文件名称：xgboost_with_python.zip
文件大小：1.18MB
文件格式：ZIP
更新时间：2023-04-02 14:59:27
machine learning
Welcome to XG'Boost With Python. This book is your guide to fast gradient boosting in Python You will discover the XGroost. Python library for gradient boosting and how to use it to develop and evaluate gradient boosting models. In this book you will discover the techniques, recipes and skills with XGBoost that you can then bring to your own machine learning projects Gradient boosting does have a some fascinating math under the covers, but you do not need to know it to be able to pick it up as a tool and wield it on important projects to deliver real value. From the applied perspective, gradient boosting is quite a shallow field and a motivated developer can quickly pick it up and start making very real and impactful contributions.
【文件预览】：
README.txt
xgboost_with_python.pdf
code
----chapter_10()
--------learning_curves.py(2KB)
--------evaluate_validation_set.py(845B)
--------pima-indians-diabetes.csv(23KB)
--------early_stopping.py(893B)
----chapter_06()
--------cross_validation.py(547B)
--------train_test_split.py(777B)
--------pima-indians-diabetes.csv(23KB)
--------stratified_cross_validation.py(578B)
----chapter_16()
--------tune_row_sample_rate.py(1KB)
--------tune_column_sample_rate_split.py(1KB)
--------tune_column_sample_rate_bytree.py(1KB)
----chapter_11()
--------eval_parallel_cv_and_xgboost.py(1KB)
--------eval_num_threads.py(860B)
----chapter_07()
--------plot_tree.py(395B)
--------pima-indians-diabetes.csv(23KB)
--------plot_tree-left-to-right.py(422B)
----chapter_09()
--------feature_selection.py(1KB)
--------manual_feature_importance.py(484B)
--------automatic_feature_importance.py(443B)
--------pima-indians-diabetes.csv(23KB)
----chapter_14()
--------tune_num_trees_and_depth.py(2KB)
--------tune_depth.py(1KB)
--------tune_trees.py(1KB)
----chapter_08()
--------serialize_with_pickle.py(1KB)
--------serialize_with_joblib.py(1KB)
--------pima-indians-diabetes.csv(23KB)
----chapter_05()
--------iris_label_encode.py(985B)
--------horse-colic.csv(25KB)
--------datasets-uci-breast-cancer.csv(24KB)
--------horse_colic_missing.py(1KB)
--------iris.csv(4KB)
--------breast_one_hot.py(2KB)
--------horse_colic_missing_imputer.py(1KB)
----chapter_15()
--------tune_learning_rate_and_num_trees.py(2KB)
--------tune_learning_rate.py(1KB)
--------plot_performance.py(343B)
----chapter_12()
--------check_num_threads.py(630B)
----chapter_04()
--------first_model.py(811B)
--------pima-indians-diabetes.csv(23KB)