MahsaShirazian
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Browse files- README.md +56 -0
- model.joblib +3 -0
- target_encoders.joblib +3 -0
- training_params.json +1 -0
README.md
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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-eno5u-hy49n/autotrain-data
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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: 0.9988073651686958
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- mse: 337854.8880689932
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- mae: 398.9371570016889
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- rmse: 581.2528606974706
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- rmsle: 0.01184006241771929
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- loss: 581.2528606974706
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## Best Params
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- learning_rate: 0.09168110099890295
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- reg_lambda: 0.11592417839619104
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- reg_alpha: 0.0010410090431649107
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- subsample: 0.357364833100802
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- colsample_bytree: 0.9155936367985213
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- max_depth: 7
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- early_stopping_rounds: 158
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- n_estimators: 15000
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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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model.joblib
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version https://git-lfs.github.com/spec/v1
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oid sha256:aa122a3f1e798145ee2de7b9364e9c2419af9a8bfe1ae4aa71164d0e2e9f7303
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size 1505380
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target_encoders.joblib
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version https://git-lfs.github.com/spec/v1
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oid sha256:926248e52d1fa532c317e37da24ed652ae64110f8219cb5e061668bd3091f048
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size 5
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training_params.json
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{"data_path": "autotrain-eno5u-hy49n/autotrain-data", "model": "xgboost", "username": "MahsaShirazian", "seed": 42, "train_split": "train", "valid_split": "validation", "project_name": "autotrain-eno5u-hy49n", "push_to_hub": true, "id_column": "autotrain_id", "target_columns": ["autotrain_label"], "categorical_columns": null, "numerical_columns": null, "task": "regression", "num_trials": 10, "time_limit": 600, "categorical_imputer": "most_frequent", "numerical_imputer": "median", "numeric_scaler": "robust"}
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