Machine learning metrics and loss functions
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There are many metrics in machine learning to measure whether a model is good enough. Here I will summarize them one by one.
Performance Metrics
$ accuracy = \frac{number\; of \; correct \; predictions}{number \; of \; total \; predictions}=\frac{TP+TN}{TP+TN+FP+FN} $. It can be used if the data are balanced.
$TRP=sensitivity=Recall=\frac{TP}{TP+FN}=1-FNR$.
TPR: true positive rate, FNR: false negative rate
$precision = \frac{TP}{TP+FP}$
$F_1$ score is the harmonic average of recall and precision, taking values between 0 and 1.
$F_1 \; socre = \frac{2}{\frac{1}{recall}+\frac{1}{precision}}$.
It still works for imbalanced data.
Loss functions
Mean squared error
Cross entropy error