sklearn.model_selection.train_test_split() function

The train_test_split() method is used to split our data into train and test sets. First, we need to divide our data into features (X) and labels (y). The dataframe gets divided into X_train, X_test, y_train, and y_test. X_train and y_train sets are used for training and fitting the model. The X_test and y_test sets are used for testing the model if it’s predicting the right outputs/labels. we can explicitly test the size of the train and test sets. It is suggested to keep our train sets larger than the test sets.

• Train set: The training dataset is a set of data that was utilized to fit the model. The dataset on which the model is trained. This data is seen and learned by the model.
• Test set: The test dataset is a subset of the training dataset that is utilized to give an accurate evaluation of a final model fit.
• validation set:  A validation dataset is a sample of data from your model’s training set that is used to estimate model performance while tuning the model’s hyperparameters.
• underfitting: A data model that is under-fitted has a high error rate on both the training set and unobserved data because it is unable to effectively represent the relationship between the input and output variables.
• overfitting:  when a statistical model matches its training data exactly but the algorithm’s goal is lost because it is unable to accurately execute against unseen data is called overfitting

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

Parameters:

• *arrays: sequence of indexables. Lists, numpy arrays, scipy-sparse matrices, and pandas dataframes are all valid inputs.
• test_size: int or float, by default None. If float, it should be between 0.0 and 1.0 and represent the percentage of the dataset to test split. If int is used, it refers to the total number of test samples. If the value is None, the complement of the train size is used. It will be set to 0.25 if train size is also None.
• train_size: int or float, by default None.
• random_state : int,by default None. Controls how the data is shuffled before the split is implemented. For repeatable output across several function calls, pass an int.
• shuffle: boolean object , by default True. Whether or not the data should be shuffled before splitting. Stratify must be None if shuffle=False.
• stratify: array-like object , by default it is None. If None is selected, the data is stratified using these as class labels.

Returns:

splitting: The train-test split of inputs is represented as a list.

In this article, we will discuss how to split a dataset using scikit-learns’ train_test_split().