💎一站式轻松地调用各大LLM模型接口，支持GPT4、智谱、星火、月之暗面及文生图 广告
# 3. 模型选择和评估 - [3.1. 交叉验证：评估估算器的表现](modules/cross_validation.html) - [3.1.1. 计算交叉验证的指标](modules/cross_validation.html#id2) - [3.1.1.1. cross\_validate 函数和多度量评估](modules/cross_validation.html#cross-validate) - [3.1.1.2. 通过交叉验证获取预测](modules/cross_validation.html#id3) - [3.1.2. 交叉验证迭代器](modules/cross_validation.html#id4) - [3.1.3. 交叉验证迭代器–循环遍历数据](modules/cross_validation.html#iid-cv) - [3.1.3.1. K 折](modules/cross_validation.html#k) - [3.1.3.2. 重复 K-折交叉验证](modules/cross_validation.html#id6) - [3.1.3.3. 留一交叉验证 (LOO)](modules/cross_validation.html#loo) - [3.1.3.4. 留 P 交叉验证 (LPO)](modules/cross_validation.html#p-lpo) - [3.1.3.5. 随机排列交叉验证 a.k.a. Shuffle & Split](modules/cross_validation.html#a-k-a-shuffle-split) - [3.1.4. 基于类标签、具有分层的交叉验证迭代器](modules/cross_validation.html#id7) - [3.1.4.1. 分层 k 折](modules/cross_validation.html#id8) - [3.1.4.2. 分层随机 Split](modules/cross_validation.html#split) - [3.1.5. 用于分组数据的交叉验证迭代器](modules/cross_validation.html#group-cv) - [3.1.5.1. 组 k-fold](modules/cross_validation.html#k-fold) - [3.1.5.2. 留一组交叉验证](modules/cross_validation.html#id10) - [3.1.5.3. 留 P 组交叉验证](modules/cross_validation.html#p) - [3.1.5.4. Group Shuffle Split](modules/cross_validation.html#group-shuffle-split) - [3.1.6. 预定义的折叠 / 验证集](modules/cross_validation.html#id11) - [3.1.7. 交叉验证在时间序列数据中应用](modules/cross_validation.html#timeseries-cv) - [3.1.7.1. 时间序列分割](modules/cross_validation.html#id13) - [3.1.8. A note on shuffling](modules/cross_validation.html#a-note-on-shuffling) - [3.1.9. 交叉验证和模型选择](modules/cross_validation.html#id14) - [3.2. 调整估计器的超参数](modules/grid_search.html) - [3.2.1. 网格追踪法–穷尽的网格搜索](modules/grid_search.html#id3) - [3.2.2. 随机参数优化](modules/grid_search.html#randomized-parameter-search) - [3.2.3. 参数搜索技巧](modules/grid_search.html#grid-search-tips) - [3.2.3.1. 指定目标度量](modules/grid_search.html#gridsearch-scoring) - [3.2.3.2. 为评估指定多个指标](modules/grid_search.html#multimetric-grid-search) - [3.2.3.3. 复合估计和参数空间](modules/grid_search.html#id16) - [3.2.3.4. 模型选择：开发和评估](modules/grid_search.html#id18) - [3.2.3.5. 并行机制](modules/grid_search.html#id19) - [3.2.3.6. 对故障的鲁棒性](modules/grid_search.html#id20) - [3.2.4. 暴力参数搜索的替代方案](modules/grid_search.html#alternative-cv) - [3.2.4.1. 模型特定交叉验证](modules/grid_search.html#id22) - [3.2.4.1.1. `sklearn.linear_model`.ElasticNetCV](modules/generated/sklearn.linear_model.ElasticNetCV.html) - [3.2.4.1.2. `sklearn.linear_model`.LarsCV](modules/generated/sklearn.linear_model.LarsCV.html) - [3.2.4.1.3. `sklearn.linear_model`.LassoCV](modules/generated/sklearn.linear_model.LassoCV.html) - [3.2.4.1.3.1. Examples using `sklearn.linear_model.LassoCV`](modules/generated/sklearn.linear_model.LassoCV.html#examples-using-sklearn-linear-model-lassocv) - [3.2.4.1.4. `sklearn.linear_model`.LassoLarsCV](modules/generated/sklearn.linear_model.LassoLarsCV.html) - [3.2.4.1.4.1. Examples using `sklearn.linear_model.LassoLarsCV`](modules/generated/sklearn.linear_model.LassoLarsCV.html#examples-using-sklearn-linear-model-lassolarscv) - [3.2.4.1.5. `sklearn.linear_model`.LogisticRegressionCV](modules/generated/sklearn.linear_model.LogisticRegressionCV.html) - [3.2.4.1.6. `sklearn.linear_model`.MultiTaskElasticNetCV](modules/generated/sklearn.linear_model.MultiTaskElasticNetCV.html) - [3.2.4.1.7. `sklearn.linear_model`.MultiTaskLassoCV](modules/generated/sklearn.linear_model.MultiTaskLassoCV.html) - [3.2.4.1.8. `sklearn.linear_model`.OrthogonalMatchingPursuitCV](modules/generated/sklearn.linear_model.OrthogonalMatchingPursuitCV.html) - [3.2.4.1.8.1. Examples using `sklearn.linear_model.OrthogonalMatchingPursuitCV`](modules/generated/sklearn.linear_model.OrthogonalMatchingPursuitCV.html#examples-using-sklearn-linear-model-orthogonalmatchingpursuitcv) - [3.2.4.1.9. `sklearn.linear_model`.RidgeCV](modules/generated/sklearn.linear_model.RidgeCV.html) - [3.2.4.1.9.1. Examples using `sklearn.linear_model.RidgeCV`](modules/generated/sklearn.linear_model.RidgeCV.html#examples-using-sklearn-linear-model-ridgecv) - [3.2.4.1.10. `sklearn.linear_model`.RidgeClassifierCV](modules/generated/sklearn.linear_model.RidgeClassifierCV.html) - [3.2.4.2. 信息标准](modules/grid_search.html#id23) - [3.2.4.2.1. `sklearn.linear_model`.LassoLarsIC](modules/generated/sklearn.linear_model.LassoLarsIC.html) - [3.2.4.2.1.1. Examples using `sklearn.linear_model.LassoLarsIC`](modules/generated/sklearn.linear_model.LassoLarsIC.html#examples-using-sklearn-linear-model-lassolarsic) - [3.2.4.3. 出袋估计](modules/grid_search.html#out-of-bag) - [3.2.4.3.1. `sklearn.ensemble`.RandomForestClassifier](modules/generated/sklearn.ensemble.RandomForestClassifier.html) - [3.2.4.3.1.1. Examples using `sklearn.ensemble.RandomForestClassifier`](modules/generated/sklearn.ensemble.RandomForestClassifier.html#examples-using-sklearn-ensemble-randomforestclassifier) - [3.2.4.3.2. `sklearn.ensemble`.RandomForestRegressor](modules/generated/sklearn.ensemble.RandomForestRegressor.html) - [3.2.4.3.2.1. Examples using `sklearn.ensemble.RandomForestRegressor`](modules/generated/sklearn.ensemble.RandomForestRegressor.html#examples-using-sklearn-ensemble-randomforestregressor) - [3.2.4.3.3. `sklearn.ensemble`.ExtraTreesClassifier](modules/generated/sklearn.ensemble.ExtraTreesClassifier.html) - [3.2.4.3.3.1. Examples using `sklearn.ensemble.ExtraTreesClassifier`](modules/generated/sklearn.ensemble.ExtraTreesClassifier.html#examples-using-sklearn-ensemble-extratreesclassifier) - [3.2.4.3.4. `sklearn.ensemble`.ExtraTreesRegressor](modules/generated/sklearn.ensemble.ExtraTreesRegressor.html) - [3.2.4.3.4.1. Examples using `sklearn.ensemble.ExtraTreesRegressor`](modules/generated/sklearn.ensemble.ExtraTreesRegressor.html#examples-using-sklearn-ensemble-extratreesregressor) - [3.2.4.3.5. `sklearn.ensemble`.GradientBoostingClassifier](modules/generated/sklearn.ensemble.GradientBoostingClassifier.html) - [3.2.4.3.5.1. Examples using `sklearn.ensemble.GradientBoostingClassifier`](modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#examples-using-sklearn-ensemble-gradientboostingclassifier) - [3.2.4.3.6. `sklearn.ensemble`.GradientBoostingRegressor](modules/generated/sklearn.ensemble.GradientBoostingRegressor.html) - [3.2.4.3.6.1. Examples using `sklearn.ensemble.GradientBoostingRegressor`](modules/generated/sklearn.ensemble.GradientBoostingRegressor.html#examples-using-sklearn-ensemble-gradientboostingregressor) - [3.3. 模型评估: 量化预测的质量](modules/model_evaluation.html) - [3.3.1. `scoring` 参数: 定义模型评估规则](modules/model_evaluation.html#scoring) - [3.3.1.1. 常见场景: 预定义值](modules/model_evaluation.html#id2) - [3.3.1.2. 根据 metric 函数定义您的评分策略](modules/model_evaluation.html#metric) - [3.3.1.3. 实现自己的记分对象](modules/model_evaluation.html#diy-scoring) - [3.3.1.4. 使用多个指数评估](modules/model_evaluation.html#multimetric-scoring) - [3.3.2. 分类指标](modules/model_evaluation.html#classification-metrics) - [3.3.2.1. 从二分到多分类和 multilabel](modules/model_evaluation.html#multilabel) - [3.3.2.2. 精确度得分](modules/model_evaluation.html#accuracy-score) - [3.3.2.3. Cohen’s kappa](modules/model_evaluation.html#cohen-s-kappa) - [3.3.2.4. 混淆矩阵](modules/model_evaluation.html#confusion-matrix) - [3.3.2.5. 分类报告](modules/model_evaluation.html#classification-report) - [3.3.2.6. 汉明损失](modules/model_evaluation.html#hamming-loss) - [3.3.2.7. Jaccard 相似系数 score](modules/model_evaluation.html#jaccard-score) - [3.3.2.8. 精准，召回和 F-measures](modules/model_evaluation.html#f-measures) - [3.3.2.8.1. 二分类](modules/model_evaluation.html#id14) - [3.3.2.8.2. 多类和多标签分类](modules/model_evaluation.html#id15) - [3.3.2.9. Hinge loss](modules/model_evaluation.html#hinge-loss) - [3.3.2.10. Log 损失](modules/model_evaluation.html#log) - [3.3.2.11. 马修斯相关系数](modules/model_evaluation.html#matthews-corrcoef) - [3.3.2.12. Receiver operating characteristic (ROC)](modules/model_evaluation.html#receiver-operating-characteristic-roc) - [3.3.2.13. 零一损失](modules/model_evaluation.html#zero-one-loss) - [3.3.2.14. Brier 分数损失](modules/model_evaluation.html#brier) - [3.3.3. 多标签排名指标](modules/model_evaluation.html#multilabel-ranking-metrics) - [3.3.3.1. 覆盖误差](modules/model_evaluation.html#coverage-error) - [3.3.3.2. 标签排名平均精度](modules/model_evaluation.html#label-ranking-average-precision) - [3.3.3.3. 排序损失](modules/model_evaluation.html#label-ranking-loss) - [3.3.4. 回归指标](modules/model_evaluation.html#regression-metrics) - [3.3.4.1. 解释方差得分](modules/model_evaluation.html#explained-variance-score) - [3.3.4.2. 平均绝对误差](modules/model_evaluation.html#mean-absolute-error) - [3.3.4.3. 均方误差](modules/model_evaluation.html#mean-squared-error) - [3.3.4.4. 均方误差对数](modules/model_evaluation.html#mean-squared-log-error) - [3.3.4.5. 中位绝对误差](modules/model_evaluation.html#median-absolute-error) - [3.3.4.6. R² score, 可决系数](modules/model_evaluation.html#r2-score) - [3.3.5. 聚类指标](modules/model_evaluation.html#clustering-metrics) - [3.3.6. 虚拟估计](modules/model_evaluation.html#dummy-estimators) - [3.4. 模型持久化](modules/model_persistence.html) - [3.4.1. 持久化示例](modules/model_persistence.html#id2) - [3.4.2. 安全性和可维护性的局限性](modules/model_persistence.html#persistence-limitations) - [3.5. 验证曲线: 绘制分数以评估模型](modules/learning_curve.html) - [3.5.1. 验证曲线](modules/learning_curve.html#validation-curve) - [3.5.2. 学习曲线](modules/learning_curve.html#learning-curve)