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Titlebook: Explainable AI Recipes; Implement Solutions Pradeepta Mishra Book 2023 Pradeepta Mishra 2023 Explainable AI.Python.Artificial Intelligence

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11#
發(fā)表于 2025-3-23 13:17:14 | 只看該作者
Handbook of Mathematical Geodesy the case of multinomial output variables, the outcome can be more than two, such as high, medium, and low. In this chapter, we are going to use explainable libraries to explain a regression model and a classification model, while training a linear model.
12#
發(fā)表于 2025-3-23 14:01:46 | 只看該作者
13#
發(fā)表于 2025-3-23 18:20:19 | 只看該作者
Kathrin Natterer (née Greuling)ple models are being trained, and each model generates a classification. The final model takes into account the majority voting rule criteria to decide the final prediction. Because of the nature of ensemble models, these are harder to explain to end users. That is why we need frameworks that can explain the ensemble models.
14#
發(fā)表于 2025-3-24 01:06:49 | 只看該作者
Ethical Issues in Media Psychology,rain a machine learning model to perform text classification such as customer review classification, feedback classification, newsgroup classification, etc. In this chapter, we will be using explainable libraries to explain the predictions or classifications.
15#
發(fā)表于 2025-3-24 03:30:24 | 只看該作者
Explainability for Linear Supervised Models, the case of multinomial output variables, the outcome can be more than two, such as high, medium, and low. In this chapter, we are going to use explainable libraries to explain a regression model and a classification model, while training a linear model.
16#
發(fā)表于 2025-3-24 08:39:48 | 只看該作者
17#
發(fā)表于 2025-3-24 11:56:23 | 只看該作者
18#
發(fā)表于 2025-3-24 17:32:48 | 只看該作者
19#
發(fā)表于 2025-3-24 22:39:36 | 只看該作者
20#
發(fā)表于 2025-3-25 00:37:08 | 只看該作者
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