Data Science Model Evaluation and Validation 4

Question 1

High training accuracy but low test accuracy is a classic symptom of:

Accepted Answer

Overfitting

Answer

Overfitting means the model memorizes training data but fails to generalize.

Question 2

In the bias-variance tradeoff, a model with high bias typically:

Accepted Answer

Underfits and performs poorly on both training and test data

Answer

High bias means the model is too simple, underfitting both sets.

Question 3

Which technique most directly reduces overfitting by penalizing large coefficients?

Accepted Answer

Regularization (L1/L2)

Answer

L1/L2 regularization adds a penalty term that shrinks coefficients, reducing variance.

Question 4

Early stopping prevents overfitting during training by:

Accepted Answer

Halting training when validation performance stops improving

Answer

Early stopping ends training once validation loss begins to rise, avoiding over-training.

Question 5

A learning curve where both training and validation errors remain high suggests:

Accepted Answer

Underfitting (high bias)

Answer

Persistently high errors on both sets indicate the model is too simple.

Data Science Practice Test