randomUniformForest (version 1.1.2)

model.stats: Common statistics for a vector (or factor) of predictions and a vector (or factor) of responses

Description

Given a vector of predictions and a vector of responses, provide some statistics and plots like AUC, AUPR, confusion matrix, F1-score, geometric mean, residuals, mean squared and mean absolute error.

Usage

model.stats(predictions, responses, regression = FALSE, OOB = FALSE, plotting = TRUE)

Value

• print and plot metrics.

Examples

## not run
## Classification : synthetic data

# set.seed(2014)
# n = 1000
# p = 100 
# X = simulationData(n, p)
# X = fillVariablesNames(X)
# epsilon1 = runif(n,-1,1)
# epsilon2 = runif(n,-1,1)
# rule = 2*(X[,1]*X[,2] + X[,3]*X[,4]) + epsilon1*X[,5] + epsilon2*X[,6]
# Y = as.factor(ifelse(rule > mean(rule), "+","-")) 

# training and test sets

# train_test = init_values(X, Y, sample.size = 1/2)
# X1 = train_test$xtrain
# Y1 = train_test$ytrain
# X2 = train_test$xtest
# Y2 = train_test$ytest

# train model
# synth.ruf = randomUniformForest(X1, as.factor(Y1))

# evaluates OOB predictions
# statsOOB.pred.synth.ruf = model.stats(synth.ruf, as.factor(Y1), OOB = TRUE)

# predict 
# pred.synth.ruf = predict(synth.ruf, X2)

# statistics : produces also two plots
# stats.pred.synth.ruf = model.stats(pred.synth.ruf, as.factor(Y2))

# or, trick, do all in two lines
# synth.ruf = randomUniformForest(X1, as.factor(Y1), xtest = X2, ytest = as.factor(Y2))
# stats.pred.synth.ruf = model.stats(synth.ruf, as.factor(Y2))

## regression : synthetic data
# Y = rule
# Y1 = Y[train_test$train_idx]
# Y2 = Y[train_test$test_idx]

# synth.ruf = randomUniformForest(X1, Y1)
# statsOOB.pred.synth.ruf = model.stats(synth.ruf, Y1, OOB = TRUE, regression = TRUE)
# pred.synth.ruf = predict(synth.ruf, X2)
# stats.pred.synth.ruf = model.stats(pred.synth.ruf, Y2, regression = TRUE)