FREE Data Science Supervised Learning Algorithms Questions and Answers Flashcards

Which supervised learning algorithm uses hyperplanes to separate data into classes and maximizes the margin between them?

Answer: Support Vector Machine

Support Vector Machines find the optimal hyperplane that maximizes the margin between classes in the feature space.

In gradient boosting, what does each successive tree attempt to correct?

Answer: The residual errors of the previous ensemble

Each new tree in gradient boosting is trained on the residual errors left by the cumulative prediction of all prior trees.

What is the primary assumption made by the Naive Bayes classifier that simplifies its computation?

Answer: Features are conditionally independent given the class label

Naive Bayes assumes that all features are conditionally independent of each other given the class label, which greatly simplifies probability calculations.

Which regularization technique in linear regression adds the sum of squared coefficients as a penalty term to the loss function?

Answer: Ridge regression (L2)

Ridge regression applies L2 regularization by adding the sum of squared coefficients multiplied by a penalty parameter to the loss function.

What problem does the 'kernel trick' solve in Support Vector Machines?

Answer: It enables classification of non-linearly separable data without explicitly mapping to higher dimensions

The kernel trick allows SVMs to operate in a high-dimensional feature space without explicitly computing the transformation, enabling non-linear decision boundaries.

In a decision tree, what does the Gini impurity measure of 0 indicate about a node?

Answer: All samples in the node belong to a single class

A Gini impurity of 0 means the node is perfectly pure, containing samples from only one class.