sensitivity: Computes classification sensitivity from the confusion matrix...

In PNNL-Comp-Mass-Spec/glmnetGLR: glmnetGLR

Description

For each class, we calculate the classification sensitivity in order to summarize its performance for the signature. We compute one of two aggregate scores, to summarize the overall performance of the signature.

Usage

sensitivity(confusionSummary, aggregate = c("micro", "macro"))

Arguments

confusionSummary	list containing the confusion summary for a set of classifications
aggregate	string that indicates the type of aggregation; by default, micro. See details.

Details

We suppose that an observation can be classified into one (and only one) of K classes. For the jth class (j = 1, ..., K), we define the sensitivity as the conditional probability

Sensitivity_j = Pr(y_hat = j | y = j),

where y_hat and y are the empirical and true classifications, respectively.

To estimate sensitivity_j for the jth class, we compute

(TP_j) / (TP_j + FN_j),

where TP_j and FN_j are the true positives and false negatives, respectively. More specifically, TP_j is the number of observations that we correctly classified into the jth class, and FN_j is is the number of observations that we should have classified into class j but failed to do so.

The two aggregate score options are the macro- and micro-aggregate (average) scores. The macro-aggregate score is the arithmetic mean of the binary scores for each class. The micro-aggregate score is a weighted average of each class' binary score, where the weights are determined by the sample sizes for each class. By default, we use the micro-aggregate score because it is more robust, but the macro-aggregate score might be more intuitive to some users.

In statistical terms, notice that in the binary case (K = 2), the sensitivity is the recall.

Also, note that the sensitivity is equal to the TPR.

The sensitivity measure ranges from 0 to 1 with 1 being the optimal value.

Value

list with the accuracy measure for each class as well as the macro- and micro-averages (aggregate measures across all classes).

Examples

data(prediction_values)

confmat <- confusion(prediction_values[,"Curated_Quality"], prediction_values[,"PredictClass"])

sensitivity(confmat)

PNNL-Comp-Mass-Spec/glmnetGLR documentation built on May 28, 2019, 2:23 p.m.