5 Best Ways to Split a Dataset for Training and Testing in Python Using Scikit-Learn

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πŸ’‘ Problem Formulation: When developing a machine learning model, it’s essential to split your dataset into a training set and a testing set. This process allows you to train your model on one subset of the data and then validate its performance on an unseen subset. With Scikit-Learn, this can be accomplished in several ways. If you start with a dataset, the goal is to split this data into two parts where, for instance, 80% is used for training and 20% for testing.

Method 1: train_test_split

Scikit-Learn’s train_test_split function is the most common and straightforward approach for splitting a dataset. It provides a fast and efficient method to divide your data with options to shuffle and specify the test data proportion.

Here’s an example:

from sklearn.model_selection import train_test_split
X, y = [[0], [1], [2], [3]], [0, 1, 2, 3]  # Example features and labels
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)

Output:

X_train: [[3], [0], [1]]
X_test: [[2]]
y_train: [3, 0, 1]
y_test: [2]

This code splits the feature set X and the corresponding labels y into training and testing sets. It takes 25% of the data as the test set (indicated by test_size=0.25) and uses a random state for reproducibility.

Method 2: StratifiedShuffleSplit

The StratifiedShuffleSplit function from Scikit-Learn ensures that the proportions of the classes are the same in both the training and testing sets. It is particularly useful when the target variable is a categorical variable with imbalanced class distributions.

Here’s an example:

from sklearn.model_selection import StratifiedShuffleSplit
X, y = [[0], [1], [2], [2]], [0, 1, 0, 1]  # Imbalanced dataset
sss = StratifiedShuffleSplit(n_splits=1, test_size=0.5, random_state=42)
for train_index, test_index in sss.split(X, y):
    X_train, X_test = [X[i] for i in train_index], [X[i] for i in test_index]
    y_train, y_test = [y[i] for i in train_index], [y[i] for i in test_index]

Output:

X_train: [[1], [0]]
X_test: [[2], [2]]
y_train: [1, 0]
y_test: [0, 1]

This snippet creates a StratifiedShuffleSplit object, which is then used to generate indices for splitting the dataset. For each pair of indices, the dataset is divided to preserve the class distribution, ensuring that each split has the same percentage of each target class.

Method 3: KFold Cross-Validation

The KFold cross-validation approach splits the dataset into “k” consecutive folds, without shuffling by default. Each fold is then used as a test set, and the rest as a training set, one by one. It is useful for a thorough evaluation of model performance.

Here’s an example:

from sklearn.model_selection import KFold
kf = KFold(n_splits=2)
X, y = [[0], [1], [2], [3]], [0, 1, 2, 3]
for train_index, test_index in kf.split(X):
    X_train, X_test = [X[i] for i in train_index], [X[i] for i in test_index]
    y_train, y_test = [y[i] for i in train_index], [y[i] for i in test_index]

Output:

First Fold:
X_train: [[2], [3]]
X_test: [[0], [1]]
y_train: [2, 3]
y_test: [0, 1]

Second Fold:
X_train: [[0], [1]]
X_test: [[2], [3]]
y_train: [0, 1]
y_test: [2, 3]

In this code example, KFold is used to split the data into 2 folds. Then, each fold acts once as a validation while the k – 1 remaining folds form the training set. This process repeats k times.

Method 4: GroupKFold

GroupKFold is a variation of KFold that ensures that the same group is not represented in both the training and testing sets. This is particularly important when there are groups in your data that could leak information about other samples, affecting the model’s performance on new data.

Here’s an example:

from sklearn.model_selection import GroupKFold
X, y = [[0], [1], [2], [3]], [0, 1, 2, 3]
groups = [0, 0, 2, 2]  # Group membership for each sample
gkf = GroupKFold(n_splits=2)
for train_index, test_index in gkf.split(X, y, groups):
    X_train, X_test = [X[i] for i in train_index], [X[i] for i in test_index]
    y_train, y_test = [y[i] for i in train_index], [y[i] for i in test_index]

Output:

First Group:
X_train: [[2], [3]]
X_test: [[0], [1]]
y_train: [2, 3]
y_test: [0, 1]

Second Group:
X_train: [[0], [1]]
X_test: [[2], [3]]
y_train: [0, 1]
y_test: [2, 3]

Here, the GroupKFold function is utilized to account for the group structure in the dataset while splitting. By specifying the groups array, we ensure that samples with the same group label are grouped together.

Bonus One-Liner Method 5: ShuffleSplit

This one-liner uses ShuffleSplit, which generates a user-defined number of independent train/test dataset splits. Each split shuffles the data before making the split, which is a flexible tool for creating random subsets of data.

Here’s an example:

from sklearn.model_selection import ShuffleSplit
X, y = [[0], [1], [2], [3]], [0, 1, 2, 3]
ss = ShuffleSplit(n_splits=1, test_size=0.25)
train_index, test_index = next(ss.split(X))
X_train, X_test = [X[i] for i in train_index], [X[i] for i in test_index]
y_train, y_test = [y[i] for i in train_index], [y[i] for i in test_index]

Output:

X_train: [[1], [0], [2]]
X_test: [[3]]
y_train: [1, 0, 2]
y_test: [3]

By creating a ShuffleSplit object and calling split, we quickly generate random train/test indices. This is a simple way to get a single split of the data into training and test sets.

Summary/Discussion

  • Method 1: train_test_split. This method is simple and widely used, perfect for a quick split without complex stratification or group constraints. It might not preserve class distribution or group structures in the splits.
  • Method 2: StratifiedShuffleSplit. It’s suited for datasets with imbalanced class distributions, ensuring that both training and testing sets have proportionate class representation. Its limitation lies in computational complexity with very large datasets.
  • Method 3: KFold Cross-Validation. Ideal for validating model performance comprehensively, as it uses every part of the dataset for both training and validation. However, it does not shuffle data by default and does not maintain class proportions within each fold.
  • Method 4: GroupKFold. Excellent when dealing with groups of data that should not mix between training and test sets. It can be less intuitive to implement compared to other methods and may not be suitable when the number of groups is small.
  • Bonus Method 5: ShuffleSplit. Offers maximum flexibility in terms of the number of splits and the size of test sets. While it provides random subsets, it doesn’t guarantee stratified or grouped splits.