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Scikit-learn

Python 经典机器学习库 - 简单 API、全面算法、预处理、Pipeline

TL;DR

是什么：Python 最流行的经典机器学习库。

为什么用：简单 API、全面算法、优秀文档、预处理工具。

Quick Start

安装：

pip install scikit-learn

Hello Scikit-learn：

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

# 加载数据
iris = load_iris()
X_train, X_test, y_train, y_test = train_test_split(
    iris.data, iris.target, test_size=0.2, random_state=42
)

# 训练
model = RandomForestClassifier()
model.fit(X_train, y_train)

# 预测
predictions = model.predict(X_test)
print(f"Accuracy: {accuracy_score(y_test, predictions):.2f}")

Cheatsheet

算法类型类
分类LogisticRegression, SVC, RandomForestClassifier
回归LinearRegression, SVR, RandomForestRegressor
聚类KMeans, DBSCAN, AgglomerativeClustering
降维PCA, TSNE, LDA
预处理StandardScaler, MinMaxScaler, LabelEncoder

Gotchas

预处理

from sklearn.preprocessing import StandardScaler, LabelEncoder, OneHotEncoder
from sklearn.impute import SimpleImputer

# 数值缩放
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

# 标签编码
encoder = LabelEncoder()
y_encoded = encoder.fit_transform(['cat', 'dog', 'cat'])

# 独热编码
onehot = OneHotEncoder(sparse_output=False)
X_onehot = onehot.fit_transform(X_categorical)

# 处理缺失值
imputer = SimpleImputer(strategy='mean')
X_imputed = imputer.fit_transform(X)

Pipeline

from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC

pipeline = Pipeline([
    ('scaler', StandardScaler()),
    ('svm', SVC(kernel='rbf'))
])

pipeline.fit(X_train, y_train)
predictions = pipeline.predict(X_test)

交叉验证

from sklearn.model_selection import cross_val_score, GridSearchCV

# 简单交叉验证
scores = cross_val_score(model, X, y, cv=5)
print(f"Mean accuracy: {scores.mean():.2f} (+/- {scores.std() * 2:.2f})")

# 网格搜索
param_grid = {
    'n_estimators': [50, 100, 200],
    'max_depth': [None, 10, 20]
}

grid_search = GridSearchCV(
    RandomForestClassifier(),
    param_grid,
    cv=5,
    scoring='accuracy'
)
grid_search.fit(X_train, y_train)

print(f"Best params: {grid_search.best_params_}")
print(f"Best score: {grid_search.best_score_:.2f}")

模型评估

from sklearn.metrics import (
    classification_report, confusion_matrix,
    mean_squared_error, r2_score
)

# 分类
print(classification_report(y_test, predictions))
print(confusion_matrix(y_test, predictions))

# 回归
mse = mean_squared_error(y_test, predictions)
r2 = r2_score(y_test, predictions)
print(f"MSE: {mse:.2f}, R2: {r2:.2f}")

常用算法

# 分类
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB

# 回归
from sklearn.linear_model import Ridge, Lasso
from sklearn.tree import DecisionTreeRegressor

# 聚类
from sklearn.cluster import KMeans, DBSCAN

Next Steps