模型选择#

sklearn.model_selection module.

平衡模型复杂性和交叉验证分数

Balance model complexity and cross-validated score

比较随机搜索和网格搜索用于超参数估计

Comparing randomized search and grid search for hyperparameter estimation

网格搜索和连续减半的比较

Comparison between grid search and successive halving

混淆矩阵

Confusion matrix

具有交叉验证的网格搜索自定义改装策略

Custom refit strategy of a grid search with cross-validation

基于cross_val_score和GridSearchCV的多度量评估演示

Demonstration of multi-metric evaluation on cross_val_score and GridSearchCV

模型正规化对训练和测试误差的影响

Effect of model regularization on training and test error

嵌套与非嵌套交叉验证

Nested versus non-nested cross-validation

绘制交叉验证预测

Plotting Cross-Validated Predictions

绘制学习曲线并检查模型的可扩展性

Plotting Learning Curves and Checking Models' Scalability

事后调整决策函数的截止点

Post-hoc tuning the cut-off point of decision function

pr曲线

Precision-Recall

具有交叉验证的接收器工作特性(ROC)

Receiver Operating Characteristic (ROC) with cross validation

用于文本特征提取和评估的样本管道

Sample pipeline for text feature extraction and evaluation

使用网格搜索进行模型统计比较

Statistical comparison of models using grid search

连续减半迭代

Successive Halving Iterations

用排列测试分类分数的重要性

Test with permutations the significance of a classification score

不足与过度贴合

Underfitting vs. Overfitting

在scikit-learn中可视化交叉验证行为

Visualizing cross-validation behavior in scikit-learn