model_evaluation

# 模型选择与评估参考 ## 概述 使用 scikit-learn 的模型选择工具评估模型、调优超参数并选择最佳模型的综合指南。 ## 训练集-测试集拆分 ### 基本拆分 python from sklearn.model_selection import train_test_split # 基本拆分（默认 75/25） X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42) # 分层拆分（保留类别分布） X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.25, stratify=y, random_state=42 ) # 三路拆分（训练集/验证集/测试集） X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.3, random_state=42) X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42) ## 交叉验证 ### 交叉验证策略 **KFold** - 标准 k 折交叉验证 - 将数据拆分为 k 个连续折叠 python from sklearn.model_selection import KFold kf = KFold(n_splits=5, shuffle=True, random_state=42) for train_idx, val_idx in kf.split(X): X_train, X_val = X[train_idx], X[val_idx] y_train, y_val = y[train_idx], y[val_idx] **StratifiedKFold** - 在每个折叠中保留类别分布 - 用于不平衡分类 python from sklearn.model_selection import StratifiedKFold skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=42) for train_idx, val_idx in skf.split(X, y): X_train, X_val = X[train_idx], X[val_idx] y_train, y_val = y[train_idx], y[val_idx] **TimeSeriesSplit** - 用于时间序列数据 - 尊重时间顺序 python from sklearn.model_selection import TimeSeriesSplit tscv = TimeSeriesSplit(n_splits=5) for train_idx, val_idx in tscv.split(X): X_train, X_val = X[train_idx], X[val_idx] y_train, y_val = y[train_idx], y[val_idx] **GroupKFold** - 确保来自同一组的样本不会同时出现在训练集和验证集中 - 当样本不独立时使用 python from sklearn.model_selection import GroupKFold gkf = GroupKFold(n_splits=5) for train_idx, val_idx in gkf.split(X, y, groups=group_ids): X_train, X_val = X[train_idx], X[val_idx] y_train, y_val = y[train_idx], y[val_idx] **LeaveOneOut (LOO)** - 每个样本作为一次验证集 - 用于非常小的数据集 - 计算成本高 python from sklearn.model_selection import LeaveOneOut loo = LeaveOneOut() for train_idx, val_idx in loo.split(X): X_train, X_