This contains metrics not included in fastai.

MatthewsCorrCoefBinary[source]

MatthewsCorrCoefBinary(sample_weight=None)

Matthews correlation coefficient for single-label classification problems

get_task_metrics[source]

get_task_metrics(dls, binary_metrics=None, multi_class_metrics=None, regression_metrics=None, verbose=True)

All metrics applicable to multi classification have been created by Doug Williams (https://github.com/williamsdoug). Thanks a lot Doug!!

accuracy_multi[source]

accuracy_multi(inp, targ, thresh=0.5, sigmoid=True)

Compute accuracy when inp and targ are the same size.

metrics_multi_common[source]

metrics_multi_common(inp, targ, thresh=0.5, sigmoid=True, by_sample=False)

Computes TP, TN, FP, FN when inp and targ are the same size.

precision_multi[source]

precision_multi(inp, targ, thresh=0.5, sigmoid=True)

Computes precision when inp and targ are the same size.

recall_multi[source]

recall_multi(inp, targ, thresh=0.5, sigmoid=True)

Computes recall when inp and targ are the same size.

specificity_multi[source]

specificity_multi(inp, targ, thresh=0.5, sigmoid=True)

Computes specificity (true negative rate) when inp and targ are the same size.

balanced_accuracy_multi[source]

balanced_accuracy_multi(inp, targ, thresh=0.5, sigmoid=True)

Computes balanced accuracy when inp and targ are the same size.

Fbeta_multi[source]

Fbeta_multi(inp, targ, beta=1.0, thresh=0.5, sigmoid=True)

Computes Fbeta when inp and targ are the same size.

F1_multi[source]

F1_multi(*args, **kwargs)

mae[source]

mae(inp, targ)

Mean absolute error between inp and targ.

mape[source]

mape(inp, targ)

Mean absolute percentage error between inp and targ.