/scikit-learn-machine-learning

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

pip install scikit-learn numpy pandas matplotlib seaborn

Quick Start

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.

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}")

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.

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)

# Compare classifiers
classifiers = {
    "LogisticRegression": LogisticRegression(max_iter=200),
    "RandomForest": RandomForestClassifier(n_estimators=100, random_state=42),
    "SVM": SVC(kernel="rbf", C=1.0),
    "GradientBoosting": GradientBoostingClassifier(n_estimators=100, random_state=42),
}
for name, clf in classifiers.items():
    clf.fit(X_train, y_train)
    print(f"{name}: accuracy = {clf.score(X_test, y_test):.3f}")

Module 3: Supervised Learning — Regression

Regressors for continuous target prediction.

from sklearn.linear_model import LinearRegression, Ridge, Lasso, ElasticNet
from sklearn.ensemble import RandomForestRegressor
from sklearn.datasets import make_regression
from sklearn.metrics import mean_squared_error, r2_score

X, y = make_regression(n_samples=200, n_features=10, noise=10, random_state=42)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

models = {
    "Linear": LinearRegression(),
    "Ridge": Ridge(alpha=1.0),
    "Lasso": Lasso(alpha=0.1),
    "RandomForest": RandomForestRegressor(n_estimators=100, random_state=42),
}
for name, model in models.items():
    model.fit(X_train, y_train)
    y_pred = model.predict(X_test)
    print(f"{name}: RMSE={mean_squared_error(y_test, y_pred, squared=False):.2f}, R²={r2_score(y_test, y_pred):.3f}")

Module 4: Unsupervised Learning — Clustering

Clustering algorithms for unlabeled data.

from sklearn.cluster import KMeans, DBSCAN, AgglomerativeClustering
from sklearn.metrics import silhouette_score
from sklearn.datasets import make_blobs

X, y_true = make_blobs(n_samples=300, centers=4, random_state=42)

# K-Means with elbow method
for k in [2, 3, 4, 5, 6]:
    km = KMeans(n_clusters=k, random_state=42, n_init=10)
    labels = km.fit_predict(X)
    sil = silhouette_score(X, labels)
    print(f"k={k}: silhouette={sil:.3f}, inertia={km.inertia_:.1f}")

# DBSCAN — no need to specify k
from sklearn.cluster im