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Scikit-learn is a Python library for supervised and unsupervised machine learning. Its consistent estimator interface lets you fit models, prepare features, connect those steps in pipelines, and evaluate predictions. This guide walks through a beginner-friendly workflow, from an isolated installation to testing a model on data it has not seen.
What scikit-learn does
Scikit-learn provides tools for common machine-learning tasks, including classification, regression, clustering, preprocessing, model selection, and evaluation. In supervised learning, a model learns from examples with known targets; in unsupervised learning, it looks for structure in data without target labels. The scikit-learn Getting Started guide introduces the library and its shared workflow.
The core idea is to treat learning components as objects with familiar methods. An estimator learns from data with fit. A predictor can then use predict to produce outputs for new examples. A transformer prepares or changes features, often learning the transformation parameters from training data.
Install scikit-learn in an isolated environment
The scikit-learn installation guide recommends the latest official release for most users and recommends isolating project dependencies with tools such as Python’s venv or conda. Isolation helps prevent one project’s package changes from disrupting another. See the official installation instructions for platform-specific details and alternative methods.
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As of the project site’s October 10, 2026 status, scikit-learn 1.9.1 is labeled stable and was released in September 2026. The project’s compatibility guidance says the 1.9 series requires Python 3.11 or newer. These version requirements can change, so check the project site and installation guide before setting up a new environment.
python -m venv .venv
# Activate the environment:
# macOS or Linux
source .venv/bin/activate
# Windows PowerShell
.venvScriptsActivate.ps1
python -m pip install --upgrade pip
python -m pip install scikit-learnUse the activation command for your operating system, then run the install commands in that environment. Other installation routes have different purposes:
- Latest official release: the default choice for most users.
- Operating-system or distribution package: convenient when managed centrally, but it may not be as recent as the official release.
- Nightly build: intended for trying upcoming fixes or features, not as the ordinary stable setup.
- Source installation: mainly useful to people contributing to scikit-learn.
Understand estimators, transformers, and pipelines
Estimator: learn from examples
An estimator is an object that learns from data, typically through fit(X, y), where X contains feature rows and y contains the target values for supervised tasks. For example, a classifier estimates how to assign categories from labeled examples. Exact methods vary by estimator and task.
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A transformer changes feature data, for example by scaling numeric values. Many transformers learn parameters during fit and apply them with transform. Keeping this operation in the modeling workflow matters: if it learns from test examples, information from the evaluation data can leak into training.
Pipeline: connect preparation and prediction
A pipeline chains transformers and a final estimator so the whole sequence can be fitted and evaluated as one object. For a classification task, a common pattern is to scale features and then fit logistic regression:
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
model = make_pipeline(
StandardScaler(),
LogisticRegression()
)When the pipeline is fitted, the scaler learns its parameters from the training features, transforms them, and passes them to the classifier. At prediction time, the same fitted scaling step is applied before the classifier makes predictions. The official Getting Started examples demonstrate this estimator-and-pipeline pattern.
Split data before fitting and evaluate on held-out examples
A model’s score on examples used to fit it does not show how well it will handle unseen data. As the scikit-learn documentation puts it, “Fitting a model to some data does not entail that it will predict well on unseen data.” Set aside test examples before fitting, train the complete pipeline only on the training portion, and use the test portion for the final evaluation.
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
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, random_state=42, stratify=y
)
model = make_pipeline(StandardScaler(), LogisticRegression())
model.fit(X_train, y_train)
predictions = model.predict(X_test)
print("Held-out accuracy:", model.score(X_test, y_test))This Iris example creates a stratified train/test split, fits preprocessing and classification together using only training rows, then reports accuracy on held-out rows. Accuracy is the fraction of test predictions that match their labels; it is not the right metric for every problem, particularly when classes are imbalanced or errors have unequal costs. Choose an evaluation measure that matches the task and the consequences of mistakes.
Keep the test set out of preprocessing, model fitting, and hyperparameter decisions. If you repeatedly use the test score to choose settings, it gradually stops serving as an independent final check.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Use cross-validation and search to compare settings
A single split can give a noisy estimate, especially when the dataset is small. Cross-validation evaluates a workflow across multiple train/validation splits. Scikit-learn’s cross_validate can report scores across those splits; use a pipeline so each fold learns preprocessing only from that fold’s training portion.
Hyperparameters are choices that configure a model rather than values learned directly from the training examples. For a random forest, examples include the number of trees and maximum depth. Scikit-learn provides cross-validation-based model-selection tools, including randomized search, to compare candidate settings. Make these comparisons using training data and cross-validation; reserve the test set for the final assessment.
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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesThere is no universally best estimator established for every dataset. Compare candidates for the problem type—such as classification, regression, or clustering—the structure and size of the data, validation results, and practical constraints such as interpretability or computation. The scikit-learn User Guide provides deeper explanations of estimators, preprocessing, evaluation, and model selection.
Quick Recap
Common mistakes to avoid
- Scoring on training data and calling it generalization: use data kept out of fitting to estimate performance on unseen examples.
- Preprocessing before the split: fit transformations within a pipeline on training data, rather than letting test examples influence learned preprocessing.
- Tuning against the test set: compare settings with cross-validation on the training data, then assess the chosen workflow on the test set.
- Assuming one model or metric suits every problem: select them based on the task, data, validation evidence, and the costs of errors.
- Installing into a shared environment without checking compatibility: use an isolated environment and verify current Python and package requirements in the official installation guide.
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