Cross Validation Using K-Fold With Scikit Learn

K‑Fold Cross Validation is a model evaluation technique that divides the dataset into K equal parts (folds) and trains the model multiple times, each time using a different fold as the test set and the remaining folds as training data. This approach provides a more reliable estimate of model performance compared to a single train‑test split.

• Reduces bias and variance in model evaluation
• Widely used to validate machine learning models for better generalisation

Step 1: Import Required Libraries

• load_iris: Loads a sample classification dataset.
• KFold: Defines the K-Fold splitting strategy.
• cross_val_score: Automatically performs training and validation.
• LogisticRegression: Machine learning model for classification task.

Step 2: Load the Dataset

We load the Iris dataset and separate features (X) and target labels (y).

• X contains input features.
• y contains class labels.

Step 3: Initialize the Model

Here we will use Logistic Regression as our base model.

Step 4: Configure K Fold Strategy

• n_splits=5: Data divided into 5 folds.
• shuffle=True: Randomizes data before splitting.
• random_state=42: Ensures reproducibility.

Step 5: Perform Cross Validation

Now we execute cross validation and compute accuracy for each fold.

• Trains the model 5 times.
• Each time, a different fold acts as the test set.
• Returns accuracy scores for each fold.

Step 6: Print Results

• scores: Accuracy for each fold.
• Mean Accuracy: Final performance estimate.

Output:

Download full code from here