[LEADERG App4AI] XGBoost Classification







Use XGBoost Classification for data regression analysis.


LEADERG App4AI-XGBoost-Classification.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:

  • 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.


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).


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


The accuracy of the model to 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


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


The accuracy of the model to 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




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