Scikit-learn is a free software machine learning library for the Python programming language. It features various classification, regression, and clustering algorithms, including support vector machines, random forests, gradient boosting, k-means, and DBSCAN. It is designed to interoperate with Python numerical and scientific libraries such as NumPy and SciPy.
Here’s a basic example to get you started using Scikit-learn for a classification task:
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from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

# Load the iris dataset
iris = datasets.load_iris()
X = iris.data
y = iris.target

# Split the dataset into training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# Create a RandomForestClassifier
clf = RandomForestClassifier(n_estimators=100, random_state=42)

# Train the classifier
clf.fit(X_train, y_train)

# Make predictions
y_pred = clf.predict(X_test)

# Calculate the accuracy
acc = accuracy_score(y_test, y_pred)
print(f’Accuracy: {acc}’)
This example demonstrates the basic workflow of loading a dataset, splitting it into training and test sets, creating a classifier (in this case, a random forest), training it on the training data, and evaluating its accuracy on the test data.
Make sure you have Scikit-learn installed in your environment before running the code. You can install it using pip:
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pip install scikit-learn