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Tree-Classifier for Linear Regression (TCLR) is a novel tree model to capture the functional relationships between features and a target based on correlation.

Project description

## TCLR, Version 1, October, 2021.

Tree-Classifier for Linear Regression (TCLR) is a novel Machine learning model to capture the functional relationships between features and a target based on correlation.


Reference paper : Cao B, Yang S, Sun A, Dong Z, Zhang TY. Domain knowledge-guided interpretive machine learning - formula discovery for the oxidation behaviour of ferritic-martensitic steels in supercritical water. J Mater Inf 2022.

Doi :

Written using Python, which is suitable for operating systems, e.g., Windows/Linux/MAC OS etc.

## Installing / 安装

pip install TCLR

## Updating / 更新

pip install –upgrade TCLR

## Running / 运行 ### Ref.

output 运行结果: + classification structure tree in pdf format(Result of TCLR.pdf) 图形结果 + a folder called ‘Segmented’ for saving the subdataset of each leaf (passed test) 数据文件

note 注释:

the complete execution template can be downloaded at the Example folder 算法运行模版可在 Example 文件夹下载

graphviz (recommended installation) package is needed for generating the graphical results, which can be downloaded from the official website see user guide.(推荐安装)用于生成TCLR的图形化结果, 下载地址:

## Update log / 日志 TCLR V1.1 April, 2022. debug and print out the slopes when Pearson is used

TCLR V1.2 May, 2022. Save the dataset of each leaf

TCLR V1.3 Jun, 2022. Para: minsize - Minimum unique values for linear features of data on each leaf (Minimum number of data on each leaf before V1.3)

TCLR V1.4 Jun, 2022. + Integrated symbolic regression algorithm of gplearn package. Derive an analytical formula between features and solpes by gplearn + add a new parameter of tolerance_list, see document

TCLR V1.5 Aug, 2022. + add a new parameter of gpl_dummyfea, see document

## About / 更多 Maintained by Bin Cao. Please feel free to open issues in the Github or contact Bin Cao ( in case of any problems/comments/suggestions in using the code.

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