Scikit-learn 是 Python 最流行的机器学习库，构建在 NumPy、SciPy 和 Matplotlib 之上。由 David Cournapeau 于 2007 年发起，现在由 INRIA 和社区维护。
fit/predict/transform API 设计# 基础安装
pip install scikit-learn
# 如果需要完整科学计算环境（推荐新手）
pip install scikit-learn numpy scipy matplotlib pandas
# 清华镜像加速
pip install scikit-learn -i https://pypi.tuna.tsinghua.edu.cn/simple
验证安装：
import sklearn
print(sklearn.__version__) # 应输出 1.x.x
print(sklearn.show_versions()) # 查看所有依赖版本
Q: ImportError 关于 NumPy
pip install --upgrade numpy scipy
Q: macOS M1/M2 芯片安装失败
pip install --no-cache-dir scikit-learn
Q: 编译错误缺少 OpenMP（macOS）
brew install libomp
Q: 与已安装的 SciPy 版本冲突
pip install --upgrade scikit-learn scipy numpy
使用经典的 Iris 数据集，训练一个随机森林分类器并评估准确率。这是 scikit-learn 最经典的入门示例。
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, classification_report
# 1. 加载数据
iris = load_iris()
X, y = iris.data, iris.target
print(f"特征形状: {X.shape}, 标签形状: {y.shape}")
print(f"类别名: {iris.target_names}")
# 2. 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.3, random_state=42
)
# 3. 创建并训练模型
clf = RandomForestClassifier(n_estimators=100, random_state=42)
clf.fit(X_train, y_train)
# 4. 预测并评估
y_pred = clf.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print(f"\n准确率: {accuracy:.2%}")
print("\n分类报告:")
print(classification_report(y_test, y_pred, target_names=iris.target_names))
# 5. 特征重要性
for name, importance in zip(iris.feature_names, clf.feature_importances_):
print(f" {name}: {importance:.4f}")
pip install scikit-learn
python iris_classify.py
特征形状: (150, 4), 标签形状: (150,)
类别名: ['setosa' 'versicolor' 'virginica']
准确率: 97.78%
分类报告:
precision recall f1-score support
setosa 1.00 1.00 1.00 19
versicolor 0.93 1.00 0.96 13
virginica 1.00 0.92 0.96 13
accuracy 0.98 45
sepal length (cm): 0.1081
sepal width (cm): 0.0304
petal length (cm): 0.4195
petal width (cm): 0.4420
Scikit-learn 提供了「瑞士军刀」式的机器学习工具箱。无论你是做表格数据的分类、用户分群还是异常检测，掌握它能让你在几分钟内完成从数据处理到模型评估的完整流程。
Scikit-learn 所有模型遵循统一接口：
| 模式 | 伪代码 |
|---|---|
| 分类/回归 | model.fit(X_train, y_train) → model.predict(X_test) |
| 降维/聚类 | model.fit(X) → model.transform(X) |
| 预处理 | transformer.fit(X) → transformer.transform(X) |
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.linear_model import LogisticRegression
pipe = Pipeline([
("scaler", StandardScaler()),
("pca", PCA(n_components=2)),
("clf", LogisticRegression())
])
pipe.fit(X_train, y_train)
accuracy = pipe.score(X_test, y_test)
print(f"Pipeline 准确率: {accuracy:.2%}")
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from sklearn.pipeline import Pipeline
# 假设 columns 0-2 是数值型，column 3 是类别型
preprocessor = ColumnTransformer([
("num", StandardScaler(), [0, 1, 2]),
("cat", OneHotEncoder(), [3])
])
full_pipeline = Pipeline([
("preprocess", preprocessor),
("classifier", RandomForestClassifier())
])
from sklearn.model_selection import GridSearchCV
from sklearn.ensemble import GradientBoostingClassifier
param_grid = {
"n_estimators": [50, 100, 200],
"max_depth": [3, 5, None],
"learning_rate": [0.01, 0.1, 0.2]
}
grid = GridSearchCV(
GradientBoostingClassifier(),
param_grid,
cv=5,
scoring="accuracy",
n_jobs=-1
)
grid.fit(X_train, y_train)
print(f"最佳参数: {grid.best_params_}")
print(f"最佳 CV 分数: {grid.best_score_:.3f}")
import joblib
# 保存
joblib.dump(grid.best_estimator_, "best_model.joblib")
# 加载
model = joblib.load("best_model.joblib")
print(model.predict(X_test[:5]))
fit_transform 和 transform 在 cross-validation 中如何区分使用？