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

In randomUniformForest: Random Uniform Forests for Classification, Regression and Unsupervised Learning

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)

Arguments

predictions	a vector (or factor, if classification) of predictions.
responses	a vector (or factor, if classification) of responses of the same length than 'predictions'.
regression	if FALSE, considered arguments are treated as a classification task.
OOB	if TRUE, expects 'prediction' to be an object of class randomUniformForest (with option 'OOB' enabled) in order to assess OOB predictions.
plotting	if TRUE, displays graphics. Set it to FALSE in the case of a regression with large datasets.

Value

print and plot metrics.

Author(s)

Saip Ciss [e-mail]

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)

randomUniformForest documentation built on June 22, 2022, 1:05 a.m.