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README.md

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+
+---
+tags:
+- autotrain
+- tabular
+- regression
+- tabular-regression
+datasets:
+- gvozdev/autotrain-data-autotrain-ratings
+---
+
+# Model Trained Using AutoTrain
+
+- Problem type: Tabular regression
+
+## Validation Metrics
+
+- r2: 0.004852553257630565
+- mse: 1.704782407585897
+- mae: 1.0301575550030646
+- rmse: 1.3056731626199174
+- rmsle: 0.1919556417083651
+- loss: 1.3056731626199174
+
+## Best Params
+
+- learning_rate: 0.16113054215755473
+- reg_lambda: 3.3566663737449463e-06
+- reg_alpha: 1.999845686956423e-05
+- subsample: 0.3521158025399591
+- colsample_bytree: 0.1661721364825762
+- max_depth: 2
+- early_stopping_rounds: 172
+- 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 9019413

target_encoders.joblib

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training_params.json

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+{"data_path": "gvozdev/autotrain-data-autotrain-ratings", "model": "xgboost", "username": "gvozdev", "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": "gvozdev/autotrain-ratings", "categorical_columns": null, "numerical_columns": null, "task": "regression", "num_trials": 10, "time_limit": 600, "categorical_imputer": "most_frequent", "numerical_imputer": "median", "numeric_scaler": "robust"}