Hands-On Machine Learning with Scikit-Learn & TensorFlow Exercise Q&A Chapter07

最新推荐文章于 2022-09-19 16:10:40 发布

Leonardo Liu

最新推荐文章于 2022-09-19 16:10:40 发布

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分类专栏： Python Scikit-Learn 机器学习 Hands-On ML with sklearn & TensorFlow Exercise Q&A 文章标签： Machine Learning HandsOn Ensemble Learning

本文链接：https://blog.youkuaiyun.com/leowinbow/article/details/88748198

本文是《Hands-On Machine Learning with Scikit-Learn & TensorFlow》第七章练习问答，涵盖了集成学习中的投票分类器、硬软投票的区别、并行训练、出袋评估、额外树等概念。问答详细解释了如何通过结合不同模型提升性能，以及如何调整参数以应对过拟合和欠拟合问题。

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Q1. If you have trained five different models on the exact same training data, and they all achieve 95% precision, is there any chance that you can combine these models to get better results? If so, how? If not, why?

A1: I can try combining them into a voting ensemble. If the models are very different, this ensemble methods will surely be better!

Q2. What is the difference between hard and soft voting classifiers?

A2: A hard voting classifier just counts the votes of each classifier in the ensemble and picks the class that gets the most votes; while a soft voting classifier computes the average estimated class probability for each class and picks the class with the highest probability.

Q3. Is it possible to speed up training of a bagging ensemble by distributing it across multiple servers? What about pasting ensembles, boosting ensembles, random forests, or stacking ensembles?

A3: It is possible to speed up traini