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README.md

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+
+---
+tags:
+- autotrain
+- tabular
+- regression
+- tabular-regression
+datasets:
+- rea-svm/autotrain-data
+---
+
+# Model Trained Using AutoTrain
+
+- Problem type: Tabular regression
+
+## Validation Metrics
+
+- r2: -13.723120886842382
+- mse: 38015883737.055504
+- mae: 174448.73032973852
+- rmse: 194976.62356563544
+- rmsle: 2.6073960566969823
+- loss: 194976.62356563544
+
+## Best Params
+
+- C: 60.27729201980406
+- fit_intercept: False
+- loss: epsilon_insensitive
+- epsilon: 0.00020827552565594136
+- max_iter: 9903
+
+## 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 6601

target_encoders.joblib

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training_params.json

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+{"data_path": "rea-svm/autotrain-data", "model": "svm", "username": "maitelizarraga", "seed": 42, "train_split": "train", "valid_split": "validation", "project_name": "rea-svm", "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": "robust"}