[LEADERG APP] XGBoost Regression

Language:

繁體中文

 

 

 

[Introduction]

 

Use XGBoost Regression for data regression analysis.

 

LEADERG APP-XGBoost-Regression.png

 

[Interface functions and descriptions]

 

  • Dataset

The drop-down menu shows the datasets that can be analyzed.

 

Selecting dataset.png

 

  • Open the folder location of the dataset

You can quickly edit and add datasets.

 

Open data folder.png

 

  • Documentations and instructional videos

Open the official website to view the documentations and instructional videos.

 

Docs and Videos.png

 

  • Program flow

Set the parameters of each process and execute in the order of the process.

 

Program flow.png

 

 

[Operation steps and instructions]

 

1.Select dataset

From the drop-down menu, select the dataset you want to analyze.

 

Selecting dataset.png

 

Introduction to the datasets:

  • housing

Boston housing price forecast.

  • stock

Stock price prediction, input the opening, closing, high, low, and trading volume, and predict the closing price in five days.

Dataset preparation:

  • Training data set

File name: train_input.csv

File content:

  • The first column is the data index, or the time and date of the time series data. This column will be automatically ignored during analysis.
  • The first N columns are input, and the last column is output (prediction).
  • The following figure is an example of train_input.csv, 1 represents data index, 2 represents input, and 3 represents output.

 

Data preparation.png

 

  • Testing dataset

File name: inference_input.csv

File content:

Same as the training dataset.

 

2.In the program flow 1. Train, edit the training parameters and press Run to execute the training

Parameters setting：

  • Estimator

Number of gradient boosted trees (default: 1000).

​Results：

  • The comparison graph of the predicted value and the true value of the training dataset (train_input.csv).

 

Predicted value and true value of the training data.png

  • The scatter plot of the predicted value and the true value of the training dataset (train_input.csv).

 

Predicted value and true value scatter plot of the training data.png

 

  • Output predicted value (train_output.csv)

After opening the train_output.csv file, the last column is the predicted value of the training dataset (train_input.csv)

 

Predected values on training data.png

 

3.In the program flow 2. Inference, press Run to execute the inference

Results:

  • Comparison of the predicted and true values of the testing dataset (inference_input.csv)

 

Predicted value and true value of the testing data.png

 

  • The scatter plot of the predicted and true values of the testing dataset (inference_input.csv)

 

Predicted value and true value scatter plot of the testing data.png

 

  • Output predicted value (inference_output.csv)

After opening the inference _output.csv file, the last column is the predicted value of the testing dataset (inference _input.csv).

 

Predected values on testing data.png

 

 

[Download]

 

https://d.leaderg.com/XGBoostRegression/download

 

 

[Software Trial]

 

After downloading this software, please use 7zip to unzip it. If you would like to try it, please type "TRY30" to activate it. The 30 days trial is only one time for one computer.

 

 


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