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README.md

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+
+---
+tags:
+- autotrain
+- tabular
+- regression
+- tabular-regression
+datasets:
+- autotrain-dn6m8-0r8r6/autotrain-data
+---
+
+# Model Trained Using AutoTrain
+
+- Problem type: Tabular regression
+
+## Validation Metrics
+
+- r2: 0.9984788500175956
+- mse: 2424.0886496905105
+- mae: 26.34647989435065
+- rmse: 49.23503477901189
+- rmsle: 0.028818836691457343
+- loss: 49.23503477901189
+
+## Best Params
+
+- learning_rate: 0.12077471502182306
+- reg_lambda: 1.105329890230882e-08
+- reg_alpha: 1.4774392499746047
+- subsample: 0.73978922085205
+- colsample_bytree: 0.8233279668396214
+- max_depth: 4
+- early_stopping_rounds: 243
+- 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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+size 604618

target_encoders.joblib

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training_params.json

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+{"data_path": "autotrain-dn6m8-0r8r6/autotrain-data", "model": "xgboost", "username": "coan", "seed": 42, "train_split": "train", "valid_split": "validation", "project_name": "autotrain-dn6m8-0r8r6", "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": "minmax"}