Scikit-learn Documentation

Scikit-learn is a powerful Python library for machine learning that provides simple and efficient tools for data analysis and modeling. This documentation covers the basic concepts, usage, and examples of Scikit-learn.

Features

Supervised Learning: Linear regression, logistic regression, support vector machines, k-nearest neighbors, and more.
Unsupervised Learning: Clustering, dimensionality reduction, and anomaly detection.
Model Selection: Grid search, cross-validation, and model evaluation metrics.
Preprocessing: Data transformation, scaling, and feature extraction.

Quick Start

Here's a simple example of using Scikit-learn to train a linear regression model:

from sklearn.linear_model import LinearRegression

# Create a linear regression object
regr = LinearRegression()

# Train the model using the training sets
regr.fit(X_train, y_train)

# Make predictions using the testing set
y_pred = regr.predict(X_test)

For more detailed examples and tutorials, visit our tutorials page.

Resources