Scikit-Learn is a powerful Python library for machine learning. It provides a wide range of algorithms for data mining, data analysis, and data visualization. With Scikit-Learn, you can build models for classification, regression, clustering, and dimensionality reduction.
Features
- Extensive Algorithms: Scikit-Learn provides a variety of algorithms for different tasks.
- Easy to Use: The library is designed to be user-friendly and easy to use.
- Extensibility: You can easily extend the library with your own algorithms.
- Integration: Scikit-Learn integrates well with other Python libraries.
Getting Started
To get started with Scikit-Learn, you can install it using pip:
pip install scikit-learn
Once installed, you can import the library and start using it in your Python scripts.
from sklearn import datasets
from sklearn.model_selection import train_test_split
# Load a dataset
iris = datasets.load_iris()
X = iris.data
y = iris.target
# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
Example
Here's a simple example of a classification task using Scikit-Learn:
from sklearn.svm import SVC
# Create a support vector classifier
clf = SVC(kernel='linear')
# Train the classifier
clf.fit(X_train, y_train)
# Make predictions
y_pred = clf.predict(X_test)
# Evaluate the classifier
score = clf.score(X_test, y_test)
print(f"Accuracy: {score:.2f}")
Learn More
For more information about Scikit-Learn, please visit the official documentation: Scikit-Learn Documentation
Would you like to explore more machine learning libraries? Check out our Machine Learning Resources page for more information!