Master of Data science Supervised Learning Algorithms 1 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 of the following algorithms builds an ensemble by training each new model to correct the errors made by the previous models sequentially?
Answer: AdaBoost
AdaBoost (Adaptive Boosting) sequentially trains weak learners, assigning higher weights to misclassified samples so that each subsequent learner focuses on the errors of its predecessor.
In a decision tree, which criterion measures the impurity of a node by computing the probability that two randomly chosen samples belong to different classes?
Answer: Gini Impurity
Gini Impurity measures node impurity as 1 minus the sum of squared class probabilities, representing the likelihood that a randomly selected sample would be incorrectly classified.
What is the primary purpose of the kernel trick in Support Vector Machines?
Answer: To implicitly map input features into a higher-dimensional space without computing the transformation explicitly
The kernel trick allows SVMs to find a linear separating hyperplane in a higher-dimensional feature space by computing inner products via a kernel function, avoiding the computational cost of explicit transformation.
In logistic regression, which function maps the raw linear output to a probability between 0 and 1?
Answer: Sigmoid function
The sigmoid (logistic) function σ(z) = 1/(1+e^−z) squashes any real-valued linear combination of features into the (0,1) interval, making it suitable for binary probability estimation.
Which evaluation metric is most appropriate when the cost of false negatives is much higher than the cost of false positives in a binary classification problem?
Answer: Recall (Sensitivity)
Recall (TP / (TP + FN)) directly measures how many actual positives are correctly identified, making it the priority metric when missing a positive case (false negative) is especially costly, such as in medical diagnosis.
What distinguishes Ridge Regression from Ordinary Least Squares regression?
Answer: Ridge Regression adds an L2 penalty term to the loss function, shrinking coefficients toward zero but not eliminating them
Ridge Regression augments the OLS cost function with a penalty proportional to the sum of squared coefficients (L2 norm), which shrinks estimates to reduce variance and overfitting, but unlike Lasso it does not produce exact zero coefficients.