How to Install Scikit-learn for Machine Learning on Linux

This guide provides a comprehensive walkthrough of installing and using the Scikit-learn machine learning library on Linux systems. Scikit-learn (sklearn) is a powerful, open-source Python library offering a wide array of tools for various machine learning tasks, from basic regression to sophisticated clustering.

Understanding Scikit-learn

Scikit-learn simplifies complex machine learning algorithms, building upon NumPy, SciPy, and matplotlib. Key features include:

• Supervised learning (classification, regression)
• Unsupervised learning (clustering, dimensionality reduction)
• Robust model evaluation and validation methods
• Extensive data preprocessing capabilities
• Support for diverse data formats and streamlined model deployment

Prerequisites: Python and Pip

Before installing Scikit-learn, ensure Python and its package manager, pip, are installed. Check their presence using:

python3 --version
pip3 --version

If either is missing, use your distribution's package manager (replace with the appropriate command for your system):

# Debian/Ubuntu/Mint
sudo apt install python3 python3-pip

# RHEL/CentOS/Fedora/Rocky/AlmaLinux
sudo yum install python3 python3-pip

# Gentoo
sudo emerge -a sys-apps/python3 dev-python/pip

# Alpine
sudo apk add python3 py3-pip

# Arch Linux
sudo pacman -S python3 python-pip

# OpenSUSE
sudo zypper install python3 python3-pip

# FreeBSD
sudo pkg install python3 py38-pip

Installing Scikit-learn

It's best practice to use a virtual environment to isolate Scikit-learn and its dependencies:

python3 -m venv sklearn-env
source sklearn-env/bin/activate
pip3 install -U scikit-learn

This installs the latest Scikit-learn version and its dependencies. Verify the installation:

python3 -m pip show scikit-learn  # Shows version and location
python3 -m pip freeze             # Lists all installed packages
python3 -c "import sklearn; sklearn.show_versions()" #Detailed version information

Using Scikit-learn: A Practical Example

Let's explore Scikit-learn's capabilities with the Iris dataset:

1. Importing and Loading Data:

from sklearn.datasets import load_iris
iris = load_iris()
print(iris.data)
print(iris.target)

2. Data Splitting:

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2, random_state=42)
print("Training data:", X_train.shape)
print("Testing data:", X_test.shape)

3. Model Training (Support Vector Machine):

from sklearn.svm import SVC
model = SVC()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
print("Predicted labels:", y_pred)

4. Model Evaluation:

from sklearn.metrics import accuracy_score
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)

This example demonstrates a basic workflow: data loading, splitting, model training (using an SVM), and performance evaluation using accuracy. Scikit-learn offers a vast array of algorithms and tools to explore for more complex machine learning tasks. Experimentation with different algorithms and evaluation metrics is key to building effective models.

The above is the detailed content of How to Install Scikit-learn for Machine Learning on Linux. For more information, please follow other related articles on the PHP Chinese website!