DSE Supervised Learning: Classification 5

Question 1

What is 'concept drift' in the context of a deployed classification model?

Accepted Answer

The statistical properties of the target variable change over time, degrading model performance

Answer

Concept drift occurs when the relationship between input features and the target class shifts over time in production, causing model accuracy to degrade.

Question 2

In a confusion matrix for binary classification, what does a False Positive (FP) represent?

Accepted Answer

A negative instance incorrectly classified as positive

Answer

A False Positive occurs when the model predicts the positive class but the true label is negative.

Question 3

What is the purpose of calibrating a classifier's predicted probabilities?

Accepted Answer

To ensure the predicted probabilities accurately reflect true likelihoods

Answer

Calibration aligns predicted probabilities with actual observed frequencies, so a model predicting 0.8 is correct about 80% of the time.

Question 4

Which ensemble method trains base classifiers sequentially, with each model focusing more on previously misclassified examples?

Accepted Answer

Boosting

Answer

Boosting trains classifiers sequentially, reweighting training samples so subsequent models focus on the errors of prior ones.

Question 5

What is the 'curse of dimensionality' and how does it affect k-NN classification?

Accepted Answer

As dimensions increase, distances between points become similar, making nearest neighbors less meaningful

Answer

In high-dimensional spaces, Euclidean distances concentrate, making it hard to distinguish 'near' from 'far' neighbors, which undermines k-NN's core assumption.

DSE - Data Science Practice Test

DSE - Data Science Practice Test

DSE Supervised Learning: Classification 5