AgentSkillsCN

scikit-learn-best-practices

Python 中 scikit-learn 机器学习、模型开发、评估与部署的最佳实践

SKILL.md
--- frontmatter
name: scikit-learn-best-practices
description: Best practices for scikit-learn machine learning, model development, evaluation, and deployment in Python

Scikit-learn Best Practices

Expert guidelines for scikit-learn development, focusing on machine learning workflows, model development, evaluation, and best practices.

Code Style and Structure

  • •Write concise, technical responses with accurate Python examples
  • •Prioritize reproducibility in machine learning workflows
  • •Use functional programming for data pipelines
  • •Use object-oriented programming for custom estimators
  • •Prefer vectorized operations over explicit loops
  • •Follow PEP 8 style guidelines

Machine Learning Workflow

Data Preparation

  • •Always split data before any preprocessing: train/validation/test
  • •Use train_test_split() with random_state for reproducibility
  • •Stratify splits for imbalanced classification: stratify=y
  • •Keep test set completely separate until final evaluation

Feature Engineering

  • •Scale features appropriately for distance-based algorithms
  • •Use StandardScaler for normally distributed features
  • •Use MinMaxScaler for bounded features
  • •Use RobustScaler for data with outliers
  • •Encode categorical variables: OneHotEncoder, OrdinalEncoder, LabelEncoder
  • •Handle missing values: SimpleImputer, KNNImputer

Pipelines

  • •Always use Pipeline to chain preprocessing and modeling
  • •Prevents data leakage by fitting transformers only on training data
  • •Makes code cleaner and more reproducible
  • •Enables easy deployment and serialization
python
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestClassifier

pipeline = Pipeline([
    ('scaler', StandardScaler()),
    ('classifier', RandomForestClassifier(random_state=42))
])

Column Transformers

  • •Use ColumnTransformer for different preprocessing per feature type
  • •Combine numeric and categorical preprocessing in single pipeline

Model Selection and Tuning

Cross-Validation

  • •Use cross-validation for reliable performance estimates
  • •cross_val_score() for quick evaluation
  • •cross_validate() for multiple metrics
  • •Use appropriate CV strategy:
    • •KFold for regression
    • •StratifiedKFold for classification
    • •TimeSeriesSplit for temporal data
    • •GroupKFold for grouped data

Hyperparameter Tuning

  • •Use GridSearchCV for exhaustive search
  • •Use RandomizedSearchCV for large parameter spaces
  • •Always tune on training/validation data, never test data
  • •Set n_jobs=-1 for parallel processing

Model Evaluation

Classification Metrics

  • •Use appropriate metrics for your problem:
    • •accuracy_score for balanced classes
    • •precision_score, recall_score, f1_score for imbalanced
    • •roc_auc_score for ranking ability
  • •Use classification_report() for comprehensive overview
  • •Examine confusion_matrix() for error analysis

Regression Metrics

  • •mean_squared_error (MSE) for general use
  • •mean_absolute_error (MAE) for interpretability
  • •r2_score for explained variance

Evaluation Best Practices

  • •Report confidence intervals, not just point estimates
  • •Use multiple metrics to understand model behavior
  • •Compare against meaningful baselines
  • •Evaluate on held-out test set only once, at the end

Handling Imbalanced Data

  • •Use stratified splitting and cross-validation
  • •Consider class weights: class_weight='balanced'
  • •Use appropriate metrics (F1, AUC-PR, not accuracy)
  • •Adjust decision threshold based on business needs

Feature Selection

  • •Use SelectKBest with statistical tests
  • •Use RFE (Recursive Feature Elimination)
  • •Use model-based selection: SelectFromModel
  • •Examine feature importances from tree-based models

Model Persistence

  • •Use joblib for saving and loading models
  • •Save entire pipelines, not just models
  • •Version control model artifacts
  • •Document model metadata

Performance Optimization

  • •Use n_jobs=-1 for parallel processing where available
  • •Consider warm_start=True for iterative training
  • •Use sparse matrices for high-dimensional sparse data
  • •Consider incremental learning with partial_fit() for large data

Key Conventions

  • •Import from submodules: from sklearn.ensemble import RandomForestClassifier
  • •Set random_state for reproducibility
  • •Use pipelines to prevent data leakage
  • •Document model choices and hyperparameters