A simple example of Tabular data Binary Classification using LightGBM. 🐶
pip install pandas numpy scipy scikit-learn lightgbmpython main.py --thres=0.2 --data_path=datasets/bank-additional-full.csv🌝 output is something like this, you can optimizing the params to get better results.
[LightGBM] [Info] Total Bins 675
[LightGBM] [Info] Number of data points in the train set: 32950, number of used features: 10
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.113475 -> initscore=-2.055727
[LightGBM] [Info] Start training from score -2.055727
Training until validation scores don't improve for 10 rounds
Early stopping, best iteration is:
[51] valid_0's auc: 0.947001
Saving LightGBM model...
Start predicting...
Acc: 0.908, F1: 0.645, Precision: 0.556, Recall: 0.768
Confusion Matrix:
[[6785 552]
[ 209 692]]
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