How to check the quality of ANN model?

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Najla Matti Isaacc

There are several ways to check the quality of an Artificial Neural Network (ANN) model, including:

Accuracy: This measures how well the model is able to correctly classify or predict the outcome of the data it is trained on. The higher the accuracy, the better the model.

Precision and Recall: Precision measures the proportion of true positives among all the positives predicted by the model, while recall measures the proportion of true positives among all the actual positives in the data. Both metrics are important for evaluating the performance of a classification model.

Confusion matrix: A confusion matrix is a table that shows the number of true positives, true negatives, false positives, and false negatives for a classification model. This can help identify which classes are being predicted well and which are not.

Loss function: The loss function measures how well the model is able to minimize the error between its predicted output and the actual output. Lower values of the loss function indicate better performance.

Cross-validation: Cross-validation is a technique for testing the performance of the model on a separate validation dataset. This can help detect overfitting or underfitting of the model.

ROC curve: A receiver operating characteristic (ROC) curve plots the true positive rate against the false positive rate for different classification thresholds. The area under the curve (AUC) is a measure of how well the model is able to distinguish between the positive and negative classes.

It is important to use multiple metrics to evaluate the quality of an ANN model, as different metrics can provide different insights into the performance of the model.

Bipesh Subedi

Using performance metrics, you can check the efficiency of the model. There are various metrics to evaluate the model depending on the application. For example, BLEU, METEOR etc. are used for extensively evaluating the language model's quality. While accuracy, precision, recall etc. are all-purpose metrics.

Osamah Ali Al-Hashimi

Thank you very much Bipesh Subedi