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autotrain-fbyte-l9vfz
jul-fls/autotrain-fbyte-l9vfz
autotrain-fbyte-l9vfz is a tabular regression model from jul-fls. Use it for the tabular regression task on the model card, and read the license before you ship it in a product.
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Updated Apr 14, 2024
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.joblib203 KB · 98%
At a glance
- Task
- Tabular Regression
- Access
- Public
- Created
- Apr 14, 2024
- Updated
- Apr 14, 2024
- SHA
- 0ec2b309
Model card
From the Hugging Face model README
Model Trained Using AutoTrain
- Problem type: Tabular regression
Validation Metrics
- r2: 0.33913886288678097
- mse: 0.13878083879377598
- mae: 0.2991213083267212
- rmse: 0.37253300363025016
- rmsle: 0.15062628429771513
- loss: 0.37253300363025016
Best Params
- learning_rate: 0.017092100292696658
- reg_lambda: 2.08790995148619e-05
- reg_alpha: 5.763917500537152e-06
- subsample: 0.38707603768089427
- colsample_bytree: 0.547982260603956
- max_depth: 1
- early_stopping_rounds: 102
- n_estimators: 20000
- eval_metric: rmse
Usage
import json
import joblib
import pandas as pd
model = joblib.load('model.joblib')
config = json.load(open('config.json'))
features = config['features']
# data = pd.read_csv("data.csv")
data = data[features]
predictions = model.predict(data) # or model.predict_proba(data)
# predictions can be converted to original labels using label_encoders.pkl
- Task
- Tabular Regression
- Created
- Apr 14, 2024
- Updated
- Apr 14, 2024