TCLR 1.4.4

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

# TCLR package 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 : http://dx.doi.org/10.20517/jmi.2022.04

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

## Installing

pip install TCLR

## Updating

pip install –upgrade TCLR

## Running

` from TCLR import TCLRalgorithm as model dataSet = "testdata.csv" # dataset name correlation = 'PearsonR(+)' minsize, threshold, mininc = 3, 0.9, 0.01 model.start(dataSet, correlation, minsize, threshold, mininc, gplearn = True) `

output: + classification structure tree in pdf format（Result of TCLR.pdf) + a folder called ‘Segmented’ for saving the subdataset of each leaf (passed test)

note:

the complete execution template can be downloaded at the Source Code folder

graphviz (recommended installation) package is needed for generating the graphical results, which can be downloaded from the official website http://www.graphviz.org/. see user guide.

## Update log 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

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