MS-DS Master of Data science Machine Learning 2

Question 1

Which ensemble method trains each successive tree to correct the residual errors of the previous trees?

Accepted Answer

Gradient Boosting

Answer

Gradient Boosting fits each new learner to the residuals (negative gradients) of the ensemble so far, progressively reducing prediction error.

Question 2

In the bias-variance tradeoff, which condition describes a model that performs well on training data but poorly on unseen data?

Accepted Answer

Low bias, high variance

Answer

Low bias means the model fits training data well, but high variance means it captures noise and generalizes poorly to new data — the classic overfitting scenario.

Question 3

What is the primary purpose of the kernel trick in Support Vector Machines?

Accepted Answer

To implicitly map data to a higher-dimensional space without computing the transformation explicitly

Answer

The kernel trick computes dot products in a high-dimensional feature space implicitly, enabling SVMs to find nonlinear decision boundaries without the computational cost of explicit transformation.

Question 4

An ROC curve plots True Positive Rate against which metric?

Accepted Answer

False Positive Rate

Answer

The ROC curve plots TPR (sensitivity) on the y-axis versus FPR (1 − specificity) on the x-axis across all classification thresholds.

Question 5

Which regularization technique randomly drops neurons during training to prevent co-adaptation?

Accepted Answer

Dropout

Answer

Dropout randomly sets a fraction of neuron activations to zero during each training step, forcing the network to learn redundant representations.

MS-DS Master of Data science Practice Test

MS-DS Master of Data science Practice Test

MS-DS Master of Data science Machine Learning 2