2014年6月3日 · Are there any way to make a tree plot from caret train object? If your tree plot is simple another option could be using "tree map" visualizations. Not the same as a treeplot, but may be another interesting way to visualize the model. See treemapify in ggplot. nicer looking treeplot: How do you control for how many depths you want to draw?

2024年7月10日 · The caret package in R provides a consistent interface for training, tuning, and evaluating various machine learning models, including decision trees. In this article, we will walk through the steps to create a tree plot using the caret package.

2023年9月16日 · caret是可以调用rpart包实现决策树的，但是只支持一个超参数cp，感觉不如之前介绍的好用： 以决策树为例演示超参数调优的基本方法(上) 以决策树为例演示超参数调优的基本方法(下)

The models below are available in train. The code behind these protocols can be obtained using the function getModelInfo or by going to the github repository. Documentation for the caret package.

The caret package has several functions that attempt to streamline the model building and evaluation process. The train function can be used to evaluate, using resampling, the effect of model tuning parameters on performance

For implementing Decision Tree in r, we need to import “caret” package & “rplot.plot”. As we mentioned above, caret helps to perform various tasks for our machine learning work. The “rplot.plot” package will help to get a visual plot of the decision tree.

Caret Package is a comprehensive framework for building machine learning models in R. In this tutorial, I explain nearly all the core features of the caret package and walk you through the step-by-step process of building predictive models. Be it a decision tree or xgboost, caret helps to find the optimal model in the shortest possible time.

2019年9月28日 · caret包（Classification and Regression Training）是一系列函数的集合，它试图对创建预测模型的过程进行流程化。 本系列将就数据预处理、特征选择、抽样、模型调参等进行介绍学习。

2023年8月29日 · caret是可以调用rpart包实现决策树的，但是只支持一个超参数cp，感觉不如之前介绍的好用： 以决策树为例演示超参数调优的基本方法(上) 以决策树为例演示超参数调优的基本方法(下)

The tree created with the defaults of caret is a pruned version of the tree created with the default settings of rpart. It is possible to create a less pruned tree in caret by tuning the hyper parameters. Rpart also offers options to tune the training process.