Data Science Supervised Learning Algorithms 5

Question 1

What distinguishes a generative classifier like Naive Bayes from a discriminative one like logistic regression?

Accepted Answer

Generative models the joint distribution P(x,y); discriminative models P(y|x) directly

Answer

Generative classifiers model the joint distribution while discriminative classifiers model the conditional P(y|x) directly.

Question 2

Why might one-hot encoding be necessary before applying linear models to categorical data?

Accepted Answer

Because models treat numeric category codes as having ordinal magnitude

Answer

One-hot encoding prevents the model from assuming a false ordinal relationship among category integer codes.

Question 3

What is the role of support vectors in an SVM?

Accepted Answer

They are the points closest to the decision boundary that define the margin

Answer

Support vectors are the boundary-defining points that determine the position and width of the margin.

Question 4

When predicting a continuous target, which supervised algorithm is appropriate?

Accepted Answer

Linear regression

Answer

Linear regression predicts continuous numeric outcomes, unlike classification algorithms.

Question 5

What does early stopping accomplish when training a boosted model?

Accepted Answer

It halts adding trees when validation performance stops improving to prevent overfitting

Answer

Early stopping ends training once validation error stops improving, reducing overfitting.

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