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README.md

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+
+---
+tags:
+- autotrain
+- tabular
+- regression
+- tabular-regression
+datasets:
+- Notaspy1234/autotrain-data-Autotrain3
+---
+
+# Model Trained Using AutoTrain
+
+- Problem type: Tabular regression
+
+## Validation Metrics
+
+- r2: 0.9753017864826334
+- mse: 0.3290419495851166
+- mae: 0.47130432128906286
+- rmse: 0.5736217826975512
+- rmsle: 0.057378419858521094
+- loss: 0.5736217826975512
+
+## Best Params
+
+- learning_rate: 0.022993157585548683
+- reg_lambda: 0.0030417803769039035
+- reg_alpha: 0.17755049688249555
+- subsample: 0.33171622212758833
+- colsample_bytree: 0.10545502763287017
+- max_depth: 8
+- early_stopping_rounds: 387
+- n_estimators: 15000
+- 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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+oid sha256:e223bed5a161c0d72b0c1254e54173b3935a89b04a60465c8bc854c4eea69699
+size 922216

target_encoders.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:926248e52d1fa532c317e37da24ed652ae64110f8219cb5e061668bd3091f048
+size 5

training_params.json

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+{"data_path": "Notaspy1234/autotrain-data-Autotrain3", "model": "xgboost", "username": "Notaspy1234", "seed": 42, "train_split": "train", "valid_split": "validation", "project_name": "/tmp/model", "push_to_hub": true, "id_column": "autotrain_id", "target_columns": ["autotrain_label"], "repo_id": "Notaspy1234/Autotrain3-0", "categorical_columns": null, "numerical_columns": null, "task": "regression", "num_trials": 10, "time_limit": 1800, "categorical_imputer": null, "numerical_imputer": "mean", "numeric_scaler": "standard"}