Scikit-learn is a free and open-source machine learning library for Python. It is one of the most popular machine learning libraries in the world, and is used by data scientists and machine learning engineers in a wide variety of industries.
Scikit-learn provides a wide range of machine learning algorithms, including supervised learning algorithms (such as classification and regression), unsupervised learning algorithms (such as clustering and dimensionality reduction), and semi-supervised learning algorithms. It also provides a variety of tools for data preprocessing, model evaluation, and model selection.
Scikit-learn is easy to use and has a well-documented API. It is also very efficient, making it a good choice for large-scale machine learning projects.
History
Scikit-learn was originally developed by David Cournapeau in 2007. It was inspired by the R statistical programming language, which has a similar machine learning library called R^2.
Scikit-learn was originally released under the BSD license, but it was later switched to the Apache 2.0 license. This made it easier for other developers to contribute to the project.
Features
Scikit-learn provides a wide range of machine learning algorithms, including:
- Supervised learning algorithms:
- Classification algorithms: These algorithms are used to predict a categorical outcome, such as whether a patient has a disease or not. Some popular classification algorithms in Scikit-learn include logistic regression, decision trees, and support vector machines.
- Regression algorithms: These algorithms are used to predict a continuous outcome, such as the price of a house or the number of sales. Some popular regression algorithms in Scikit-learn include linear regression, ridge regression, and Lasso regression.
- Unsupervised learning algorithms:
- Clustering algorithms: These algorithms are used to group data points together based on their similarity. Some popular clustering algorithms in Scikit-learn include k-means, hierarchical clustering, and DBSCAN.
- Dimensionality reduction algorithms: These algorithms are used to reduce the number of features in a dataset. This can be helpful for improving the performance of machine learning algorithms. Some popular dimensionality reduction algorithms in Scikit-learn include principal component analysis (PCA), singular value decomposition (SVD), and t-SNE.
- Semi-supervised learning algorithms:
- These algorithms are used to train machine learning models on datasets that have a small number of labeled data points and a large number of unlabeled data points. Some popular semi-supervised learning algorithms in Scikit-learn include label propagation and self-training.
Scikit-learn also provides a variety of tools for data preprocessing, model evaluation, and model selection. These tools can help you to prepare your data for machine learning, evaluate the performance of your models, and select the best model for your problem.
Installation
Scikit-learn can be installed using the pip package manager. To install Scikit-learn, open a terminal window and run the following command:
pip install scikit-learn
Getting Started
To get started with Scikit-learn, you can create a new Python project and import the sklearn library. Once you have imported the library, you can start using the machine learning algorithms and tools that it provides.
For example, to train a logistic regression model on a dataset of breast cancer patients, you can use the following code:
import sklearn
# Load the breast cancer dataset
cancer_dataset = sklearn.datasets.load_breast_cancer()
# Split the dataset into training and test sets
X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(
cancer_dataset.data, cancer_dataset.target, test_size=0.25
)
# Create a logistic regression model
logistic_regression = sklearn.linear_model.LogisticRegression()
# Train the model on the training set
logistic_regression.fit(X_train, y_train)
# Evaluate the model on the test set
accuracy = logistic_regression.score(X_test, y_test)
print("Accuracy:", accuracy)
This code will load the breast cancer dataset, split it into training and test sets, create a logistic regression model, train the model on the training set, and evaluate the model on the test set. The output of the code will be the accuracy of the model on the test set.
