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)[source]¶
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 1.4
Release Highlights for scikit-learn 0.24
Release Highlights for scikit-learn 0.23
Release Highlights for scikit-learn 0.22
Comparison of Calibration of Classifiers
Probability Calibration curves
Probability calibration of classifiers
Recognizing hand-written digits
Principal Component Regression vs Partial Least Squares Regression
Post pruning decision trees with cost complexity pruning
Understanding the decision tree structure
Comparing random forests and the multi-output meta estimator
Early stopping in Gradient Boosting
Feature importances with a forest of trees
Feature transformations with ensembles of trees
Gradient Boosting Out-of-Bag estimates
Gradient Boosting regularization
Multi-class AdaBoosted Decision Trees
Prediction Intervals for Gradient Boosting Regression
Faces recognition example using eigenfaces and SVMs
Image denoising using kernel PCA
Lagged features for time series forecasting
Comparing various online solvers
Early stopping of Stochastic Gradient Descent
L1-based models for Sparse Signals
MNIST classification using multinomial logistic + L1
Multiclass sparse logistic regression on 20newgroups
Poisson regression and non-normal loss
Tweedie regression on insurance claims
Common pitfalls in the interpretation of coefficients of linear models
Failure of Machine Learning to infer causal effects
Permutation Importance vs Random Forest Feature Importance (MDI)
Permutation Importance with Multicollinear or Correlated Features
Scalable learning with polynomial kernel approximation
Evaluation of outlier detection estimators
Introducing the set_output API
ROC Curve with Visualization API
Visualizations with Display Objects
Class Likelihood Ratios to measure classification performance
Custom refit strategy of a grid search with cross-validation
Detection error tradeoff (DET) curve
Multiclass Receiver Operating Characteristic (ROC)
Multilabel classification using a classifier chain
Comparing Nearest Neighbors with and without Neighborhood Components Analysis
Dimensionality Reduction with Neighborhood Components Analysis
Nearest Neighbors Classification
Restricted Boltzmann Machine features for digit classification
Varying regularization in Multi-layer Perceptron
Visualization of MLP weights on MNIST
Column Transformer with Mixed Types
Effect of transforming the targets in regression model
Map data to a normal distribution
Target Encoder’s Internal Cross fitting
Semi-supervised Classification on a Text Dataset