Scikit-Learn cross_val_predict() to Evaluate Models

cross_val_predict() is a useful function in scikit-learn that allows you to visualize and analyze the output of cross-validation predictions.

It provides a way to generate prediction results for each data point using cross-validation, helping to better understand model performance and potential overfitting.

The key hyperparameters include the estimator (the model to be used), X and y (features and target variable), and cv (the number of cross-validation splits).

This function is appropriate for both classification and regression problems.

from sklearn.datasets import make_classification
from sklearn.model_selection import cross_val_predict
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

# generate binary classification dataset
X, y = make_classification(n_samples=100, n_features=5, n_classes=2, random_state=1)

# create model
model = RandomForestClassifier()

# use cross_val_predict
y_pred = cross_val_predict(model, X, y, cv=5)

# evaluate predictions
acc = accuracy_score(y, y_pred)
print('Accuracy: %.3f' % acc)

Running the example gives an output like:

Accuracy: 0.950

The steps are as follows:

1. Generate a synthetic binary classification dataset using make_classification(). This creates a dataset with a specified number of samples (n_samples), features (n_features), and a fixed random seed (random_state) for reproducibility.

2. Instantiate a RandomForestClassifier model with default hyperparameters.

3. Use cross_val_predict() to generate cross-validated predictions. This function performs cross-validation on the model and returns the predictions for each data point.

4. Evaluate the performance of the predictions using the accuracy score metric. The accuracy is calculated by comparing the cross-validated predictions (y_pred) to the actual values (y).