scikit-learn-machine-learning

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---
name: "scikit-learn-machine-learning"
description: "Classical ML in Python: classification, regression, clustering, dim reduction, evaluation, tuning, preprocessing pipelines. Linear models, tree ensembles, SVMs, K-Means, PCA, t-SNE. Use PyTorch/TF for deep learning; XGBoost/LightGBM for scale."
license: "BSD-3-Clause"
---

# scikit-learn

## Overview

scikit-learn is the standard Python library for classical machine learning. It provides consistent APIs for supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model evaluation, and preprocessing, with seamless integration into NumPy/pandas workflows.

## When to Use

- Building classification models for labeled data (spam detection, disease diagnosis, species identification)
- Predicting continuous outcomes with regression (price prediction, dose-response modeling)
- Clustering unlabeled data into groups (patient stratification, gene expression clusters)
- Reducing dimensionality for visualization or feature engineering (PCA, t-SNE on multi-omics data)
- Evaluating and comparing model performance with cross-validation
- Tuning hyperparameters systematically (grid search, random search)
- Building reproducible ML pipelines with preprocessing and modeling steps
- For deep learning tasks (images, NLP), use `pytorch` or `transformers` instead
- For large-scale gradient boosting, use `xgboost` or `lightgbm` instead

## Prerequisites

- **Python packages**: `scikit-learn`, `numpy`, `pandas`
- **Optional**: `matplotlib`, `seaborn` for visualization
- **Data**: Tabular data as NumPy arrays or pandas DataFrames

```bash
pip install scikit-learn numpy pandas matplotlib seaborn
```

## Quick Start

```python
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, classification_report
from sklearn.datasets import load_breast_cancer

# Load dataset, split, train, evaluate in 10 lines
X, y = load_breast_cancer(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

clf = RandomForestClassifier(n_estimators=100, random_state=42)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)

print(f"Accuracy: {accuracy_score(y_test, y_pred):.3f}")
print(classification_report(y_test, y_pred, target_names=["malignant", "benign"]))
```

## Core API

### Module 1: Data Preprocessing

Scaling, encoding, imputation, and feature engineering.

```python
from sklearn.preprocessing import StandardScaler, MinMaxScaler, OneHotEncoder
from sklearn.impute import SimpleImputer
import numpy as np

# Scaling: zero mean, unit variance
X = np.array([[1, 2], [3, 4], [5, 6]])
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
print(f"Mean: {X_scaled.mean(axis=0)}, Std: {X_scaled.std(axis=0)}")
# Mean: [0. 0.], Std: [1. 1.]

# Imputation: fill missing values
X_missing = np.array([[1, np.nan], [3, 4], [np.nan, 6]])
imputer = SimpleImputer(strategy="median")
X_filled = imputer.fit_transform(X_missing)
print(f"Filled:\n{X_filled}")
```

```python
from sklearn.preprocessing import OneHotEncoder, OrdinalEncoder, LabelEncoder

# One-hot encoding for nominal categories
enc = OneHotEncoder(sparse_output=False, handle_unknown="ignore")
X_cat = np.array([["red"], ["blue"], ["green"], ["red"]])
X_encoded = enc.fit_transform(X_cat)
print(f"Categories: {enc.categories_}")
print(f"Encoded shape: {X_encoded.shape}")  # (4, 3)
```

### Module 2: Supervised Learning — Classification

Classifiers for discrete target prediction.

```python
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

X, y = load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y, random_state=42)

#