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train_test_split: Random Train/Test Data Splitter

The train_test_split function provides a simple and flexible way to split your dataset into random training and testing subsets. It accepts NumPy arrays, Pandas DataFrame/Series, and can also handle lists or tuples as input. This function is essential for evaluating machine learning models on unseen data and is a core utility in most ML workflows.

Overview

Splitting your data into training and testing sets is a fundamental step in machine learning. The train_test_split function allows you to:

  • Randomly partition your data into train and test sets.
  • Specify the proportion or absolute number of test samples.
  • Shuffle your data for unbiased splitting.
  • Use a random seed for reproducibility.
  • Split both features (X) and targets (y) in a consistent manner.

Parameters

Parameter Type Default Description
X array-like — Feature data to be split. Accepts NumPy arrays or Pandas DataFrame. Must be indexable and of consistent length.
y array-like or None None Target data to be split alongside X. Accepts NumPy arrays or Pandas Series/DataFrame column. Must be same length as X.
test_size float or int 0.25 If float, fraction of data for test set (0.0 < test_size < 1.0). If int, absolute number of test samples.
shuffle bool True Whether to shuffle the data before splitting.
random_seed int or None None Controls the shuffling for reproducibility.

Returns

  • X_train, X_test: np.ndarray
    Train-test split of X.

  • y_train, y_test: np.ndarray or None
    Train-test split of y. If y is None, these will also be None.

Raises

  • ValueError
    If inputs are invalid or test_size is not appropriate.

  • TypeError
    If test_size is not a float or int.

Example Usage

import numpy as np
from machinegnostics.models import train_test_split

# Create sample data
X = np.arange(20).reshape(10, 2)
y = np.arange(10)

# Split into train and test sets
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.3, shuffle=True, random_seed=42
)

print("X_train:", X_train)
print("X_test:", X_test)
print("y_train:", y_train)
print("y_test:", y_test)

Notes

  • If y is not provided, only X will be split and y_train, y_test will be None.
  • If test_size is a float, it must be between 0.0 and 1.0 (exclusive).
  • If test_size is an int, it must be between 1 and len(X) - 1.
  • Setting shuffle=False will split the data in order, without randomization.
  • Use random_seed for reproducible splits.

Author: Nirmal Parmar
Date: 2025-05-01