Model testing and defining metrics
In this step, we evaluate the trained model performance on a separate set of data points, named test data (which was split and versioned earlier, in the data ingestion step). The inference of the trained model is evaluated according to the selected metrics as per the use case. The output of this step is a report on the trained model performance.
To gain a comprehensive analysis of the model performance, we will measure the accuracy, precision, recall, and f-score. This is what they mean in practice in the context of the business problem:
- Accuracy: Number of correct predictions by the total number of predictions of data test samples.
- Precision: Precision measures the proportion of positives that were correctly predicted as positive. Precision = True Positives / (True Positives + False Positives)
- Recall: Recall measures the proportion of actual positives that were identified correctly. Recall = True Positives / (True Positives...
