---

name: ds-model-selection compatibility: opencode completeness: 95 content-types:

• code
• guidance
• do-dont
• examples description: '"Provides Compares and selects models using AIC, BIC, validation curves learning curves, and model comparison techniques"' license: MIT maturity: stable metadata: domain: coding output-format: code related-skills: ds-bias-variance-tradeoff, ds-cross-validation, ds-ensemble-methods ds-hyperparameter-tuning ds-regression-evaluation role: implementation scope: implementation triggers: model selection, AIC, BIC, validation curves, learning curves, model comparison 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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Model Selection

Comprehensive guide to model selection in machine learning and data science workflows.

When to Use This Skill

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

When NOT to Use This Skill

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

Model Selection 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 Model Selection

import numpy as np
import pandas as pd
from typing import Dict, Any
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.linear_model import LinearRegression, Ridge
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.datasets import make_regression

def basic_model_selection() -> Dict[str, Any]:
    """
    Demonstrates basic model selection by comparing Linear and Ridge regression.
    Follows DRY principle by centralizing evaluation logic.
    """
    X, y = make_regression(n_samples=500, n_features=10, noise=0.1, random_state=42)
    df = pd.DataFrame(X, columns=[f'feature_{i}' for i in range(X.shape[1])])
    df['target'] = y

    X_train, X_test, y_train, y_test = train_test_split(
        df.drop('target', axis=1), df['target'], test_size=0.2, random_state=42
    )

    models: Dict[str, Any] = {
        'Linear Regression': LinearRegression()
        'Ridge Regression': Ridge(alpha=1.0)
    }

    results: Dict[str, Any] = {}
    for name, model in models.items():
        cv_scores = cross_val_score(model, X_train, y_train, cv=5, scoring='r2')
        model.fit(X_train, y_train)
        y_pred = model.predict(X_test)
        r2 = r2_score(y_test, y_pred)
        mse = mean_squared_error(y_test, y_pred)

        results[name] = {
            'cv_r2_mean': float(np.mean(cv_scores))
            'cv_r2_std': float(np.std(cv_scores))
            'test_r2': float(r2)
            'test_mse': float(mse)
        }
        print(f"{name} - CV R2: {np.mean(cv_scores):.4f} (+/- {np.std(cv_scores):.4f}), Test R2: {r2:.4f}")

    return results

if __name__ == "__main__":
    basic_model_selection()

Pattern 2: Production-Ready Model Selection

import logging
import numpy as np
import pandas as pd
from typing import Any, Dict, List
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score, classification_report
from sklearn.datasets import make_classification

logger = logging.getLogger(__name__)

class ModelSelector:
    """Production implementation of Model Selection following SOLID principles."""
    
    def __init__(self, cv_folds: int = 5, random_state: int = 42) -> None:
        self.cv_folds = cv_folds
        self.random_state = random_state
        self.selected_model: Any = None
        self.results: Dict[str, Any] = {}
    
    def _prepare_data(self, data: pd.DataFrame, target_col: str) -> tuple:
        if target_col not in data.columns:
            raise ValueError(f"Target column '{target_col}' not found in data")
        X = data.drop(columns=[target_col])
        y = data[target_col]
        if X.isnull().any().any() or y.isnull().any():
            logger.warning("Data contains missing values. Dropping rows with NaN.")
            X, y = X.dropna(), y.dropna()
        return train_test_split(X, y, test_size=0.2, random_state=self.random_state, stratify=y)
    
    def execute(self, data: pd.DataFrame, target_col: str = 'target') -> Dict[str, Any]:
        """Execute Model Selection on data"""
        try:
            X_train, X_test, y_train, y_test = self._prepare_data(data, target_col)
            models = {
                'Logistic Regression': LogisticRegression(max_iter=1000, random_state=self.random_state)
                'Random Forest': RandomForestClassifier(n_estimators=100, random_state=self.random_state)
                'SVM': SVC(kernel='rbf', probability=True, random_state=self.random_state)
            }

            best_score = -np.inf
            best_name = None

            for name, model in models.items():
                cv_scores = cross_val_score(model, X_train, y_train, cv=self.cv_folds, scoring='accuracy')
                model.fit(X_train, y_train)
                y_pred = model.predict(X_test)
                acc = accuracy_score(y_test, y_pred)
                report = classification_report(y_test, y_pred, output_dict=True)

                self.results[name] = {
                    'cv_accuracy_mean': float(np.mean(cv_scores))
                    'cv_accuracy_std': float(np.std(cv_scores))
                    'test_accuracy': float(acc)
                    'classification_report': report
                }

                if np.mean(cv_scores) > best_score:
                    best_score = np.mean(cv_scores)
                    best_name = name
                    self.selected_model = model

            logger.info(f"Selected model: {best_name} with CV accuracy: {best_score:.4f}")
            return self.results
        except Exception as e:
            logger.error(f"Model selection failed: {str(e)}")
            raise

if __name__ == "__main__":
    logging.basicConfig(level=logging.INFO)
    X, y = make_classification(n_samples=1000, n_features=20, n_classes=2, random_state=42)
    df = pd.DataFrame(X, columns=[f'feat_{i}' for i in range(X.shape[1])])
    df['target'] = y
    selector = ModelSelector(cv_folds=5)
    results = selector.execute(df, target_col='target')
    print(f"Best model selected: {selector.selected_model}")

BAD vs GOOD Example

# BAD: Hardcoded values, no error handling, mixes data prep and evaluation
def bad_selection(data):
    model = LinearRegression()
    model.fit(data[:, :-1], data[:, -1])
    return model.score(data[:, :-1], data[:, -1])

# GOOD: Type hints, validation, separation of concerns, proper metrics
def good_selection(X: np.ndarray, y: np.ndarray) -> float:
    if X.shape[0] != y.shape[0]:
        raise ValueError("X and y must have the same number of samples")
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
    model = LinearRegression()
    model.fit(X_train, y_train)
    return float(r2_score(y_test, model.predict(X_test)))

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 Model Evaluation
• Model Selection — Scikit-learn docs
• Cross-Validation (Kaggle Learn)
• AIC vs BIC (NIST Handbook)
• Model Selection and Regularization (ESL Chapter 7)