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 import DBSCAN

db = DBSCAN(eps=0.5, min_samples=5)
labels = db.fit_predict(X)
n_clusters = len(set(labels)) - (1 if -1 in labels else 0)
n_noise = (labels == -1).sum()
print(f"DBSCAN: {n_clusters} clusters, {n_noise} noise points")

Module 5: Dimensionality Reduction

PCA, t-SNE, and other methods for visualization and feature reduction.

from sklearn.decomposition import PCA
from sklearn.manifold import TSNE
from sklearn.datasets import load_digits

X, y = load_digits(return_X_y=True)
print(f"Original shape: {X.shape}")  # (1797, 64)

# PCA — preserve 95% variance
pca = PCA(n_components=0.95)
X_pca = pca.fit_transform(X)
print(f"PCA: {X_pca.shape[1]} components, explained variance: {pca.explained_variance_ratio_.sum():.3f}")

# t-SNE — 2D visualization
tsne = TSNE(n_components=2, perplexity=30, random_state=42)
X_tsne = tsne.fit_transform(X)
print(f"t-SNE shape: {X_tsne.shape}")  # (1797, 2)

Module 6: Model Evaluation & Selection

Cross-validation, metrics, hyperparameter tuning.

from sklearn.model_selection import cross_val_score, GridSearchCV, StratifiedKFold
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report, confusion_matrix
from sklearn.datasets import load_iris

X, y = load_iris(return_X_y=True)

# Cross-validation
clf = RandomForestClassifier(n_estimators=100, random_state=42)
scores = cross_val_score(clf, X, y, cv=StratifiedKFold(5), scoring="accuracy")
print(f"CV accuracy: {scores.mean():.3f} ± {scores.std():.3f}")

# Hyperparameter tuning with GridSearchCV
param_grid = {
    "n_estimators": [50, 100, 200],
    "max_depth": [5, 10, None],
    "min_samples_split": [2, 5]
}
grid = GridSearchCV(
    RandomForestClassifier(random_state=42),
    param_grid, cv=5, scoring="accuracy", n_jobs=-1
)
grid.fit(X, y)
print(f"Best params: {grid.best_params_}")
print(f"Best score: {grid.best_score_:.3f}")

Module 7: Pipelines

Chain preprocessing and models; prevent data leakage.

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

# Mixed-type preprocessing
numeric_features = ["age", "income"]
categorical_features = ["gender", "occupation"]

preprocessor = ColumnTransformer([
    ("num", Pipeline([
        ("imputer", SimpleImputer(strategy="median")),
        ("scaler", StandardScaler())
    ]), numeric_features),
    ("cat", Pipeline([
        ("imputer", SimpleImputer(strategy="most_frequent")),
        ("onehot", OneHotEncoder(handle_unknown="ignore"))
    ]), categorical_features),
])

pipe = Pipeline([
    ("preprocessor", preprocessor),
    ("classifier", GradientBoostingClassifier(random_state=42))
])
# pipe.fit(X_train, y_train); pipe.predict(X_test)
print("Pipeline steps:", [name for name, _ in pipe.steps])

Common Workflows

Workflow 1: End-to-End Classification

Goal: Complete classification workflow from data loading to evaluation.

import pandas as pd
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report
from sklearn.datasets import load_breast_cancer

# Load data
X, y = load_breast_cancer(return_X_y=True, as_frame=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y, random_state=42)

# Build pipeline
pipe = Pipeline([
    ("scaler", StandardScaler()),
    ("clf", RandomForestClassifier(n_estimators=200, random_state=42))
])

# Cross-validate
cv_scores = cross_val_score(pipe, X_train, y_train, cv=5, scoring="f1")
print(f"CV F1: {cv_scores.mean():.3f} ± {cv_scores.std():.3f}")

# Final evaluation
pipe.fit(X_train, y_train)
y_pred = pipe.predict(X_test)
print(classification_report(y_test, y_pred))

Workflow 2: Clustering with Visualization

Goal: Cluster data and visualize with dimensionality reduction.

from sklearn.datasets import make_blobs
from sklearn.preprocessing import StandardScaler
from sklearn.cluster import KMeans
from sklearn.decomposition import PCA
from sklearn.metrics import silhouette_score
import matplotlib.pyplot as plt

# Generate and scale data
X, _ = make_blobs(n_samples=500, centers=4, random_state=42)
X_scaled = StandardScaler().fit_transform(X)

# Cluster
km = KMeans(n_clusters=4, random_state=42, n_init=10)
labels = km.fit_predict(X_scaled)
print(f"Silhouette: {silhouette_score(X_scaled, labels):.3f}")

# Visualize
X_2d = PCA(n_components=2).fit_transform(X_scaled)
plt.scatter(X_2d[:, 0], X_2d[:, 1], c=labels, cmap="viridis", s=20, alpha=0.7)
plt.title("K-Means Clustering (PCA projection)")
plt.savefig("clustering_result.png", dpi=150, bbox_inches="tight")
print("Saved clustering_result.png")

Workflow 3: Feature Selection + Model Pipeline

Goal: Select best features and build a tuned model.

from sklearn.datasets import make_classification
from sklearn.feature_selection import SelectKBest, f_classif
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
from sklearn.model_selection import GridSearchCV

X, y = make_classification(n_samples=500, n_features=50, n_informative=10, random_state=42)

pipe = Pipeline([
    ("scaler", StandardScaler()),
    ("selector", SelectKBest(f_classif)),
    ("svm", SVC(kernel="rbf"))
])

param_grid = {
    "selector__k": [5, 10, 20],
    "svm__C": [0.1, 1, 10],
    "svm__gamma": ["scale", "auto"]
}

grid = GridSearchCV(pipe, param_grid, cv=5, scoring="accuracy", n_jobs=-1)
grid.fit(X, y)
print(f"Best params: {grid.best_params_}")
print(f"Best accuracy: {grid.best_score_:.3f}")

Key Parameters

Parameter	Module	Default	Range / Options	Effect
n_estimators	RandomForest, GradientBoosting	100	50-1000	Number of trees; higher = better but slower
max_depth	Tree-based models	None	1-50, None	Tree depth; None = no limit (can overfit)
C	SVM, LogisticRegression	1.0	0.001-1000	Regularization strength (inverse); lower = more regularization
alpha	Ridge, Lasso	1.0	0.001-100	Regularization strength; higher = more regularization
n_clusters	KMeans	required	2-N	Number of clusters to form
eps	DBSCAN	0.5	0.01-10	Neighborhood radius; smaller = more clusters
n_components	PCA	required	1-N or 0.0-1.0	Components to keep; float = variance ratio
perplexity	t-SNE	30	5-50	Balance local/global structure
cv	GridSearchCV	5	2-10	Cross-validation folds
scoring	GridSearchCV, cross_val_score

Common Recipes

Recipe: Feature Importance Analysis

When to use: Understanding which features drive model predictions.

import numpy as np
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_iris

X, y = load_iris(return_X_y=True)
clf = RandomForestClassifier(n_estimators=200, random_state=42).fit(X, y)

importances = clf.feature_importances_
indices = np.argsort(importances)[::-1]
feature_names = load_iris().feature_names
for i in range(X.shape[1]):
    print(f"{feature_names[indices[i]]}: {importances[indices[i]]:.4f}")

Recipe: Learning Curve Diagnosis

When to use: Diagnosing overfitting vs underfitting.

from sklearn.model_selection import learning_curve
import matplotlib.pyplot as plt
import numpy as np

train_sizes, train_scores, val_scores = learning_curve(
    clf, X, y, cv=5, train_sizes=np.linspace(0.1, 1.0, 10), scoring="accuracy"
)
plt.plot(train_sizes, train_scores.mean(axis=1), label="Train")
plt.plot(train_sizes, val_scores.mean(axis=1), label="Validation")
plt.xlabel("Training size"); plt.ylabel("Accuracy"); plt.legend()
plt.savefig("learning_curve.png", dpi=150, bbox_inches="tight")
print("Saved learning_curve.png")

Recipe: Save and Load Models

When to use: Persisting trained models for later use.

import joblib

# Save
joblib.dump(pipe, "model_pipeline.joblib")
print("Model saved to model_pipeline.joblib")

# Load
loaded_pipe = joblib.load("model_pipeline.joblib")
y_pred = loaded_pipe.predict(X_test)
print(f"Loaded model predictions: {y_pred[:5]}")

Troubleshooting

Problem	Cause	Solution
ConvergenceWarning	Model didn't converge	Increase max_iter (e.g., 1000) or scale features with StandardScaler
High train accuracy, low test accuracy	Overfitting	Add regularization, reduce max_depth, use cross-validation
ValueError: unknown categories	New categories in test data	Use OneHotEncoder(handle_unknown='ignore')
MemoryError with large data	Full dataset in memory	Use SGDClassifier/MiniBatchKMeans for incremental learning
Poor clustering results	Unscaled features or wrong k	Scale features first; use silhouette score to find optimal k
NotFittedError	Predict before fit	Call model.fit(X_train, y_train) first
Different results each run	Missing random_state	Set random_state=42 in model and train_test_split
Slow GridSearchCV	Large parameter grid	Use RandomizedSearchCV or HalvingGridSearchCV; add n_jobs=-1

References

• scikit-learn User Guide — official documentation
• scikit-learn API Reference — complete API
• scikit-learn Examples Gallery — tutorials
• Pedregosa et al. (2011). Scikit-learn: Machine Learning in Python. JMLR 12:2825-2830.