---

name: ds-cross-validation compatibility: opencode completeness: 95 content-types:

• code
• guidance
• do-dont
• examples description: '"Implements k-fold cross-validation, stratified cross-validation, time-series cross-validation, and model validation strategies"' license: MIT maturity: stable metadata: domain: coding output-format: code related-skills: ds-bias-variance-tradeoff, ds-classification-metrics, ds-hyperparameter-tuning ds-model-selection ds-regression-evaluation role: implementation scope: implementation triggers: cross-validation, k-fold, stratified cross-validation, time-series cross-validation validation archetypes:
  • tactical
  • generation anti_triggers:
  • brainstorming
  • vague ideation
  • code golf
  • over-engineering response_profile: verbosity: low directive_strength: high abstraction_level: operational version: "1.0.0"

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Cross-Validation

Comprehensive guide to cross-validation in machine learning and data science workflows.

When to Use This Skill

• Solving real-world model evaluation & selection problems
• Building machine learning pipelines with cross-validation
• Implementing best practices for cross-validation
• Optimizing model performance using cross-validation techniques
• Learning industry-standard approaches to cross-validation

When NOT to Use This Skill

• When using pre-built libraries without understanding underlying concepts
• For toy problems that don't require cross-validation rigor
• When domain expertise in specific problem requires different approach
• If your problem doesn't require the complexity this skill provides

Purpose and Key Concepts

Cross-Validation is a critical component of the machine learning workflow. This skill covers:

1. Theoretical foundations — Mathematical principles and statistical concepts
2. Practical implementation — Working code examples and patterns
3. Common pitfalls — Mistakes to avoid and how to recover from them
4. Best practices — Industry-standard approaches and optimization techniques

Core Workflow

1. Understand the problem — Clearly define what you're solving for
2. Select approach — Choose the right technique for your data and constraints
3. Implement solution — Write clean, tested code following best practices
4. Validate results — Verify your implementation with tests and validation
5. Optimize performance — Improve efficiency and accuracy incrementally

Implementation Patterns

Pattern 1: Basic Cross-Validation

import numpy as np
import pandas as pd
from sklearn.model_selection import KFold, cross_val_score
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import make_classification
from sklearn.metrics import accuracy_score, classification_report

def basic_kfold_cv(X: np.ndarray, y: np.ndarray, n_splits: int = 5) -> dict:
    """Perform basic k-fold cross-validation and return metrics."""
    if X.shape[0] != y.shape[0]:
        raise ValueError("X and y must have the same number of samples.")
    
    kf = KFold(n_splits=n_splits, shuffle=True, random_state=42)
    model = RandomForestClassifier(n_estimators=100, random_state=42)
    
    scores = cross_val_score(model, X, y, cv=kf, scoring='accuracy')
    
    # Generate predictions for the first fold to demonstrate usage
    train_idx, test_idx = next(kf.split(X))
    model.fit(X[train_idx], y[train_idx])
    y_pred = model.predict(X[test_idx])
    
    return {
        'mean_accuracy': float(np.mean(scores))
        'std_accuracy': float(np.std(scores))
        'fold_scores': scores.tolist()
        'first_fold_report': classification_report(y[test_idx], y_pred, output_dict=True)
    }

# Example usage with synthetic data
if __name__ == "__main__":
    X, y = make_classification(n_samples=500, n_features=10, n_classes=2, random_state=42)
    results = basic_kfold_cv(X, y, n_splits=5)
    print(f"Mean CV Accuracy: {results['mean_accuracy']:.4f} (+/- {results['std_accuracy']:.4f})")

Pattern 2: Production-Ready Cross-Validation

import logging
import numpy as np
import pandas as pd
from typing import Any, Dict, List, Optional
from sklearn.model_selection import StratifiedKFold, TimeSeriesSplit, cross_validate
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
from sklearn.datasets import load_breast_cancer

logger = logging.getLogger(__name__)

class ProductionCrossValidator:
    """Production-grade cross-validation wrapper with logging and error handling."""
    
    def __init__(self, cv_strategy: str = 'stratified', n_splits: int = 5, random_state: int = 42):
        self.cv_strategy = cv_strategy
        self.n_splits = n_splits
        self.random_state = random_state
        self.logger = logging.getLogger(self.__class__.__name__)
        
    def _get_cv_splitter(self, y: np.ndarray) -> Any:
        if self.cv_strategy == 'stratified':
            return StratifiedKFold(n_splits=self.n_splits, shuffle=True, random_state=self.random_state)
        elif self.cv_strategy == 'timeseries':
            return TimeSeriesSplit(n_splits=self.n_splits)
        else:
            raise ValueError(f"Unsupported CV strategy: {self.cv_strategy}")
            
    def execute(self, X: pd.DataFrame, y: pd.Series, model: Any = None) -> Dict[str, Any]:
        """Execute cross-validation on provided data and model."""
        try:
            if X is None or y is None:
                raise ValueError("Input data cannot be None")
            if X.shape[0] != y.shape[0]:
                raise ValueError("X and y must have matching sample counts")
                
            if model is None:
                model = Pipeline([
                    ('scaler', StandardScaler())
                    ('classifier', GradientBoostingClassifier(n_estimators=100, random_state=self.random_state))
                ])
                
            cv_splitter = self._get_cv_splitter(y.values)
            scoring_metrics = ['accuracy', 'precision_weighted', 'recall_weighted', 'f1_weighted']
            
            cv_results = cross_validate(
                model, X, y, cv=cv_splitter, 
                scoring=scoring_metrics, return_train_score=True, n_jobs=-1
            )
            
            self.logger.info(f"CV completed with strategy: {self.cv_strategy}")
            return {
                'status': 'success'
                'cv_strategy': self.cv_strategy
                'n_splits': self.n_splits
                'test_scores': {k: float(np.mean(v)) for k, v in cv_results.items() if k.startswith('test_')}
                'train_scores': {k: float(np.mean(v)) for k, v in cv_results.items() if k.startswith('train_')}
                'fit_times': float(np.mean(cv_results['fit_time']))
                'score_times': float(np.mean(cv_results['score_time']))
            }
        except Exception as e:
            self.logger.error(f"Cross-validation failed: {str(e)}")
            return {'status': 'error', 'message': str(e)}

# Example usage
if __name__ == "__main__":
    logging.basicConfig(level=logging.INFO)
    data = load_breast_cancer()
    X, y = pd.DataFrame(data.data, columns=data.feature_names), pd.Series(data.target)
    validator = ProductionCrossValidator(cv_strategy='stratified', n_splits=5)
    results = validator.execute(X, y)
    print(f"Test F1 Score: {results['test_scores']['f1_weighted']:.4f}")

Best Practices

• ✅ Always validate your implementation on test data
• ✅ Document your assumptions and methodology
• ✅ Use version control for reproducibility
• ✅ Monitor performance metrics in production
• ✅ Periodically review and update your approach
• ✅ Test with edge cases and outliers
• ✅ Log all significant operations for debugging

Common Pitfalls

| Pitfall | Problem | Solution | |

---

---

Constraints

MUST DO

• Validate all data preprocessing steps are fit-only on training data, never on validation or test sets
• Implement reproducible pipelines with fixed random seeds and deterministic operations where possible
• Report model performance with confidence intervals via bootstrapping or cross-validation across multiple runs
• Log all experiments with parameters, metrics, and artifacts using MLflow or equivalent tracking system

MUST NOT DO

• Do not evaluate a model on the same data used for training — always hold out a proper test set
• Avoid overfitting to the validation set by limiting hyperparameter search iterations
• Never use features that can only be computed at inference time (look-ahead bias)
• Do not report single-run accuracy without statistical significance testing or error bars

Live References

Authoritative documentation links for this skill's domain. The model follows markdown links at load time to resolve external references and inline content.

• Scikit-learn Cross Validation
• Model Selection / Cross-Validation (scikit-learn docs)
• Stratified K-Fold — Scikit-learn
• Cross-Validation (Kaggle Learn)
• Optuna Cross-Validation Integration