Introduction to AutoXGB

The guide for AutoXGB which includes training and running web server.

AutoXGB is simple but effective AutoML tool to train model tabular dataset directly. The AutoXGB use XGBoost for training the model, Optuna for hyperparameters optimization and FastAPI to run web app.

• auto train xgboost directly from CSV files
• auto tune xgboost using Optuna
• auto serve best xgboot model using FastAPI

pip install autoxgb

Dataset

The dataset is available at Kaggle: Adult Census Income under CC0: Public Domain. It was extracted from the 1994 Census bureau database by Ronny Kohavi and Barry Becker (Data Mining and Visualization, Silicon Graphics).The prediction task is to determine whether a person makes over $50K a year.

Training with CLI

Train the model in terminal using the autoxgb train command. The parameters are same as above.

autoxgb train \
 --train_filename binary_classification.csv \
 --output output \

Web API

By using autoxgb serve on CLI you can run localy FastAPI server.

Input

We are going to use FastAPI GUI to run predictions on model by adding /docs at the end of the link. For example 172.3.167.43:39118/docs

• workclass: "Private"
• education: "HS-grad"
• marital.status: "Widowed"
• occupation: "Transport-moving"
• relationship: "Unmarried"
• race: "White"
• sex: "Male"
• native.country: "United-States"
• age: 20
• fnlwgt: 313986
• education.num: 9
• capital.gain: 0
• capital.loss: 0
• hours.per.week: 40

Outcome

The result is <50k with confidence of 97.6% and >50k with confidence of 2.3%.