DSE Data Science 2

Question 1

Which technique is used to reduce the dimensionality of data while preserving as much variance as possible?

Accepted Answer

Principal Component Analysis

Answer

PCA transforms data into orthogonal components ordered by the amount of variance they explain.

Question 2

In a confusion matrix, what does a False Negative represent?

Accepted Answer

Model predicted negative but the actual was positive

Answer

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

Question 3

What is the purpose of regularization in machine learning models?

Accepted Answer

Prevent overfitting by penalizing large coefficients

Answer

Regularization adds a penalty term to the loss function to discourage overly complex models.

Question 4

Which of the following best describes the bias-variance tradeoff?

Accepted Answer

High bias causes underfitting; high variance causes overfitting

Answer

Simple models have high bias (underfitting) while complex models have high variance (overfitting).

Question 5

What does the term 'feature engineering' refer to in data science?

Accepted Answer

Creating or transforming input variables to improve model performance

Answer

Feature engineering involves creating new features or modifying existing ones to help the model learn better.

DSE - Data Science Practice Test