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摘要
En 中文
I present two related commands, r_ml_stata_cv and c_ml_stata_cv, for fitting popular machine learning methods in both a regression and a classification setting. Using the recent Stata/Python integration platform introduced in Stata 16, these commands provide hyperparameters' optimal tuning via K-fold cross-validation using grid search. More specifically, they use the Python Scikitlearn application programming interface to carry out both cross-validation and outcome/label prediction.
Keyword:
pr0076
r_ml_stata_cv
c_ml_stata_cv
get_test_train
machine learning
Python
optimal tuning
期刊
S
IF:
2.4
论文数:
1.2K
被引数:
8.4K
机构
引用论文
Gradient-based learning applied to document recognition基于梯度的学习在文档识别中的应用
PROCEEDINGS OF THE IEEE
IF25.9

