Data Science with Python Certification Data Science with Python Supervised Learning Models 4

Question 1

In a Gradient Boosting model, what are the subsequent trees trained to predict?

Accepted Answer

The residuals (errors) of the previous ensemble

Answer

Each new tree in gradient boosting fits the negative gradient of the loss function, which for MSE equals the residuals from the current ensemble's predictions.

Question 2

What is the Gini impurity used for in decision tree algorithms?

Accepted Answer

Selecting the best feature and threshold for each split

Answer

Gini impurity quantifies how often a randomly chosen element would be misclassified, and the feature/threshold minimizing it is chosen for the split.

Question 3

You train a Naive Bayes classifier and encounter a feature value never seen during training for a class. What problem does this cause?

Accepted Answer

Zero probability issue that collapses the posterior to zero

Answer

In Naive Bayes, a zero likelihood for any feature-class combination multiplies the entire posterior to zero; Laplace smoothing is used to fix this.

Question 4

When performing feature scaling, which supervised learning algorithms are LEAST sensitive to the scale of input features?

Accepted Answer

Decision Trees and Random Forests

Answer

Tree-based models split on feature thresholds independently for each feature, so the relative scale between features does not affect their performance.

Question 5

What does the ROC AUC score of 0.5 indicate about a binary classifier?

Accepted Answer

The model performs no better than random guessing

Answer

AUC of 0.5 corresponds to the diagonal of the ROC curve, meaning the classifier has no discriminating ability beyond random chance.

Data Science with Python Certification Practice Test

Data Science with Python Certification Practice Test

Data Science with Python Certification Data Science with Python Supervised Learning Models 4