# accuracy

##### Performance measures for regression and classification models

`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

`numeric`

,`character`

, or`factor`

.- ypred
A vector of the predicted labels, predicted class or predicted response. Can be

`numeric, character, or factor`

.- measure
Type of measure, see

`details`

section.- cost
Cost value by class (only for input factors).

- tab
Confusion matrix (Contingency table: observed class by rows, predicted class by columns).

##### Details

`cat2meas`

compute \(tab=table(yobs,ypred)\) and calls`tab2meas`

function.`tab2meas`

function computes the following measures (see`measure`

argument) for a binary classification model:`accuracy`

the accuracy classification score`recall`

,`sensitivity,TPrate`

\(R=TP/(TP+FN)\)`precision`

\(P=TP/(TP+FP)\)`specificity`

,`TNrate`

\(TN/(TN+FP)\)`FPrate`

\(FP/(TN+FP)\)`FNrate`

\(FN/(TP+FN)\)`Fmeasure`

\(2/(1/R+1/P)\)`Gmean`

\(sqrt(R*TN/(TN+FP))\)`kappa`

the kappa index`cost`

\(sum(diag(tab)/rowSums(tab)*cost)/sum(cost)\)

`pred2meas`

function computes the following measures of error, usign the`measure`

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()`

*Documentation reproduced from package fda.usc, version 2.0.1, License: GPL-2*