SKLearnForQlik Log: Wed Nov  6 09:32:01 2019 

Model Name: HR-Attrition-RF-GSCV

Execution arguments: {'overwrite': True, 'test_size': 0.3, 'cv': 3, 'time_series_split': 0, 'max_train_size': None, 'lags': None, 'lag_target': False, 'scale_target': False, 'make_stationary': None, 'random_state': 42, 'compress': 3, 'retain_data': False, 'calculate_importances': True, 'debug': True}

Scaler: StandardScaler, missing: zeros, scale_hashed: True, scale_vectors: True
Scaler kwargs: {'with_mean': True, 'with_std': True}

Estimator: RandomForestRegressor
Estimator kwargs: {}


Cache updated. Models in cache:
['HR-Attrition-RF-GSCV']


TABLE DESCRIPTION SENT TO QLIK:

fields {
  name: "model_name"
}
fields {
  name: "result"
}
fields {
  name: "timestamp"
}
name: "SSE-Response"
numberOfRows: 1
 

RESPONSE: (1, 3) rows x cols
Sample Data:

             model_name                            result                               time_stamp
0  HR-Attrition-RF-GSCV  Model successfully saved to disk  09:32:01 11/06/19 Hora estándar romance
...
             model_name                            result                               time_stamp
0  HR-Attrition-RF-GSCV  Model successfully saved to disk  09:32:01 11/06/19 Hora estándar romance