Data Science with Python Certification Data Science with Python Model Evaluation and Validation 3

Question 1

In regression model evaluation, what does a residual plot with a clear funnel shape (heteroscedasticity) suggest?

Accepted Answer

Residual variance increases with predicted values, violating OLS assumptions

Answer

A funnel-shaped residual plot indicates heteroscedasticity — the error variance is not constant, violating a key linear regression assumption.

Question 2

Which scikit-learn utility allows you to chain preprocessing steps and a model estimator so that cross-validation applies transformations correctly within each fold?

Accepted Answer

Pipeline

Answer

sklearn.pipeline.Pipeline ensures that fit/transform steps are applied only to training data within each fold, preventing data leakage.

Question 3

What is the bias-variance tradeoff implication of increasing model complexity on a fixed dataset?

Accepted Answer

Bias decreases and variance increases

Answer

More complex models fit training data more closely (lower bias) but become sensitive to noise, increasing variance and risking overfitting.

Question 4

Which metric penalizes large prediction errors more heavily due to squaring residuals?

Accepted Answer

Mean Squared Error (MSE)

Answer

MSE squares each residual before averaging, so large errors contribute disproportionately more to the final score than small errors.

Question 5

In the context of model evaluation, what is 'overfitting' most precisely?

Accepted Answer

Low training error and high test error

Answer

An overfit model memorizes training data, achieving low training error but failing to generalize, resulting in high test error.

Data Science with Python Certification Practice Test

Data Science with Python Certification Practice Test

Data Science with Python Certification Data Science with Python Model Evaluation and Validation 3