scikit-learn

scikit-learn - Machine Learning in Python

A robust library for classical machine learning. It features a uniform API: all objects share the same interface for fitting, transforming, and predicting.

When to Use

• Classification: Detecting categories (Spam vs. Ham, Disease diagnosis).
• Regression: Predicting continuous values (House prices, Stock trends).
• Clustering: Grouping similar objects (Market segmentation, Image compression).
• Dimensionality Reduction: Reducing feature count while keeping info (PCA, Visualization).
• Model Selection: Comparing models and tuning hyperparameters (Cross-validation, Grid search).
• Preprocessing: Transforming raw data into features (Scaling, Encoding, Imputation).

Reference Documentation

Official docs: https://scikit-learn.org/stable/
User Guide: https://scikit-learn.org/stable/user_guide.html
Search patterns: sklearn.pipeline.Pipeline, sklearn.model_selection, sklearn.ensemble, sklearn.preprocessing

Core Principles

The "Estimator" Interface

• Estimators: Implement fit(X, y). They learn from data.
• Transformers: Implement transform(X) (and fit_transform(X)). They modify data.
• Predictors: Implement predict(X). They provide estimates for new data.

Use scikit-learn For

• Tabular data (Excel-like, CSVs).
• Traditional ML (Random Forests, SVMs, Linear Models).
• Feature engineering and pipeline automation.
• Small to medium-sized datasets.

Do NOT Use For

• Deep Learning / Neural Networks (use PyTorch or TensorFlow).
• Natural Language Processing at scale (use spaCy or HuggingFace).
• Large-scale "Big Data" (use Spark MLlib or Dask-ML).
• Real-time streaming predictions (consider specialized inference engines).

Quick Reference

Installation

pip install scikit-learn

Standard Imports

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.metrics import classification_report, mean_squared_error