MS-DS Master of Data science Supervised Learning Algorithms Questions and Answers Flashcards
6 cards from real MS-DS Master of Data science practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
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Which supervised learning algorithm uses the concept of maximum margin to separate classes?
Answer: Support Vector Machine
SVMs find the hyperplane that maximizes the margin between the closest data points of different classes.
In gradient boosting, what does each subsequent tree attempt to predict?
Answer: The residual errors of the previous model
Each new tree in gradient boosting is trained on the residual errors left by the ensemble of all previous trees.
What is the primary assumption of the Naive Bayes classifier?
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 regularization technique in linear regression can shrink coefficients exactly to zero, enabling feature selection?
Answer: L1 (Lasso)
L1 regularization (Lasso) penalizes the absolute value of coefficients, which can drive some coefficients to exactly zero.
What problem does the kernel trick solve in Support Vector Machines?
Answer: It allows SVMs to find non-linear decision boundaries without explicitly mapping to higher dimensions
The kernel trick computes dot products in a high-dimensional space without explicitly transforming the data, enabling non-linear classification.
In a Random Forest, what technique is used to ensure diversity among individual decision trees?
Answer: Bagging with random feature subsets at each split
Random Forest combines bootstrap aggregating (bagging) with random feature selection at each split to decorrelate the trees.