# accuracy: Performance measures for regression and classification models In fda.usc: Functional Data Analysis and Utilities for Statistical Computing

## Description

`cat2meas` and `tab2meas` calculate the measures for a multiclass classification model.
`pred2meas` calculates the measures for a regression model.

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11``` ```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, ∑ (ypred- yobs)^2 /n

• `RMSE` Root mean squared error √(∑ (ypred- yobs)^2 /n )

• `MAE` Mean Absolute Error, ∑ |yobs - ypred| /n

Other performance: `weights4class()`