生成Shapash 报告,让你能够深入了解模型的性能和数据集的情况。 步骤5:从训练到部署:SmartPredictor 对象 # 使用 SmartPredictor 对象进行本地解释的摘要 predictor = xpl.to_smartpredictor() Shapash 还提供了 SmartPredictor 对象,适用于本地解释的摘要,更轻便,可满足不同操作需求。可以轻松部署,适用于 API 或...
Python Shapash 库提供了一组类和方法,用于创建解释器、生成解释器报告以及可视化模型的解释结果。以下是一些 Python Shapash 库的常见用法示例: 示例1:创建解释器和解释器报告 import shap from shapash.explainer.smart_explainer import SmartExplainer # 创建一个解释器 explainer = SmartExplainer() # 加载训练好的模...
Shapash 是一个使机器学习易于理解和解释的Python库。数据爱好者可以轻松理解并分享他们的模型。Shapash 使用 Lime 和 Shap 作为后端,只需几行代码即可显示结果。Shapash 依赖于构建机器学习模型以使结果合理的各种重要...
大家好,我是小猴子!关注和星标『机器学习研习院』,和小猴子一起研习,一起玩转机器学习!👆点击关注|选择星标|干货速递👆什么是 Shapash模型可解释性和可理解性一直是许多研究论文和开源项目的关注的重点。并且很多项目中都配备了数据专家和训练有素的专业人员。Shapash 是...
Shapash is suitable for Regression, Binary Classification and Multiclass problems. It iscompatible with numerous models, including Catboost, Xgboost, LightGBM, Sklearn Ensemble, Linear models, and SVM. For other models, solutions to integrate Shapash are available; more details can be foundhere. ...
Shapash is a Python library designed tomake machine learning interpretable and comprehensible for everyone. It offers various visualizations with clear and explicit labels that are easily understood by all. With Shapash, you can generate aWebappthat simplifies the comprehension ofinteractions between the...
沙帕什（Shapash），或沙帕舒（Shapsh(u)）（乌加里特/迦南），是迦南神话中的女太阳神， 伊勒(伊卢)(El)与阿西拉特(Asherah，又释：亚舍拉)的女儿，被称为“众神的火炬”， 在迦南神殿和腓尼基人宗教中的一位重要神祇。众神争夺对世界的统治权，沙帕什站在生命与善的力量一边，协助巴卢（阿雅利努...
Shapashis open to several ways of proceeding: It can be used to easily access results or to work on better wording. Very few arguments are required to display results. But the more you work with cleaning and documenting the dataset, the clearer the results will be for the end-user. ...
EXPLANATIONMACHINE learningHEART diseasesARTIFICIAL intelligenceDECISION makingFAILURE mode & effects analysisConsistency, scalability, and local stability properties ensure that a model or method produces reliable and predictable outcomes. The Shapash helps users understand how th...
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