Data Science Flashcards

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

Answer: Principal Component Analysis

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

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

Answer: Model predicted negative but the actual was positive

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

What is the purpose of regularization in machine learning models?

Answer: Prevent overfitting by penalizing large coefficients

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

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

Answer: High bias causes underfitting; high variance causes overfitting

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

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

Answer: Creating or transforming input variables to improve model performance

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

In the context of gradient descent, what is the learning rate?

Answer: A scalar that controls the step size during parameter updates

The learning rate determines how large each update step is when minimizing the loss function.

Which cross-validation strategy is most appropriate when data is time-ordered and temporal leakage must be avoided?

Answer: Time Series Split cross-validation

Time Series Split ensures training folds always precede validation folds to prevent future data leaking into training.