R Programming Language Certification Machine Learning with R

Question 1

Which R package provides a unified interface to train and evaluate many machine learning models?

Accepted Answer

caret

Answer

The caret (Classification And REgression Training) package provides a standardized interface for training, tuning, and evaluating hundreds of ML models in R.

Question 2

In the caret package, which function is used to train a machine learning model?

Accepted Answer

train()

Answer

The train() function in caret is the central function for fitting models, supporting cross-validation, hyperparameter tuning, and preprocessing in one call.

Question 3

Which tidymodels package is responsible for model specification (defining the model type and engine)?

Accepted Answer

parsnip

Answer

parsnip provides a unified API for specifying model type (e.g., logistic_reg(), rand_forest()) and the computational engine (e.g., 'glmnet', 'ranger') separately from fitting.

Question 4

What is the primary purpose of k-fold cross-validation in machine learning?

Accepted Answer

To obtain a more reliable estimate of model performance

Answer

K-fold cross-validation splits data into k subsets, trains on k-1 and validates on the remaining fold repeatedly, producing a more reliable and less biased performance estimate than a single train/test split.

Question 5

In R's tidymodels framework, which package handles data preprocessing and feature engineering steps?

Accepted Answer

recipes

Answer

The recipes package defines preprocessing pipelines (steps) such as normalization, dummy encoding, and imputation, which are applied consistently to training and new data.

R Programming Language Certification Practice Test

R Programming Language Certification Practice Test

R Programming Language Certification Machine Learning with R