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name: ds-regression-evaluation compatibility: opencode completeness: 95 content-types:

• code
• guidance
• do-dont
• examples description: '"Evaluates regression models using MSE, RMSE, MAE, MAPE, R-squared and other metrics for assessing predictive accuracy"' license: MIT maturity: stable metadata: domain: coding output-format: code related-skills: ds-cross-validation, ds-linear-regression, ds-metrics-and-kpis ds-time-series-forecasting ds-model-selection role: implementation scope: implementation triggers: regression evaluation, MSE, RMSE, MAE, R-squared, regression metrics how do i evaluate 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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Regression Evaluation

Comprehensive guide to regression evaluation in machine learning and data science workflows.

When to Use This Skill

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

When NOT to Use This Skill

• When using pre-built libraries without understanding underlying concepts
• For toy problems that don't require regression evaluation 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

Regression Evaluation 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 Regression Evaluation

import numpy as np
import pandas as pd
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
from sklearn.model_selection import train_test_split
from typing import Dict, Tuple

def basic_regression_evaluation(X: np.ndarray, y: np.ndarray) -> Dict[str, float]:
    """Train a linear regression model and compute core evaluation metrics."""
    if X.shape[0] != y.shape[0]:
        raise ValueError("X and y must have the same number of samples")
    if X.ndim != 2 or y.ndim != 1:
        raise ValueError("X must be 2D array and y must be 1D array")
        
    test_ratio: float = 0.2
    X_train, X_test, y_train, y_test = train_test_split(
        X, y, test_size=test_ratio, random_state=42
    )
    
    model = LinearRegression()
    model.fit(X_train, y_train)
    y_pred = model.predict(X_test)
    
    metrics: Dict[str, float] = {
        'mse': float(mean_squared_error(y_test, y_pred))
        'rmse': float(np.sqrt(mean_squared_error(y_test, y_pred)))
        'mae': float(mean_absolute_error(y_test, y_pred))
        'r2': float(r2_score(y_test, y_pred))
    }
    return metrics

Pattern 2: Production-Ready Regression Evaluation

import logging
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
from typing import Any, Dict, Optional

logger = logging.getLogger(__name__)

class RegressionEvaluator:
    """Production-grade regression evaluation with comprehensive metrics and logging."""
    
    def __init__(self, test_size: float = 0.2, random_state: int = 42) -> None:
        self.test_size: float = test_size
        self.random_state: int = random_state
        self.model: Optional[RandomForestRegressor] = None
        
    def evaluate(self, X: pd.DataFrame, y: pd.Series) -> Dict[str, Any]:
        """Evaluate regression performance on provided data."""
        if X.empty or y.empty:
            raise ValueError("Input data cannot be empty")
        if X.shape[0] != y.shape[0]:
            raise ValueError("X and y must have matching row counts")
            
        logger.info("Starting regression evaluation pipeline")
        
        X_train, X_test, y_train, y_test = self._split_data(X, y)
        self.model = RandomForestRegressor(n_estimators=100, random_state=self.random_state)
        self.model.fit(X_train, y_train)
        y_pred = self.model.predict(X_test)
        
        metrics: Dict[str, Any] = {
            'mse': float(mean_squared_error(y_test, y_pred))
            'rmse': float(np.sqrt(mean_squared_error(y_test, y_pred)))
            'mae': float(mean_absolute_error(y_test, y_pred))
            'r2': float(r2_score(y_test, y_pred))
            'mape': float(np.mean(np.abs((y_test - y_pred) / y_test)) * 100)
        }
        logger.info(f"Evaluation complete. R2: {metrics['r2']:.4f}")
        return metrics
        
    def _split_data(self, X: pd.DataFrame, y: pd.Series) -> Tuple[pd.DataFrame, pd.DataFrame, pd.Series, pd.Series]:
        """Split data into training and testing sets."""
        from sklearn.model_selection import train_test_split
        return train_test_split(X, y, test_size=self.test_size, random_state=self.random_state)

Pattern 3: BAD vs GOOD Evaluation Practices

BAD: Computing metrics on training data without validation, using hardcoded values, and ignoring dimension checks

import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score

def bad_evaluation(X: pd.DataFrame, y: pd.Series) -> float:
    """Bad practice: no test split, hardcoded parameters, missing validation."""
    model = LinearRegression()
    model.fit(X, y)
    y_pred = model.predict(X)
    score = r2_score(y, y_pred)
    return score  # Returns overfit metric, no error handling, no type hints

GOOD: Proper train/test split with comprehensive metric reporting, explicit validation, and type hints

import numpy as np
import pandas as pd
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
from sklearn.model_selection import train_test_split
from typing import Dict, Tuple

def good_evaluation(X: pd.DataFrame, y: pd.Series) -> Dict[str, float]:
    """Good practice: proper split, validation, type hints, and multiple metrics."""
    if X.shape[0] != y.shape[0]:
        raise ValueError("Dimension mismatch between features and targets")
    if X.isnull().any().any() or y.isnull().any():
        raise ValueError("Input data contains missing values")
        
    test_ratio: float = 0.2
    X_train, X_test, y_train, y_test = train_test_split(
        X, y, test_size=test_ratio, random_state=42
    )
    
    model = LinearRegression()
    model.fit(X_train, y_train)
    y_pred = model.predict(X_test)
    
    metrics: Dict[str, float] = {
        'r2': float(r2_score(y_test, y_pred))
        'rmse': float(np.sqrt(mean_squared_error(y_test, y_pred)))
        'mae': float(mean_absolute_error(y_test, y_pred))
    }
    return metrics

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
• ✅ Adhere to SOLID principles to keep evaluation logic modular, testable, and maintainable

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 Model Evaluation — Regression Metrics
• Mean Squared Error, R² — Scikit-learn docs
• Residual Analysis (NIST Handbook)
• Regression Diagnostics — Wikipedia
• Cross-Validation for Regression (Kaggle Learn)