📊 Model Evaluation & Testing
Rigorous model evaluation is critical for building reliable ML systems. Learn comprehensive strategies for offline validation, online testing, metrics selection, and statistical testing to ensure your models perform well in production.
Offline Evaluation
Historical data validation with cross-validation and metrics
Online Testing
A/B testing and real-world performance validation
Testing Strategy
Statistical significance and bias detection
Evaluation Frameworks
Offline
Validation
Online
Testing
Metrics
Selection
Testing
Strategies
Evaluation Frameworks
Offline Evaluation
PythonKey Concepts:
- • Cross-validation strategies
- • Train/validation/test splits
- • Metric selection and interpretation
- • Statistical significance testing
class OfflineEvaluationFramework:
"""
Comprehensive offline evaluation for ML models
Following industry best practices for model validation
"""
def __init__(self, model, data, task_type='classification'):
self.model = model
self.data = data
self.task_type = task_type
self.results = {}
def time_series_split(self, data, n_splits=5):
"""Time-aware splitting for temporal data"""
from sklearn.model_selection import TimeSeriesSplit
tscv = TimeSeriesSplit(n_splits=n_splits)
splits = []
for train_idx, test_idx in tscv.split(data):
train_data = data.iloc[train_idx]
test_data = data.iloc[test_idx]
splits.append((train_data, test_data))
return splits
def stratified_evaluation(self, X, y, cv_folds=5):
"""Stratified cross-validation for imbalanced datasets"""
from sklearn.model_selection import StratifiedKFold
from sklearn.metrics import classification_report, confusion_matrix
skf = StratifiedKFold(n_splits=cv_folds, shuffle=True, random_state=42)
fold_scores = []
for fold, (train_idx, val_idx) in enumerate(skf.split(X, y)):
X_train, X_val = X[train_idx], X[val_idx]
y_train, y_val = y[train_idx], y[val_idx]
# Train model
self.model.fit(X_train, y_train)
# Predict and evaluate
y_pred = self.model.predict(X_val)
y_proba = self.model.predict_proba(X_val)[:, 1] if hasattr(self.model, 'predict_proba') else None
fold_result = {
'fold': fold,
'accuracy': accuracy_score(y_val, y_pred),
'precision': precision_score(y_val, y_pred, average='weighted'),
'recall': recall_score(y_val, y_pred, average='weighted'),
'f1': f1_score(y_val, y_pred, average='weighted'),
'auc': roc_auc_score(y_val, y_proba) if y_proba is not None else None,
'confusion_matrix': confusion_matrix(y_val, y_pred).tolist()
}
fold_scores.append(fold_result)
return fold_scores
def regression_evaluation(self, X, y, cv_folds=5):
"""Comprehensive regression model evaluation"""
from sklearn.model_selection import KFold
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
kf = KFold(n_splits=cv_folds, shuffle=True, random_state=42)
fold_scores = []
for fold, (train_idx, val_idx) in enumerate(kf.split(X)):
X_train, X_val = X[train_idx], X[val_idx]
y_train, y_val = y[train_idx], y[val_idx]
self.model.fit(X_train, y_train)
y_pred = self.model.predict(X_val)
fold_result = {
'fold': fold,
'rmse': np.sqrt(mean_squared_error(y_val, y_pred)),
'mae': mean_absolute_error(y_val, y_pred),
'r2': r2_score(y_val, y_pred),
'mape': np.mean(np.abs((y_val - y_pred) / y_val)) * 100,
'residuals': (y_val - y_pred).tolist()
}
fold_scores.append(fold_result)
return fold_scores
def statistical_significance_test(self, model1_scores, model2_scores, alpha=0.05):
"""Compare two models using statistical tests"""
from scipy import stats
# Paired t-test for model comparison
t_stat, p_value = stats.ttest_rel(model1_scores, model2_scores)
result = {
'test': 'Paired t-test',
't_statistic': t_stat,
'p_value': p_value,
'significant': p_value < alpha,
'alpha': alpha,
'interpretation': f"Model 1 {'significantly' if p_value < alpha else 'not significantly'} different from Model 2"
}
return resultEvaluation Strategy Selection
| Task Type | Primary Metrics | Validation Strategy | Special Considerations |
|---|---|---|---|
| Classification | Accuracy, F1, AUC-ROC | Stratified K-Fold | Class imbalance, fairness metrics |
| Regression | RMSE, MAE, R² | K-Fold CV | Heteroscedasticity, outliers |
| Time Series | MAPE, SMAPE, MAE | Time Series Split | Temporal leakage, seasonality |
| Ranking | NDCG, MAP, MRR | Group-based CV | Position bias, relevance labels |
| NLP | BLEU, ROUGE, BERTScore | Domain-aware splits | Human evaluation, bias detection |
Evaluation Best Practices
🎯 Offline Validation
- • Use time-aware splits for temporal data
- • Ensure representative test sets
- • Test multiple random seeds for stability
- • Validate on holdout data only once
🔄 Online Testing
- • Start with shadow mode deployment
- • Use statistical significance testing
- • Monitor business metrics alongside ML metrics
- • Implement automatic rollback triggers
📊 Metrics Selection
- • Align metrics with business objectives
- • Consider class imbalance effects
- • Include fairness and bias metrics
- • Track prediction uncertainty
🧪 Testing Strategy
- • Implement automated testing pipelines
- • Test data quality and schema compliance
- • Validate model invariants and assumptions
- • Monitor performance and latency requirements
Common Evaluation Pitfalls
❌ Data Leakage
Future information bleeding into training data. Use proper temporal splits and feature engineering pipelines.
⚠️ Selection Bias
Non-representative test sets. Ensure test data reflects production distribution and use cases.
🔄 Metric Gaming
Optimizing for metrics that don't reflect business value. Always validate with business KPIs.
📊 Multiple Testing
Running too many experiments without correction. Apply Bonferroni or FDR correction for multiple comparisons.
Essential Technologies for Model Evaluation
🔄
MLflow→
Experiment tracking and model validation workflows
📊
Weights & Biases→
Advanced experiment tracking and model comparison
🧠
TensorFlow→
Model development with TensorFlow Extended (TFX) for validation
🔥
PyTorch→
Model development and evaluation with PyTorch ecosystem
📈
Prometheus→
Model performance monitoring and alerting
📊
Grafana→
Visualization and dashboards for model metrics
📝 Model Evaluation Mastery Check
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