Area (Language):

大陆港澳 (简体中文)

台灣 (繁體中文)

 

 

[Introduction]

 

Use XGBoost Classification for data regression analysis.

 

 

[Interface functions and descriptions]

 

• Dataset

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

 

 

• Open the folder location of the dataset

You can quickly edit and add datasets.

 

 

• Documentations and instructional videos

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

 

 

• Program flow

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

 

 

 

[Operation steps and instructions]

 

1.Select dataset

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

 

 

Introduction to the datasets:

• titanic

Predict the survival of Titanic passengers.

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.

 

 

• 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 prediction accuracy of the trained model against the training dataset (train_input.csv)

 

 

• 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)

 

 

 

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

Results:

• The prediction accuracy of the trained model against the testing dataset (inference_input.csv).

 

 

• 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).