Data Science with Python Certification Data Science with Python Supervised Learning Models 2

Question 1

Which scikit-learn parameter controls the maximum depth of a decision tree to prevent overfitting?

Accepted Answer

max_depth

Answer

max_depth limits how deep the tree grows, reducing overfitting by preventing the model from memorizing training data.

Question 2

In logistic regression, what function maps the linear output to a probability between 0 and 1?

Accepted Answer

Sigmoid

Answer

The sigmoid function squashes any real-valued number into the (0,1) range, making it suitable for binary classification probabilities.

Question 3

A Support Vector Machine with an RBF kernel has a high value of gamma. What effect does this have?

Accepted Answer

The model overfits with a complex, tightly fitted boundary

Answer

High gamma means each training point has a small radius of influence, causing the model to fit training data very tightly and potentially overfit.

Question 4

Which evaluation metric is most appropriate when false negatives are more costly than false positives, such as in cancer detection?

Accepted Answer

Recall

Answer

Recall (sensitivity) measures the proportion of actual positives correctly identified, minimizing missed cases (false negatives).

Question 5

In a Random Forest, what technique introduces randomness when building each tree?

Accepted Answer

Bootstrap sampling and random feature subsets

Answer

Random Forest uses bootstrap sampling of data rows and random subsets of features at each split, reducing correlation between trees.

Data Science with Python Certification Practice Test