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README.md

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+
+---
+tags:
+- autotrain
+- tabular
+- regression
+- tabular-regression
+datasets:
+- will-clarke/autotrain-data-km3p-5cou-dikk
+---
+
+# Model Trained Using AutoTrain
+
+- Problem type: Tabular regression
+
+## Validation Metrics
+
+- r2: -0.008598428559009497
+- mse: 598.976166598342
+- mae: 9.062458591043514
+- rmse: 24.473989593001424
+- rmsle: 1.2592486785782957
+- loss: 24.473989593001424
+
+## Best Params
+
+- learning_rate: 0.05243299592316927
+- reg_lambda: 6.717966298706072e-08
+- reg_alpha: 1.6032915106085746e-08
+- subsample: 0.5114836334096384
+- colsample_bytree: 0.42603286105240046
+- max_depth: 1
+- early_stopping_rounds: 455
+- n_estimators: 20000
+- 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 1254219

target_encoders.joblib

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training_params.json

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+{"data_path": "will-clarke/autotrain-data-km3p-5cou-dikk", "model": "xgboost", "username": "will-clarke", "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": "will-clarke/km3p-5cou-dikk-0", "categorical_columns": null, "numerical_columns": null, "task": "regression", "num_trials": 100, "time_limit": 3600, "categorical_imputer": null, "numerical_imputer": "mean", "numeric_scaler": "standard"}