Evaluating sklearn model using KFold cross validation in python

When building machine learning models in python, it is important to evaluate the performance of the model to ensure its effectiveness. One common method for evaluating a model is KFold cross validation, which is available in the scikit-learn (sklearn) library.

KFold cross validation involves splitting the dataset into k-folds, training the model on k-1 folds, and testing it on the remaining fold. This process is repeated k times, with each fold serving as the test set exactly once. This allows for a more robust evaluation of the model’s performance, as it reduces the impact of variability in a single train-test split.

In python, the sklearn library provides a KFold class which can be used to implement KFold cross validation. The following is an example of how to use KFold cross validation to evaluate a model using scikit-learn in python:

      
        import numpy as np
        from sklearn.model_selection import KFold
        from sklearn.model_selection import cross_val_score
        from sklearn.linear_model import LogisticRegression
        from sklearn.datasets import load_iris
        
        # Load the iris dataset
        iris = load_iris()
        X = iris.data
        y = iris.target
        
        # Create a Logistic Regression model
        model = LogisticRegression()
        
        # Initialize the KFold cross validation
        kfold = KFold(n_splits=5, shuffle=True, random_state=42)
        
        # Evaluate the model using KFold cross validation
        results = cross_val_score(model, X, y, cv=kfold)
        
        # Print the mean and standard deviation of the cross validation results
        print("Mean Accuracy:", np.mean(results))
        print("Standard Deviation:", np.std(results))
      
    

In the above example, we first load the iris dataset using sklearn’s datasets module. We then create a logistic regression model and initialize a KFold object with 5 folds. Finally, we use the cross_val_score function to evaluate the model using KFold cross validation, and print the mean accuracy and standard deviation of the cross validation results.

KFold cross validation is a powerful tool for evaluating the performance of machine learning models in python. By using KFold cross validation, we can obtain a more robust estimation of the model’s performance, which can be important for making informed decisions about the model’s effectiveness and generalizability to new data.