决策树分类器花了16分钟才能适合
因此，由于某些原因，我的笔记本电脑将数据放到了DecisionTreeClalefier中。通常需要大约1秒才能适合其他类型的机器学习模型。有人可以帮助我解决这里发生的事情吗？我不确定我应该提供哪些信息来帮助这一点。随意问！
我的猜测是与编码器变换语法有关，我不知道如何从许多在线搜索中进行修复。它表明我的方法会导致性能差，但是该语法来自库本身，因此我不知道如何更改内部的代码。
So, for some reasons, It took my laptop to 16min to fit data into DecisionTreeClassifier. It usually take like 1 sec to fit into other type of machine learning model. Anyone can help me with what is happening here? I am not sure what information should I provide to help with this. Feel free to ask away!
My guess is it has to do with encoder transform syntax, which I have no idea how to fix from many online searches. It shows that my approach will lead to poor performance, but this syntax is from the library itself, so I do not know how to change the code inside.
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大概，您的数据集在编码后具有更多的列，这会导致性能差和较长的培训时间。您可以在编码后检查数据集的列，以确保。
Probably, your dataset has way more columns after encoding, which leads to poor performance and a long training time. You can check the columns of the dataset after encoding to be sure.