Use XGBoost Classification for data regression analysis.
[Interface functions and descriptions]
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.
Set the parameters of each process and execute in the order of the process.
[Operation steps and instructions]
From the drop-down menu, select the dataset you want to analyze.
Introduction to the datasets:
Predict the survival of Titanic passengers.
File name: train_input.csv
- 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.
File name: inference_input.csv
Same as the training dataset.
2.In the program flow 1. Train, edit the training parameters and press Run to execute the training
Number of gradient boosted trees (default: 1000).
- 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
- 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).
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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