22.1 机器学习概述
22.1.1 机器学习分类
22.1.2 典型应用场景
学习类型
应用案例
常用算法
分类
垃圾邮件识别
决策树/SVM
回归
房价预测
线性回归
聚类
客户分群
K-Means
降维
数据可视化
PCA
22.2 scikit-learn工作流
22.2.1 基本流程
from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import accuracy_score # 1. 数据准备 X_train, X_test, y_train, y_test = train_test_split( features, labels, test_size=0.2, random_state=42 ) # 2. 特征工程 scaler = StandardScaler() X_train_scaled = scaler.fit_transform(X_train) X_test_scaled = scaler.transform(X_test) # 3. 模型训练 model = RandomForestClassifier(n_estimators=100) model.fit(X_train_scaled, y_train) # 4. 模型评估 y_pred = model.predict(X_test_scaled) print(f"准确率: {accuracy_score(y_test, y_pred):.2f}")22.2.2 交叉验证
from sklearn.model_selection import cross_val_score scores = cross_val_score( model, X_train_scaled, y_train, cv=5, # 5折交叉验证 scoring=accuracy ) print(f"交叉验证平均准确率: {scores.mean():.2f} (±{scores.std():.2f})")22.3 监督学习算法
22.3.1 线性模型
from sklearn.linear_model import LinearRegression, LogisticRegression # 线性回归 lin_reg = LinearRegression() lin_reg.fit(X_train, y_train) # 逻辑回归 log_reg = LogisticRegression(max_iter=1000) log_reg.fit(X_train, y_train)22.3.2 决策树与集成方法
from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import GradientBoostingClassifier # 决策树 tree = DecisionTreeClassifier(max_depth=5) tree.fit(X_train, y_train) # 梯度提升树 gbdt = GradientBoostingClassifier(n_estimators=100) gbdt.fit(X_train, y_train)22.4 无监督学习
22.4.1 聚类分析
from sklearn.cluster import KMeans from sklearn.metrics import silhouette_score # K-Means聚类 kmeans = KMeans(n_clusters=3, random_state=42) clusters = kmeans.fit_predict(X_scaled) # 评估轮廓系数 score = silhouette_score(X_scaled, clusters) print(f"轮廓系数: {score:.2f}")22.4.2 降维技术
from sklearn.decomposition import PCA # PCA降维 pca = PCA(n_components=2) X_pca = pca.fit_transform(X_scaled) # 可视化 plt.scatter(X_pca[:, 0], X_pca[:, 1], c=y) plt.xlabel(PC1) plt.ylabel(PC2) plt.show()22.5 模型评估与优化
22.5.1 分类评估指标
from sklearn.metrics import classification_report, roc_auc_score print(classification_report(y_test, y_pred)) print(f"AUC分数: {roc_auc_score(y_test, y_pred_proba):.2f}")22.5.2 超参数调优
from sklearn.model_selection import GridSearchCV param_grid = { n_estimators: [50, 100, 200], max_depth: [3, 5, None] } grid_search = GridSearchCV( RandomForestClassifier(), param_grid, cv=5, scoring=accuracy ) grid_search.fit(X_train, y_train) print(f"最佳参数: {grid_search.best_params_}") print(f"最佳分数: {grid_search.best_score_:.2f}")22.6 应用举例
案例1：鸢尾花分类
from sklearn.datasets import load_iris from sklearn.svm import SVC from sklearn.pipeline import make_pipeline # 加载数据 iris = load_iris() X, y = iris.data, iris.target # 创建管道 model = make_pipeline( StandardScaler(), SVC(kernel=rbf, probability=True) ) # 训练评估 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3) model.fit(X_train, y_train) print(f"测试集准确率: {model.score(X_test, y_test):.2f}") # 可视化决策边界 def plot_decision_boundary(model, X, y): # 降维到前两个特征 X = X[:, :2] model.fit(X, y) x_min, x_max = X[:, 0].min()-1, X[:, 0].max()+1 y_min, y_max = X[:, 1].min()-1, X[:, 1].max()+1 xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.02), np.arange(y_min, y_max, 0.02)) Z = model.predict(np.c_[xx.ravel(), yy.ravel()]) Z = Z.reshape(xx.shape) plt.contourf(xx, yy, Z, alpha=0.4) plt.scatter(X[:, 0], X[:, 1], c=y, s=20, edgecolor=k) plt.title(决策边界可视化) plot_decision_boundary(model, X_train, y_train)案例2：手写数字识别
from sklearn.datasets import load_digits from sklearn.neural_network import MLPClassifier # 加载数据 digits = load_digits() X, y = digits.data, digits.target # 数据可视化 fig, axes = plt.subplots(4, 4, figsize=(6, 6)) for i, ax in enumerate(axes.flat): ax.imshow(X[i].reshape(8, 8), cmap=gray) ax.set_title(f"Label: {y[i]}") ax.axis(off) # 训练MLP分类器 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) mlp = MLPClassifier(hidden_layer_sizes=(64, 32), max_iter=1000) mlp.fit(X_train, y_train) # 评估模型 print(f"测试集准确率: {mlp.score(X_test, y_test):.2f}") # 混淆矩阵 from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay cm = confusion_matrix(y_test, mlp.predict(X_test)) disp = ConfusionMatrixDisplay(cm) disp.plot() plt.title(混淆矩阵) plt.show()22.7 学习路径
22.8 学习总结
核心要点：
掌握机器学习工作流程理解不同算法适用场景熟练使用scikit-learn工具能够评估模型性能持续更新Python编程学习日志与技巧，敬请关注！
#编程# #学习# #python# #在头条记录我的2025#
