DSE Knowledge 3

Question 1

Which algorithm is a non-parametric, instance-based learning method that classifies new points based on the majority label among their nearest neighbors?

Accepted Answer

K-Nearest Neighbors (KNN)

Answer

KNN stores training examples and classifies new instances by a vote among the k closest training points in feature space.

Question 2

What is the difference between precision and recall in a classification context?

Accepted Answer

Precision is TP/(TP+FP); recall is TP/(TP+FN)

Answer

Precision measures how many predicted positives are actually positive, while recall measures how many actual positives were correctly identified.

Question 3

Which data structure does a decision tree use to partition feature space?

Accepted Answer

Recursive binary splits based on feature thresholds

Answer

Decision trees recursively split the data using axis-aligned thresholds on individual features, creating a hierarchical partition of the input space.

Question 4

What is 'feature engineering' in a data science workflow?

Accepted Answer

Creating, transforming, or selecting input variables to improve model performance

Answer

Feature engineering involves creating or transforming raw variables to make patterns more learnable by a model.

Question 5

In a confusion matrix for binary classification, what does a False Negative (FN) represent?

Accepted Answer

Model predicted negative; actual class is positive

Answer

A False Negative occurs when the model predicts the negative class but the true label is positive, meaning a real positive was missed.

DSE - Data Science Practice Test

DSE - Data Science Practice Test

DSE Knowledge 3