sklearn-model-trainer

name	sklearn-model-trainer
description	Scikit-learn model training skill with cross-validation, hyperparameter tuning, pipeline construction, and model serialization. Enables automated ML model development using scikit-learn's comprehensive toolkit.
allowed-tools	Read, Grep, Write, Bash, Edit, Glob
graph	{"domains":["domain:data-science"],"specializations":["specialization:data-science-ml"],"skillAreas":["skill-area:machine-learning-frameworks","skill-area:model-evaluation"],"roles":["role:ml-engineer","role:data-scientist"],"workflows":["workflow:ml-model-lifecycle"]}

Scikit-learn Model Trainer

Train machine learning models using scikit-learn with cross-validation, hyperparameter tuning, and pipeline construction.

Overview

This skill provides comprehensive capabilities for training machine learning models using scikit-learn. It supports the full model development workflow from data preprocessing through model training, evaluation, and serialization.

Capabilities

Model Training

• Train classification models (LogisticRegression, RandomForest, SVM, etc.)
• Train regression models (LinearRegression, GradientBoosting, etc.)
• Train clustering models (KMeans, DBSCAN, etc.)
• Support for ensemble methods (VotingClassifier, Stacking, etc.)

Cross-Validation

• K-fold cross-validation
• Stratified K-fold for imbalanced datasets
• Time series split for temporal data
• Leave-one-out and leave-p-out validation
• Custom cross-validation strategies

Hyperparameter Tuning

• GridSearchCV for exhaustive search
• RandomizedSearchCV for random sampling
• Halving search strategies for efficiency
• Custom scoring functions
• Multi-metric evaluation

Pipeline Construction

• Feature preprocessing pipelines
• Column transformers for heterogeneous data
• Feature selection integration
• Composite pipelines with caching

Model Serialization

• Save models with joblib (recommended)
• Pickle serialization
• ONNX export for interoperability
• Model versioning support

Prerequisites

Installation

pip install scikit-learn>=1.0.0 joblib pandas numpy

Optional Dependencies

# For ONNX export
pip install skl2onnx onnxruntime

# For additional preprocessing
pip install category_encoders imbalanced-learn

Usage Patterns

Basic Model Training

from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.metrics import classification_report
import joblib

# Split data
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42, stratify=y
)

# Train model
model = RandomForestClassifier(
    n_estimators=100,
    max_depth=10,
    random_state=42
)
model.fit(X_train, y_train)

# Cross-validation
cv_scores = cross_val_score(model, X_train, y_train, cv=5, scoring='accuracy')
print(f"CV Accuracy: {cv_scores.mean():.3f} (+/- {cv_scores.std() * 2:.3f})")

# Evaluate
y_pred = model.predict(X_test)
print(classification_report(y_test, y_pred))

# Save model
joblib.dump(model, 'model.joblib')

Pipeline with Preprocessing

from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.impute import SimpleImputer
from sklearn.ensemble import GradientBoostingClassifier

# Define preprocessing
numeric_features = ['age', 'income', 'score']
categorical_features = ['category', 'region']

numeric_transformer = Pipeline(steps=[
    ('imputer', SimpleImputer(strategy='median')),
    ('scaler', StandardScaler())
])

categorical_transformer = Pipeline(steps=[
    ('imputer', SimpleImputer(strategy='constant', fill_value='missing')),
    ('onehot', OneHotEncoder(handle_unknown='ignore'))
])

preprocessor = ColumnTransformer(
    transformers=[
        ('num', numeric_transformer, numeric_features),
        ('cat', categorical_transformer, categorical_features)
    ]
)

# Create full pipeline
pipeline = Pipeline(steps=[
    ('preprocessor', preprocessor),
    ('classifier', GradientBoostingClassifier())
])

# Train
pipeline.fit(X_train, y_train)

Hyperparameter Tuning with GridSearchCV

from sklearn.model_selection import GridSearchCV

# Define parameter grid
param_grid = {
    'classifier__n_estimators': [50, 100, 200],
    'classifier__max_depth': [3, 5, 10, None],
    'classifier__learning_rate': [0.01, 0.1, 0.2]
}

# Grid search
grid_search = GridSearchCV(
    pipeline,
    param_grid,
    cv=5,
    scoring='f1_weighted',
    n_jobs=-1,
    verbose=2
)

grid_search.fit(X_train, y_train)

print(f"Best parameters: {grid_search.best_params_}")
print(f"Best score: {grid_search.best_score_:.3f}")

# Get best model
best_model = grid_search.best_estimator_

Feature Selection

from sklearn.feature_selection import SelectFromModel, RFE
from sklearn.ensemble import RandomForestClassifier

# Method 1: SelectFromModel
selector = SelectFromModel(
    RandomForestClassifier(n_estimators=100, random_state=42),
    threshold='median'
)
X_selected = selector.fit_transform(X_train, y_train)

# Method 2: Recursive Feature Elimination
rfe = RFE(
    estimator=RandomForestClassifier(n_estimators=100, random_state=42),
    n_features_to_select=10,
    step=1
)
X_rfe = rfe.fit_transform(X_train, y_train)

# Get selected features
selected_features = X.columns[rfe.support_].tolist()

Integration with Babysitter SDK

Task Definition Example

const sklearnTrainingTask = defineTask({
  name: 'sklearn-model-training',
  description: 'Train a scikit-learn model with cross-validation',

  inputs: {
    modelType: { type: 'string', required: true },
    trainDataPath: { type: 'string', required: true },
    targetColumn: { type: 'string', required: true },
    hyperparameters: { type: 'object', default: {} },
    cvFolds: { type: 'number', default: 5 },
    scoringMetric: { type: 'string', default: 'accuracy' }
  },

  outputs: {
    modelPath: { type: 'string' },
    cvScores: { type: 'array' },
    bestScore: { type: 'number' },
    featureImportances: { type: 'object' }
  },

  async run(inputs, taskCtx) {
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      : ,
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        }
      },
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    };
  }
});

Model Selection Guide

Classification Models

Model	Use Case	Pros	Cons
LogisticRegression	Binary/multiclass, interpretable	Fast, interpretable	Linear boundary
RandomForestClassifier	General purpose	Robust, handles nonlinearity	Can overfit
GradientBoostingClassifier	High accuracy needed	State-of-art performance	Slower training
SVC	Small/medium datasets	Effective in high dimensions	Slow on large data
XGBClassifier	Competition/production	Fast, accurate	Many hyperparameters

Regression Models

Model	Use Case	Pros	Cons
LinearRegression	Baseline, interpretable	Simple, fast	Assumes linearity
Ridge/Lasso	Regularization needed	Prevents overfitting	Still linear
RandomForestRegressor	General purpose	Handles nonlinearity	Can overfit
GradientBoostingRegressor	High accuracy	Excellent performance	Slower
SVR	Small datasets	Robust to outliers	Slow scaling

Best Practices

1. Always Use Pipelines: Prevent data leakage by including preprocessing in pipelines
2. Stratified Splits: Use stratified sampling for imbalanced classification
3. Cross-Validation: Never tune hyperparameters on test data
4. Feature Scaling: Apply appropriate scaling for distance-based models
5. Random Seeds: Set random_state for reproducibility
6. Model Persistence: Use joblib over pickle for large numpy arrays

References

• Scikit-learn Documentation
• Scikit-learn User Guide
• Claude Scientific Skills - sklearn
• ML Models as MCP Tools