Data Science with Python Certification Data Science with Python Supervised Learning Models 3

Question 1

When using sklearn's train_test_split, what does the stratify parameter do?

Accepted Answer

Preserves the class distribution in both train and test sets

Answer

stratify=y ensures the proportion of each class label is maintained in both the training and test subsets.

Question 2

Which of the following is a key difference between bagging and boosting ensemble methods?

Accepted Answer

Boosting trains models sequentially, each correcting the previous model's errors

Answer

Boosting trains each new model to focus on the samples that previous models misclassified, building a strong learner sequentially.

Question 3

In a Ridge regression model, what is the effect of increasing the regularization parameter alpha?

Accepted Answer

Coefficients shrink toward zero, reducing overfitting

Answer

Higher alpha imposes stronger L2 penalty, shrinking all coefficients toward zero and reducing model variance.

Question 4

What is the purpose of cross-validation in supervised learning?

Accepted Answer

To get a more reliable estimate of model generalization performance

Answer

Cross-validation repeatedly splits data into train/validation folds, providing a robust performance estimate that reduces variance compared to a single hold-out split.

Question 5

Which algorithm would be most suitable for a dataset with 1 million samples and 500 features where training speed is critical?

Accepted Answer

Stochastic Gradient Descent Classifier

Answer

SGD Classifier scales efficiently to large datasets by updating weights using one sample (or a mini-batch) at a time, making it much faster than SVM or KNN.

Data Science with Python Certification Practice Test

Data Science with Python Certification Practice Test

Data Science with Python Certification Data Science with Python Supervised Learning Models 3