Data Science Data Science Supervised Learning Models Questions and Answers 3

Question 1

Which supervised learning method models the probability of a binary outcome using a logistic (sigmoid) function?

Accepted Answer

Logistic Regression

Answer

Logistic regression applies the sigmoid function to a linear combination of features to output probabilities for binary classification.

Question 2

What is the bias-variance tradeoff in supervised learning?

Accepted Answer

The balance between a model's ability to fit training data closely and its ability to generalize to new data

Answer

The bias-variance tradeoff describes how reducing bias (underfitting) often increases variance (overfitting) and vice versa.

Question 3

In a k-Nearest Neighbors classifier, what effect does increasing the value of k typically have on the decision boundary?

Accepted Answer

The decision boundary becomes smoother and less sensitive to noise

Answer

A larger k averages over more neighbors, which smooths the decision boundary and reduces sensitivity to individual noisy data points.

Question 4

Which technique splits the dataset into multiple folds to train and validate a supervised model, ensuring every observation is used for both?

Accepted Answer

K-Fold Cross-Validation

Answer

K-fold cross-validation divides data into k equal folds, rotating which fold serves as the validation set so every sample is tested exactly once.

Question 5

What assumption does Naive Bayes make about the relationship between features given the class label?

Accepted Answer

All features are conditionally independent given the class

Answer

Naive Bayes assumes that each feature contributes independently to the probability of a class, which simplifies computation but rarely holds exactly.

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