项目地址:https://github.com/WillKoehrsen/feature-selector 特征选择(feature selection)是查找和选择数据集中最有用特征的过程,是机器学习流程中的一大关键步骤。不必要的特征会降低训练速度、降低模型可解释性,并且最重要的是还会降低其在测试集上的泛化表现。 目前存在一些专用型的特征选择方法,我常常要一遍又一遍...
feature_column embedding_column dimension设置多少 feature_select 特征选择(feature selection)是查找和选择数据集中最有用特征的过程,是机器学习流程中的一大关键步骤。不必要的特征会降低训练速度、降低模型可解释性,并且最重要的是还会降低其在测试集上的泛化表现。 目前存在一些专用型的特征选择方法,我常常要一遍又一...
一、实例数据集介绍 为了进行演示,我们将使用来自Kaggle的Home Credit Default Risk「家庭信用违约风险」机器学习竞赛的一个数据样本。了解该竞赛可参阅:https://towardsdatascience.com/machine-learning-kaggle-competition-part-one-getting-started-32fb9ff47426,完整数据集可在这里下载:https://www.kaggle.com/c/ho...
feature-selector除了能每次运行一个identify_*函数来选择一种类型特征外,还可以使用identify_all函数一次性选择5种类型的特征选。 # 注意:# 少了下面任何一个参数都会报错,raise ValueErrorfs.identify_all(selection_params={'missing_threshold':0.6,'correlation_threshold':0.98,'task':'classification','eval_metr...
Thus, one may use the SHAP feature importance ranking in a feature selection technique by selecting the k highest ranking features. Furthermore, this SHAP-based feature selection technique is applicable regardless of the availability of labels for data. We use the Kaggle Credit Card Fraud detection...
这是kaggle ieee的真实数据,一开始使用了大佬参数的情况下的训练过程: [200] training's auc: 0.95087 valid_1's auc: 0.907384 [400] training's auc: 0.980323 valid_1's auc: 0.926144 [600] training's auc: 0.992067 valid_1's auc: 0.935025 ...
In the context of high-dimensional credit card fraud data, researchers and practitioners commonly utilize feature selection techniques to enhance the performance of fraud detection models. This study presents a comparison in model performance using the m
feature selection methods. It is worth noting that the datasets used in Marcilio and Eler’s experiments are not highly imbalanced, and not in the credit card fraud domain. In addition, the datasets are significantly smaller in size compared to the Kaggle Credit Card Fraud Detection Dataset, ...
from sklearn.feature_selection import SelectKBest, chi2 selector = SelectKBest(score_func=chi2,...
这个开源的 Python 库可以从一组相关的表中自动构造特征。特征工具基于名为「深度特征合成」的方法(参见《Deep Feature Synthesis: Towards Automating Data Science Endeavors》),这个方法的名字听起来比其本身更高大上(这个名字源于叠加了多重特征,而不是因为使用了深度学习方法!)。