Statistical Learning Quiz

What is the primary goal of statistical learning?

• To create complex algorithms

• To predict outcomes based on data

• To eliminate all errors in data

• To analyze historical data only

In the model Y = f(X) + ε, what does Y represent?

• The predictor variable

• The measurement error

• The input vector

• The response or target variable

What does the regression function f(x) aim to estimate?

• The maximum value of Y

• The expected value of Y given X

• The variance of Y

• The total number of observations

What is the curse of dimensionality?

• The simplification of models with more variables

• The increase in error with more data points

• The difficulty of estimating functions in high dimensions

• The reduction of data quality with more features

Which of the following is a characteristic of linear models?

• They can only handle one predictor variable

• They are non-parametric models

• They are specified in terms of parameters

• They are always the best fit for any data

What does the bias-variance trade-off refer to?

• The balance between model complexity and prediction accuracy

• The number of predictors in a model

• The difference between observed and predicted values

• The relationship between training and test data

What is the purpose of K-nearest neighbors (KNN) in classification?

• To find the average of all data points

• To assign a class label based on the nearest neighbors

• To eliminate outliers from the dataset

• To create a linear regression model

What is the Bayes optimal classifier based on?

• The average of all predictions

• The conditional class probabilities

• The maximum likelihood estimation

• The least squares method

What does the term 'irreducible error' refer to?

• The error that can be eliminated with more data

• The error that can be minimized by better models

• The error that remains even with the best model

• The error due to incorrect data collection

Which method is known for its flexibility in fitting data?

• Linear regression

• K-nearest neighbors

• Logistic regression

• Generalized additive models