What are the most effective evaluation metrics for ANN models?

1 Accuracy and error

One of the simplest and most common evaluation metrics for ANN models is accuracy, which is the ratio of correct predictions to total predictions. Accuracy is useful for binary or multi-class classification problems, where you want to know how well your model can distinguish between different classes. However, accuracy can be misleading if your data is imbalanced, meaning that some classes are more frequent than others. In this case, you might want to use error, which is the inverse of accuracy, or 1 - accuracy. Error tells you how often your model makes wrong predictions, and can help you identify sources of bias or noise in your data.

2 Precision and recall

Another pair of evaluation metrics for ANN models is precision and recall, which are especially relevant for binary classification problems, where you have a positive and a negative class. Precision is the ratio of true positives to predicted positives, meaning how many of the positive predictions are actually correct. Recall is the ratio of true positives to actual positives, meaning how many of the positive instances are actually detected. Precision and recall can help you balance the trade-off between false positives and false negatives, depending on your goal and the cost of each type of error. For example, if you are building a spam filter, you might want to prioritize precision, to avoid marking legitimate emails as spam. On the other hand, if you are building a medical diagnosis system, you might want to prioritize recall, to avoid missing any potential diseases.

3 F1-score and ROC AUC

Sometimes, you might want to combine precision and recall into a single evaluation metric for ANN models, to get a more comprehensive picture of your model's performance. One way to do that is to use the F1-score, which is the harmonic mean of precision and recall. The F1-score ranges from 0 to 1, and gives a higher weight to lower values of precision and recall. The F1-score is useful for comparing models with similar accuracy, but different precision and recall. Another way to combine precision and recall is to use the ROC AUC, which stands for receiver operating characteristic area under the curve. The ROC AUC is the area under the curve that plots the true positive rate (recall) against the false positive rate (1 - precision) for different threshold values. The ROC AUC ranges from 0.5 to 1, and indicates how well your model can discriminate between the positive and negative classes. The ROC AUC is useful for comparing models with different accuracy, precision, and recall.

4 MSE and MAE

If you are working on a regression problem, where you want to predict a continuous value, rather than a discrete class, you might want to use different evaluation metrics for ANN models. One of the most common evaluation metrics for regression problems is the mean squared error (MSE), which is the average of the squared differences between the actual and predicted values. The MSE measures how close your predictions are to the true values, and penalizes large errors more than small errors. However, the MSE can be sensitive to outliers, meaning that a few extreme values can skew the result. In this case, you might want to use the mean absolute error (MAE), which is the average of the absolute differences between the actual and predicted values. The MAE measures how close your predictions are to the true values, and gives equal weight to all errors. The MAE is more robust to outliers, but less sensitive to small errors.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?