[LEADERG APP] XGBoost Regression

[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