What are metrics in Neural networks in R

This recipe explains what are metrics in Neural networks in R

Recipe Objective - What are metrics in neural networks in R?

Metrics help in evaluating deep learning models. Metrics are used to monitor and measure the performance of model. Metrics are not differentiable but in some cases is differentiable as can be used both as loss function(Regularization added) and a metric. Metrics involve many categories like Classification metrics, Regression metrics, Ranking metrics, statistical metrics, Computer vision metrics, Natural language processing metrics, Deep learning metrics etc. Some famous classification metrics used are Accuracy, precision, F1-score, ROC, AUC etc. Regression metrics are MSE and MAE. Statistical metrics is Correlation. Deep learning metrics is Inception score, Frechet Inception distance etc.

Is Deep Learning same as Machine Learning? Find Out The Answer Now!

Explanation of metrics.

Accuracy is defined as the division of number of correct predictions to the total number of prediction multiplied by 100. Precision metrics is used when accuracy doesnot clearly defines the neural network. Precision is the division of True positive to the addition of True positive and False Positive. Sometimes Precision gives good results than accuracy.

Precision= True_Positive/ (True_Positive+ False_Positive)

Recall is defined as the division of True positive to the addition of True positive and False negative. F1-score combines the recall and the precision and is defined as the harmonic mean of precision and recall.

Recall= True_Positive/ (True_Positive+ False_Negative) F1-score= 2*Precision*Recall/(Precision+Recall)

Receiver operating characteristic curve is the plot that shows performance of a binary classifier as the function of its cut-off threshold. It shows true positive rate (TPR) against false positive rate (FPR) for various threshold values. ROC curve finds out the True positive rate and False positive rate for many threshold values and plots TPR against the FPR.

Area under the curve is aggregated measure of performance of the binary classifier on all possible threshold values and therefore, it is a threshold invariant. AUC calculates area under the ROC curve and therefore it ranges between 0 and 1 AUC can be interpreted as the probability that model ranks the random positive example more highly than the random negative example.

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