sklearn.model_selection.train_test_split

sklearn.model_selection.train_test_split(*arrays, test_size=None, train_size=None, random_state=None, shuffle=True, stratify=None)

Split arrays or matrices into random train and test subsets.

Quick utility that wraps input validation, next(ShuffleSplit().split(X, y)), and application to input data into a single call for splitting (and optionally subsampling) data into a one-liner.

Read more in the User Guide.

Parameters:

*arrayssequence of indexables with same length / shape[0]

Allowed inputs are lists, numpy arrays, scipy-sparse matrices or pandas dataframes.

test_sizefloat or int, default=None

If float, should be between 0.0 and 1.0 and represent the proportion of the dataset to include in the test split. If int, represents the absolute number of test samples. If None, the value is set to the complement of the train size. If train_size is also None, it will be set to 0.25.

train_sizefloat or int, default=None

If float, should be between 0.0 and 1.0 and represent the proportion of the dataset to include in the train split. If int, represents the absolute number of train samples. If None, the value is automatically set to the complement of the test size.

random_stateint, RandomState instance or None, default=None

Controls the shuffling applied to the data before applying the split. Pass an int for reproducible output across multiple function calls. See Glossary.

shufflebool, default=True

Whether or not to shuffle the data before splitting. If shuffle=False then stratify must be None.

stratifyarray-like, default=None

If not None, data is split in a stratified fashion, using this as the class labels. Read more in the User Guide.

Returns:

splittinglist, length=2 * len(arrays)

List containing train-test split of inputs.

New in version 0.16: If the input is sparse, the output will be a scipy.sparse.csr_matrix. Else, output type is the same as the input type.

Examples

>>> import numpy as np
>>> from sklearn.model_selection import train_test_split
>>> X, y = np.arange(10).reshape((5, 2)), range(5)
>>> X
array([[0, 1],
       [2, 3],
       [4, 5],
       [6, 7],
       [8, 9]])
>>> list(y)
[0, 1, 2, 3, 4]

>>> X_train, X_test, y_train, y_test = train_test_split(
...     X, y, test_size=0.33, random_state=42)
...
>>> X_train
array([[4, 5],
       [0, 1],
       [6, 7]])
>>> y_train
[2, 0, 3]
>>> X_test
array([[2, 3],
       [8, 9]])
>>> y_test
[1, 4]

>>> train_test_split(y, shuffle=False)
[[0, 1, 2], [3, 4]]

Examples using sklearn.model_selection.train_test_split

Release Highlights for scikit-learn 0.24

Release Highlights for scikit-learn 0.24

Release Highlights for scikit-learn 0.23

Release Highlights for scikit-learn 0.23

Release Highlights for scikit-learn 0.22

Release Highlights for scikit-learn 0.22

Comparison of Calibration of Classifiers

Comparison of Calibration of Classifiers

Probability Calibration curves

Probability Calibration curves

Probability calibration of classifiers

Probability calibration of classifiers

Classifier comparison

Classifier comparison

Recognizing hand-written digits

Recognizing hand-written digits

Principal Component Regression vs Partial Least Squares Regression

Principal Component Regression vs Partial Least Squares Regression

Post pruning decision trees with cost complexity pruning

Post pruning decision trees with cost complexity pruning

Understanding the decision tree structure

Understanding the decision tree structure

Kernel PCA

Kernel PCA

Comparing random forests and the multi-output meta estimator

Comparing random forests and the multi-output meta estimator

Discrete versus Real AdaBoost

Discrete versus Real AdaBoost

Early stopping of Gradient Boosting

Early stopping of Gradient Boosting

Feature importances with a forest of trees

Feature importances with a forest of trees

Feature transformations with ensembles of trees

Feature transformations with ensembles of trees

Gradient Boosting Out-of-Bag estimates

Gradient Boosting Out-of-Bag estimates

Gradient Boosting regression

Gradient Boosting regression

Gradient Boosting regularization

Gradient Boosting regularization

IsolationForest example

IsolationForest example

Multi-class AdaBoosted Decision Trees

Multi-class AdaBoosted Decision Trees

Prediction Intervals for Gradient Boosting Regression

Prediction Intervals for Gradient Boosting Regression

Faces recognition example using eigenfaces and SVMs

Faces recognition example using eigenfaces and SVMs

Image denoising using kernel PCA

Image denoising using kernel PCA

Model Complexity Influence

Model Complexity Influence

Prediction Latency

Prediction Latency

Pipeline ANOVA SVM

Pipeline ANOVA SVM

Univariate Feature Selection

Univariate Feature Selection

Comparing various online solvers

Comparing various online solvers

Early stopping of Stochastic Gradient Descent

Early stopping of Stochastic Gradient Descent

L1-based models for Sparse Signals

L1-based models for Sparse Signals

MNIST classification using multinomial logistic + L1

MNIST classification using multinomial logistic + L1

Multiclass sparse logistic regression on 20newgroups

Multiclass sparse logistic regression on 20newgroups

Non-negative least squares

Non-negative least squares

Poisson regression and non-normal loss

Poisson regression and non-normal loss

Tweedie regression on insurance claims

Tweedie regression on insurance claims

Common pitfalls in the interpretation of coefficients of linear models

Common pitfalls in the interpretation of coefficients of linear models

Failure of Machine Learning to infer causal effects

Failure of Machine Learning to infer causal effects

Permutation Importance vs Random Forest Feature Importance (MDI)

Permutation Importance vs Random Forest Feature Importance (MDI)

Permutation Importance with Multicollinear or Correlated Features

Permutation Importance with Multicollinear or Correlated Features

Scalable learning with polynomial kernel approximation

Scalable learning with polynomial kernel approximation

Evaluation of outlier detection estimators

Evaluation of outlier detection estimators

Introducing the set_output API

Introducing the set_output API

ROC Curve with Visualization API

ROC Curve with Visualization API

Visualizations with Display Objects

Visualizations with Display Objects

Class Likelihood Ratios to measure classification performance

Class Likelihood Ratios to measure classification performance

Confusion matrix

Confusion matrix

Custom refit strategy of a grid search with cross-validation

Custom refit strategy of a grid search with cross-validation

Detection error tradeoff (DET) curve

Detection error tradeoff (DET) curve

Multiclass Receiver Operating Characteristic (ROC)

Multiclass Receiver Operating Characteristic (ROC)

Precision-Recall

Precision-Recall

Train error vs Test error

Train error vs Test error

Classifier Chain

Classifier Chain

Comparing Nearest Neighbors with and without Neighborhood Components Analysis

Comparing Nearest Neighbors with and without Neighborhood Components Analysis

Dimensionality Reduction with Neighborhood Components Analysis

Dimensionality Reduction with Neighborhood Components Analysis

Nearest Neighbors Classification

Nearest Neighbors Classification

Restricted Boltzmann Machine features for digit classification

Restricted Boltzmann Machine features for digit classification

Varying regularization in Multi-layer Perceptron

Varying regularization in Multi-layer Perceptron

Visualization of MLP weights on MNIST

Visualization of MLP weights on MNIST

Column Transformer with Mixed Types

Column Transformer with Mixed Types

Effect of transforming the targets in regression model

Effect of transforming the targets in regression model

Feature discretization

Feature discretization

Importance of Feature Scaling

Importance of Feature Scaling

Map data to a normal distribution

Map data to a normal distribution

Target Encoder’s Internal Cross fitting

Target Encoder's Internal Cross fitting

Semi-supervised Classification on a Text Dataset

Semi-supervised Classification on a Text Dataset