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README.md

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+
+---
+tags:
+- autotrain
+- tabular
+- regression
+- tabular-regression
+datasets:
+- autotrain-uljkp-sdhgs/autotrain-data
+---
+
+# Model Trained Using AutoTrain
+
+- Problem type: Tabular regression
+
+## Validation Metrics
+
+- r2: 0.9900762497798218
+- mse: 10317.805777253338
+- mae: 74.54517527770996
+- rmse: 101.57660053995377
+- rmsle: 0.042811727450114016
+- loss: 101.57660053995377
+
+## Best Params
+
+- learning_rate: 0.016479102091350954
+- reg_lambda: 0.3449233788687026
+- reg_alpha: 3.244557908377455e-07
+- subsample: 0.5379679408548034
+- colsample_bytree: 0.9050706969365716
+- max_depth: 4
+- early_stopping_rounds: 293
+- n_estimators: 7000
+- eval_metric: rmse
+
+## Usage
+
+```python
+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
+
+```

model.joblib

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+size 11941646

target_encoders.joblib

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+size 5

training_params.json

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+{"data_path": "autotrain-uljkp-sdhgs/autotrain-data", "model": "xgboost", "username": "as-cle-bert", "seed": 42, "train_split": "train", "valid_split": "validation", "project_name": "autotrain-uljkp-sdhgs", "push_to_hub": true, "id_column": "autotrain_id", "target_columns": ["autotrain_label"], "repo_id": "as-cle-bert/autotrain-uljkp-sdhgs", "categorical_columns": null, "numerical_columns": null, "task": "regression", "num_trials": 10, "time_limit": 600, "categorical_imputer": "most_frequent", "numerical_imputer": "median", "numeric_scaler": "robust"}