Scikit-learn – Machine Learning

What is Scikit-learn

Scikit-learn — is one of the most popular and powerful open-source Python libraries for machine learning. Developed in 2007 by David Cournapeau, the library has become the de facto standard for implementing machine learning algorithms in the Python ecosystem.

Main Tasks and Capabilities of Scikit-learn

The library provides ready-made tools for solving a wide range of machine learning tasks:

Supervised learning tasks:

• Classification — determining the belonging of objects to specific classes
• Regression — predicting continuous numerical values

Unsupervised learning tasks:

• Clustering — grouping similar objects
• Dimensionality reduction — compressing data while preserving important information
• Anomaly detection — identifying outliers in data

Additional capabilities:

• Data preprocessing — normalization, scaling, encoding
• Feature selection — choosing the most informative characteristics
• Model evaluation — various quality metrics
• Cross-validation — checking model stability
• Hyperparameter tuning — automatic parameter adjustment

Installation and Initial Setup

Installation via pip

pip install scikit-learn

Installation with additional dependencies

pip install scikit-learn pandas matplotlib seaborn jupyter

Checking the installation

import sklearnprint(sklearn.__version__)

Importing Main Components

# Main modulesfrom sklearn.model_selection import train_test_split, cross_val_scorefrom sklearn.preprocessing import StandardScaler, LabelEncoderfrom sklearn.linear_model import LinearRegression, LogisticRegressionfrom sklearn.ensemble import RandomForestClassifierfrom sklearn.metrics import accuracy_score, classification_reportfrom sklearn.datasets import load_iris, make_classification

Architecture and Main Concepts

Estimator

Estimator — is the base class for all machine learning algorithms in Scikit-learn. Each estimator implements two main methods:

• fit(X, y) — training the model on data
• predict(X) — making predictions for new data

from sklearn.linear_model import LinearRegression

model = LinearRegression()model.fit(X_train, y_train)predictions = model.predict(X_test)

Transformer

Transformer — is a class for transforming data. It implements the methods:

• fit(X) — learning the transformation parameters
• transform(X) — applying the transformation
• fit_transform(X) — combining fit and transform

from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()X_scaled = scaler.fit_transform(X)

Pipeline

Pipeline allows you to combine several steps of data processing and training into a single sequence:

from sklearn.pipeline import Pipelinefrom sklearn.preprocessing import StandardScalerfrom sklearn.svm import SVC

pipeline = Pipeline([    ('scaler', StandardScaler()),    ('classifier', SVC())])

pipeline.fit(X_train, y_train)predictions = pipeline.predict(X_test)

Working with Data

Built-in Datasets

Scikit-learn provides many ready-made dat