accuracy {fda.usc} | R Documentation |
Performance measures for regression and classification models
Description
cat2meas
and tab2meas
calculate the measures for a multiclass classification model.
pred2meas
calculates the measures for a regression model.
Usage
cat2meas(yobs, ypred, measure = "accuracy", cost = rep(1, nlevels(yobs)))
tab2meas(tab, measure = "accuracy", cost = rep(1, nrow(tab)))
pred.MSE(yobs, ypred)
pred.RMSE(yobs, ypred)
pred.MAE(yobs, ypred)
pred2meas(yobs, ypred, measure = "RMSE")
Arguments
yobs |
A vector of the labels, true class or observed response. Can be |
ypred |
A vector of the predicted labels, predicted class or predicted response. Can be |
measure |
Type of measure, see |
cost |
Cost value by class (only for input factors). |
tab |
Confusion matrix (Contingency table: observed class by rows, predicted class by columns). |
Details
-
cat2meas
computetab=table(yobs,ypred)
and callstab2meas
function. -
tab2meas
function computes the following measures (seemeasure
argument) for a binary classification model:-
accuracy
: Proportion of correct predictions.\frac{TP + TN}{TP + TN + FP + FN}
-
sensitivity, TPrate, recall
: True Positive Rate or recall.\frac{TP}{TP + FN}
-
precision
: Positive Predictive Value.\frac{TP}{TP + FP}
-
specificity, TNrate
: True Negative Rate.\frac{TN}{TN + FP}
-
FPrate
: False Positive Rate.\frac{FP}{TN + FP}
-
FNrate
: False Negative Rate.\frac{FN}{TP + FN}
-
Fmeasure
: Harmonic mean of precision and recall.\frac{2}{\frac{1}{\text{recall}} + \frac{1}{\text{precision}}}
-
Gmean
: Geometric Mean of recall and specificity.\sqrt{\left(\frac{TP}{TP + FN}\right) \cdot \left(\frac{TN}{TN + FP}\right)}
-
kappa
: Cohen's Kappa index.Kappa = \frac{P_o - P_e}{1 - P_e}
whereP_o
is the proportion of observed agreement,\frac{TP + TN}{TP + TN + FP + FN}
, andP_e
is the proportion of agreement expected by chance,\frac{(TP + FP)(TP + FN) + (TN + FN)(TN + FP)}{(TP + TN + FP + FN)^2}
. -
cost
: Weighted accuracy, calculated as\frac{\sum (\text{diag(tab)} / \text{rowSums(tab)} \cdot \text{cost})}{\sum(\text{cost})}
-
IOU
: Mean Intersection over Union.\frac{TP}{TP + FN + FP}
-
IOU4class
: Intersection over Union by class level.\frac{TP}{TP + FN + FP}
#'
-
-
pred2meas
function computes the following measures of error, usign themeasure
argument, for observed and predicted vectors:-
MSE
: Mean squared error,\frac{\sum{(ypred- yobs)^2}}{n}
. -
RMSE
: Root mean squared error\sqrt{\frac{\sum{(ypred- yobs)^2}}{n} }
. -
MAE
: Mean Absolute Error,\frac{\sum |yobs - ypred|}{n}
.
-
See Also
Other performance:
weights4class()