Scikit-Learn 教程

Scikit-Learn 是一个开源的 Python 库，用于数据挖掘和数据分析。以下是一些 Scikit-Learn 的基本教程，帮助您快速入门。

安装 Scikit-Learn

首先，您需要安装 Scikit-Learn。可以使用以下命令进行安装：

pip install scikit-learn

基本用法

Scikit-Learn 提供了多种机器学习算法，包括分类、回归、聚类等。以下是一些基本用法示例：

分类

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

iris = load_iris()
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.3, random_state=0)

knn = KNeighborsClassifier()
knn.fit(X_train, y_train)
print(knn.score(X_test, y_test))

回归

from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression

boston = load_boston()
X_train, X_test, y_train, y_test = train_test_split(boston.data, boston.target, test_size=0.3, random_state=0)

lr = LinearRegression()
lr.fit(X_train, y_train)
print(lr.score(X_test, y_test))

聚类

from sklearn.datasets import make_blobs
from sklearn.cluster import KMeans

X, y = make_blobs(n_samples=150, centers=3, cluster_std=0.5, random_state=0)

kmeans = KMeans(n_clusters=3)
kmeans.fit(X)
print(kmeans.labels_)

扩展阅读

如果您想了解更多关于 Scikit-Learn 的信息，可以访问以下链接：

Scikit-Learn 官方文档