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--- |
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tags: |
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- autotrain |
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- tabular |
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- regression |
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- tabular-regression |
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datasets: |
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- autotrain-uzdtm-nwkp2/autotrain-data |
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pipeline_tag: tabular-regression |
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library_name: transformers |
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--- |
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# Model Trained Using AutoTrain |
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- Problem type: Tabular regression |
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## Validation Metrics |
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- r2: 0.5287307064016351 |
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- mse: 3.103168000915719e+19 |
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- mae: 2243863540.8 |
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- rmse: 5570608585.168877 |
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- rmsle: 8.027979609819264 |
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- loss: 5570608585.168877 |
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## Best Params |
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- learning_rate: 0.11299209471906922 |
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- reg_lambda: 1.95078305416454e-06 |
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- reg_alpha: 0.03568550183373181 |
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- subsample: 0.6486218191662874 |
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- colsample_bytree: 0.22654368454464396 |
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- max_depth: 1 |
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- early_stopping_rounds: 481 |
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- n_estimators: 20000 |
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- eval_metric: rmse |
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## Usage |
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```python |
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import json |
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import joblib |
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import pandas as pd |
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model = joblib.load('model.joblib') |
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config = json.load(open('config.json')) |
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features = config['features'] |
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# data = pd.read_csv("data.csv") |
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data = data[features] |
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predictions = model.predict(data) # or model.predict_proba(data) |
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# predictions can be converted to original labels using label_encoders.pkl |
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``` |
