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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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- nicoler229/autotrain-data-renp-vcyx-5hff |
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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: 0.8987710422047952 |
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- mse: 15.386801584871137 |
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- mae: 3.1008129119873047 |
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- rmse: 3.9226013798079378 |
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- rmsle: 0.049014949862444 |
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- loss: 3.9226013798079378 |
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## Best Params |
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- learning_rate: 0.09858308825036341 |
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- reg_lambda: 1.7244892825164977e-06 |
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- reg_alpha: 0.004880162297132929 |
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- subsample: 0.5918267532876357 |
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- colsample_bytree: 0.6228647593929555 |
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- max_depth: 8 |
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- early_stopping_rounds: 440 |
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- n_estimators: 7000 |
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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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``` |
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