--- title: Metrics keywords: fastai sidebar: home_sidebar summary: "This contains metrics not included in fastai." description: "This contains metrics not included in fastai." nb_path: "nbs/051_metrics.ipynb" ---
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MatthewsCorrCoefBinary[source]

MatthewsCorrCoefBinary(sample_weight=None)

Matthews correlation coefficient for single-label classification problems

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get_task_metrics[source]

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

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

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accuracy_multi[source]

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

Compute accuracy when inp and targ are the same size.

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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.

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precision_multi[source]

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

Computes precision when inp and targ are the same size.

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recall_multi[source]

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

Computes recall when inp and targ are the same size.

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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.

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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.

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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.

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F1_multi[source]

F1_multi(*args, **kwargs)

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mae[source]

mae(inp, targ)

Mean absolute error between inp and targ.

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mape[source]

mape(inp, targ)

Mean absolute percentage error between inp and targ.

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