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Results tagged with classification
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user 131402
Statistical classification is the problem of identifying the sub-population to which new observations belong, where the identity of the sub-population is unknown, on the basis of a training set of data containing observations whose sub-population is known. Therefore these classifications will show a variable behavior which can be studied by statistics.
2
votes
How can I assess case-level uncertainty of classification using logistic regression?
People usually distinguish two types of uncertainty: aleatoric uncertainty and epistemic uncertainty.
Aleatoric uncertainty describes the uncertainty due to pure randomness in the data-generating proc …
- 3,098
12
votes
Area under the ROC curve when there is imbalance: is there a problem, and if not, why does t...
and the classification problem at hand, including its class distribution. (They propose their H-measure as a supposedly superior alternative, which has been discussed a few times on stats.SE.) …
- 3,098
1
vote
Accepted
Optimizing a threshold value on a dependent metric using a classifier trained to optimize a ...
That is a completely standard approach. (Although - rather than AUROC - folks would usually maximize a proper scoring rule such as the Brier score / mean squared error or log loss during training.)
In …
- 3,098
5
votes
ROC AUC has $0.5$ as random performance. Does PR AUC have a similar notion?
Firstly, you will want to have a look at precision-recall-gain curves, which enable comparison of classifier performance across datasets with different base rates. It's basically just a clever (and th …
- 3,098
5
votes
Accepted
What can be said about ROC of binary classifier trained for data with inverted class labels ...
If you invert the labels, you have
$$\mathrm{TPR}_1 = \mathrm{TNR}_{\text{inv}} = 1-\mathrm{FPR}_{\text{inv}}$$
and
$$\mathrm{FPR}_1= \mathrm{FNR}_{\text{inv}} = 1-\mathrm{TPR}_{\text{inv}},$$
where b …
- 3,098
0
votes
Accepted
Bootstrapping (aleatoric and epistemic) risk score uncertainty
As I just elaborated on in my answer to my original, broader question, what I am asking for seems to be fundamentally impossible to do.
- 3,098
4
votes
Risk score uncertainty quantification
There are a number of recent theoretical results in the literature pointing in similar directions, e.g., that distribution-free confidence intervals for risk predictions in binary classification are necessarily …
- 3,098
2
votes
1
answer
201
views
Bootstrapping (aleatoric and epistemic) risk score uncertainty
I am working on various risk score estimation problems. I assume individual subjects are associated with a true risk
$$r_i = f(x_i; \epsilon_i), \quad 0 \leq r_i \leq 1,$$
where $x_i$ is some availabl …
- 3,098
5
votes
1
answer
308
views
Risk score uncertainty quantification
Again, I am having trouble adapting this line of work to the classification / binary outcome setting. …
- 3,098
18
votes
When is unbalanced data really a problem in Machine Learning?
I know I'm late to the party, but: the theory behind the data imbalance problem has been beautifully worked out by Sugiyama (2000) and a huge number of highly cited papers following that, under the ke …
- 3,098