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有没有一种方法可以用决策树/随机森林进行迁移学习？

[英]Is there a method to do transfer learning with decision trees/random forest?

原文 2021-05-11 07:53:07 4 1 python/ scikit-learn/ random-forest/ decision-tree

Is there a way to do transfer learning with a decision tree or a random forest model?有没有办法用决策树或随机森林 model 进行迁移学习？ Concretely, I was wondering if there is a good and easy way of doing so in Python with a model trained with Scikit-learn .具体来说，我想知道在 Python 中是否有一种很好且简单的方法可以使用 model 训练Scikit-learn 。

All I can think of is training a random forest on the original dataset, and when new data arrive, train new trees and add these to your model.我能想到的就是在原始数据集上训练一个随机森林，当新数据到达时，训练新树并将它们添加到您的 model 中。 However, I wonder if this is a good approach and if there are any other better methods.但是，我想知道这是否是一个好方法，是否还有其他更好的方法。

1 个解决方案

Possible but not practical.可能但不实用。

The aim of transfer learning is to give initial weights to the deep learning (DL) models and speed up the learning process.迁移学习的目的是为深度学习（DL）模型赋予初始权重并加快学习过程。 You can find that given one same DL model, when applied to similar applications such as computer vision, all the generated DL models have relative range of values although not entirely significant but better than randomization of weights or even sparse.您会发现，给定一个相同的 DL model，当应用于计算机视觉等类似应用时，所有生成的 DL 模型都有相对的取值范围，虽然并不完全显着，但优于权重随机化甚至稀疏。

Machine learning (ML) models have shallow architectures and you can simply use randomization of weights to train-test the model.机器学习 (ML) 模型具有浅层架构，您可以简单地使用权重随机化来训练测试 model。

If you insist to do transfer learning, you can use the weights of the previous model you are referred to but make sure you have same input-output data and configure your model accordingly.如果您坚持进行迁移学习，您可以使用您之前提到的 model 的权重，但请确保您具有相同的输入输出数据并相应地配置您的 model。 You will notice that you cannot find transfer learning for ML anywhere and easier, because it is not practical.您会注意到，您无法在任何地方找到用于 ML 的迁移学习并且更容易，因为它不实用。 Better learn from scratch.最好从头开始学习。