scikit-learn

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Use when "scikit-learn", "sklearn", "machine learning", "classification", "regression", "clustering", or asking about "train test split", "cross validation", "hyperparameter tuning", "ML pipeline", "random forest", "SVM", "preprocessing"

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Sourceeyadsibai/ltk

Added on2026-02-09

NPX Install

npx skill4agent add eyadsibai/ltk scikit-learn

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Scikit-learn Machine Learning

Industry-standard Python library for classical machine learning.

When to Use

Classification or regression tasks
Clustering or dimensionality reduction
Preprocessing and feature engineering
Model evaluation and cross-validation
Hyperparameter tuning
Building ML pipelines

Algorithm Selection

Classification

Algorithm	Best For	Strengths
Logistic Regression	Baseline, interpretable	Fast, probabilistic
Random Forest	General purpose	Handles non-linear, feature importance
Gradient Boosting	Best accuracy	State-of-art for tabular
SVM	High-dimensional data	Works well with few samples
KNN	Simple problems	No training, instance-based

Regression

Algorithm	Best For	Notes
Linear Regression	Baseline	Interpretable coefficients
Ridge/Lasso	Regularization needed	L2 vs L1 penalty
Random Forest	Non-linear relationships	Robust to outliers
Gradient Boosting	Best accuracy	XGBoost, LightGBM wrappers

Clustering

Algorithm	Best For	Key Parameter
KMeans	Spherical clusters	n_clusters (must specify)
DBSCAN	Arbitrary shapes	eps (density)
Agglomerative	Hierarchical	n_clusters or distance threshold
Gaussian Mixture	Soft clustering	n_components

Dimensionality Reduction

Method	Preserves	Use Case
PCA	Global variance	Feature reduction
t-SNE	Local structure	2D/3D visualization
UMAP	Both local/global	Visualization + downstream

Pipeline Concepts

Key concept: Pipelines prevent data leakage by ensuring transformations are fit only on training data.

Component	Purpose
Pipeline	Sequential steps (transform → model)
ColumnTransformer	Apply different transforms to different columns
FeatureUnion	Combine multiple feature extraction methods

Common preprocessing flow:

Impute missing values (SimpleImputer)
Scale numeric features (StandardScaler, MinMaxScaler)
Encode categoricals (OneHotEncoder, OrdinalEncoder)
Optional: feature selection or polynomial features

Model Evaluation

Cross-Validation Strategies

Strategy	Use Case
KFold	General purpose
StratifiedKFold	Imbalanced classification
TimeSeriesSplit	Temporal data
LeaveOneOut	Very small datasets

Metrics

Task	Metric	When to Use
Classification	Accuracy	Balanced classes
	F1-score	Imbalanced classes
	ROC-AUC	Ranking, threshold tuning
	Precision/Recall	Domain-specific costs
Regression	RMSE	Penalize large errors
	MAE	Robust to outliers
	R²	Explained variance

Hyperparameter Tuning

Method	Pros	Cons
GridSearchCV	Exhaustive	Slow for many params
RandomizedSearchCV	Faster	May miss optimal
HalvingGridSearchCV	Efficient	Requires sklearn 0.24+

Key concept: Always tune on validation set, evaluate final model on held-out test set.

Best Practices

Practice	Why
Split data first	Prevent leakage
Use pipelines	Reproducible, no leakage
Scale for distance-based	KNN, SVM, PCA need scaled features
Stratify imbalanced	Preserve class distribution
Cross-validate	Reliable performance estimates
Check learning curves	Diagnose over/underfitting

Common Pitfalls

Pitfall	Solution
Fitting scaler on all data	Use pipeline or fit only on train
Using accuracy for imbalanced	Use F1, ROC-AUC, or balanced accuracy
Too many hyperparameters	Start simple, add complexity
Ignoring feature importance	Use `feature_importances_` or permutation importance

Resources

Docs: https://scikit-learn.org/
User Guide: https://scikit-learn.org/stable/user_guide.html
Algorithm Cheat Sheet: https://scikit-learn.org/stable/tutorial/machine_learning_map/