Language:
繁體中文
[Introduction]
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]
1.Select dataset
From the drop-down menu, select the dataset you want to analyze.
Introduction to the datasets:
Predict the survival of Titanic passengers.
Dataset preparation:
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.
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：
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).
Thanks for our customers
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