What function randomly splits a data set into training and testing subsets?

Question

BlackTom AI · Accepted Answer

train_test_split() randomly splits a dataset into training and testing subsets by design, so this is the correct function.

1) Relevant knowledge points and concepts:
- In scikit-learn, model evaluation commonly requires partitioning data into training and testing sets.
- The function train_test_split from sklearn.model_selection performs a single randomized split.
- Cross-validation functions (cross_val_score, cross_val_predict) perform repeated splitting into folds rather than a single random train/test split.
- Key parameters: test_size (or train_size), random_state (for reproducibility), and shuffle.

2) Step-by-step reasoning:
- Inspect the answer options: "train_test(split=x)" is not a valid sklearn API; "cross_val_predict()" and "cross_val_score()" are valid sklearn functions but serve different purposes.
- The question asks for the function that "randomly splits a data set into training and testing subsets" — this matches the documented behavior of train_test_split().

3) Example and intermediate steps:
- Typical usage:
  from sklearn.model_selection import train_test_split
  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
- If X has 100 samples and test_size=0.2:
  - Compute test count: 100 * 0.2 = 20
  - Compute train count: 100 - 20 = 80
  - The function shuffles (by default) and returns 80 training and 20 testing samples; setting random_state fixes the random shuffle.

4) Why the correct answer is correct:
- train_test_split is explicitly implemented to split arrays or matrices into random train and test subsets and is the standard scikit-learn utility for this exact task.

5) Why other options are incorrect:
- cross_val_score(): computes evaluation scores by cross-validation across multiple folds (not a single train/test split).
- cross_val_predict(): returns cross-validated estimates/predictions for each input data point using CV; it does not produce a single train/test split.
- train_test(split=x): not a valid function name/syntax in scikit-learn.

6) Conclusion:
The correct function is train_test_split(), which matches the provided answer.

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