Nov 28, 2019 · 在了解sklearn决策树的random_state参数之前我们可以先看看决策树 决策树 为了要将表格（数据）转化为一棵树，决策树需要找出最佳节点和最佳的分枝方法，对分类决策树来说，衡量这 …

Jul 22, 2020 · All the examples I saw, the person that created the ML model used a random state or a random seed to stop the randomness of the process. But, in real life, when you're trying to apply a …

一、前言 在学习机器学习的过程中，常常遇到random_state这个参数，并且好多时候都是 random_state=42，感觉挺有意思的，这里做一个总结。 作用：控制随机状态。 问题：为什么需要 …

I have been using sklearn for quite some time and I understand using the same number say 100 or 200 as a value for the random_state argument will help me to produce the similar train and test data sets.

I am confused about random_state parameter in some algorithms like AdaboostClasifier, DecisionTree and so on Here is an example from sklearn.model_selection import * from sklearn.ensemble import

random_state 相当于随机数种子，你可以先看一下图片中的代码和运行结果来了解它的作用。 图中设置了 random.seed () 就相当于在 SVC 中设置了 random_state。 第一段和第二段代码完全相同，在1 …

Apr 6, 2020 · 请问大佬们，sklearn决策树里面的参数random_state是干啥用的? 如果不设置random_state居然每次都树都是不一样的。 我觉得对于相同的数据 决策树在每个节点计算的值应该 …

sklearn.tree.DecisionTreeClassifier 上提供了一个叫 splitter 的参数，random_state主要就是为这个参数而准备的。 从文档可以看到，这个参数的值可以是“best”或者”random“，两者都会带来一定的随机性 …

May 10, 2022 · It appears that by using random_state=42, I unknowingly chose the random state (between 0 and 99) with the largest difference between the grid search best score, and the specificity …

Dec 5, 2022 · How could I randomly split a data matrix and the corresponding label vector into a X_train, X_test, X_val, y_train, y_test, y_val with scikit-learn? As far as I know, sklearn.model_selection.