Exploring the model’s results
Evaluating results is an essential part of any CNN implementation process. This, of course, is true for any algorithm based on ML. Evaluation metrics are quantitative measures used to assess the performance and quality of a model, algorithm, or system in various tasks, such as ML, data analysis, and optimization. These metrics provide a way to objectively quantify how well a model is performing and to compare different models or approaches.
The type of metric to adopt obviously depends on the type of algorithm we are implementing; in the previous section, we implemented a CNN for the classification of the pistachio species. So, let’s take a look at the metrics available for this type of algorithm.
For a classification task, we can use the following metrics:
- Accuracy: The proportion of correctly classified instances out of the total instances
- Precision: The ratio of true positive predictions to the total number of positive...
