly toward zero but rarely setting any exactly to zero.

Question 2

In a random forest, what is the purpose of feature randomness (selecting a subset of features at each split)?

Accepted Answer

It decorrelates the individual trees, reducing ensemble variance

Answer

By considering only a random subset of features at each split, trees in a random forest are de-correlated, so their errors are less likely to coincide, reducing overall variance.

Question 3

Which scenario is most likely to benefit from using a polynomial regression model instead of linear regression?

Accepted Answer

When the relationship between the feature and target is curved or non-linear

Answer

Polynomial regression extends linear regression by adding polynomial terms, allowing it to fit curved relationships between predictors and a continuous outcome.

Question 4

What is the purpose of the learning rate in gradient descent optimization?

Accepted Answer

It controls the step size taken in the direction of the negative gradient

Answer

The learning rate scales the gradient to determine how large a step is taken when updating parameters; too large causes divergence, too small causes slow convergence.

Question 5

In logistic regression, what function maps the linear combination of inputs to a probability?

Accepted Answer

Sigmoid (logistic) function

Answer

The sigmoid function maps any real-valued linear combination to a value between 0 and 1, which can be interpreted as a class probability in binary logistic regression.

MS-DS Master of Data science Practice Test

MS-DS Master of Data science Practice Test

MS-DS Master of Data science Supervised Learning Algorithms 4