cal.F1Score: Calculate F1 score for classification, the inputs must be...

In GAGAs: Global Adaptive Generative Adjustment Algorithm for Generalized Linear Models

Calculate F1 score for classification, the inputs must be characters, and each of these elements must be either 'FALSE' or 'TRUE'.

Description

Calculate F1 score for classification, the inputs must be characters, and each of these elements must be either 'FALSE' or 'TRUE'.

Usage

cal.F1Score(predictions, truelabels)

Arguments

predictions	predictions
truelabels	true labels

Value

F1 score

Examples

set.seed(2022)
p_size = 30
sample_size=300
R1 = 3
R2 = 2
ratio = 0.5 #The ratio of zeroes in coefficients
# Set the true coefficients
zeroNum = round(ratio*p_size)
ind = sample(1:p_size,zeroNum)
beta_true = runif(p_size,0,R2)
beta_true[ind] = 0
X = R1*matrix(rnorm(sample_size * p_size), ncol = p_size)
y=X%*%beta_true + rnorm(sample_size,mean=0,sd=2)
# Estimation
fit = GAGAs(X,y,alpha = 3,family="gaussian")
Eb = fit$beta
cat("\n F1 score:", cal.F1Score(as.character(Eb!=0),as.character(beta_true!=0)))

GAGAs documentation built on May 29, 2024, 5:52 a.m.