Model Evaluation¶
The model is evaluated based on various regression based metrics.
Contains classes and methods to obtain various regression based metrics to evaluate
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class
dlmfg.core.metrics_eval.MetricsEval[source]¶ MetricsEval Class
Evaluate metrics to evaluate model performance
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metrics_eval_base(predicted_y, test_y, logs_path, run_id=0)[source]¶ Get predicted and actual value for all KCCs and return regression metrics namely: Mean Absolute Error, Mean Squared Error, Root Mean Squared Error, R-Squared Value
- Parameters
predicted_y – predicted values for the process parameters
predicted_y – actual values for the process parameters
logs_path (str (required)) – Logs path to save the evaluation metrics
- Returns
dictionary of all metrics for each KCC
- Return type
- Returns
dataframe of all metrics for each KCC
- Return type
pandas.dataframe
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metrics_eval_classification(y_pred, y_true, logs_path, run_id=0)[source]¶ Get predicted and actual value for all KCCs and return regression metrics namely: Mean Absolute Error, Mean Squared Error, Root Mean Squared Error, R-Squared Value
- Parameters
predicted_y – predicted values for the process parameters
predicted_y – actual values for the process parameters
logs_path (str (required)) – Logs path to save the evaluation metrics
- Returns
dictionary of all metrics for each KCC
- Return type
- Returns
dataframe of all metrics for each KCC
- Return type
pandas.dataframe
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metrics_eval_cop(predicted_y, test_y, logs_path, run_id=0)[source]¶ Get predicted and actual value for all KCCs and return regression metrics namely: Mean Absolute Error, Mean Squared Error, Root Mean Squared Error, R-Squared Value
- Parameters
predicted_y – predicted values for the process parameters
predicted_y – actual values for the process parameters
logs_path (str (required)) – Logs path to save the evaluation metrics
- Returns
dictionary of all metrics for each KCC
- Return type
- Returns
dataframe of all metrics for each KCC
- Return type
pandas.dataframe
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