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# calculate shannon-entropy -sum(freqs * log2(freqs)) [1] 0.940286 As a side note, the function entropy.empirical is in the entropy package where you set the units to log2 allowing some more flexibility. Example: entropy.empirical(freqs, unit="log2") [1] 0.940286
The entropy function allows to estimate entropy from observed counts by a variety of methods: method="ML":maximum likelihood, see entropy.empirical. method="MM":bias-corrected maximum likelihood, see entropy.MillerMadow. method="Jeffreys": entropy.Dirichlet with a=1/2. method="Laplace": entropy.Dirichlet with a=1.
entropy is an R package that provides tools for estimating entropy, mutual information, and related quantities. These are fundamental concepts in information theory and have applications in various fields including statistics, machine learning, and data analysis.
Implements various estimators of entropy for discrete random variables, including the shrinkage estimator by Hausser and Strimmer (2009), the maximum likelihood and the Millow-Madow estimator, various Bayesian estimators, and the Chao-Shen estimator.
Now, the question is: assuming that the following is the algorithm used to calculate the entropy – taken from Wikipedia $$ \mathrm{H}(X) = -\sum_{i} {\mathrm{P}(x_i) \log_b \mathrm{P}(x_i)} $$ my questions are the following.
12 Δεκ 2022 · Now i want an R code function (or loop) that can calculate the entropy and information gain for each levels in each categorical variable and return the lowest entropy and highest information gain.
Let’s create a function to compute entropy, and try it out. #compute Shannon entropy entropy <- function(target) { freq <- table(target)/length(target) # vectorize vec <- as.data.frame(freq)[,2] #drop 0 to avoid NaN resulting from log2 vec<-vec[vec>0] #compute entropy -sum(vec * log2(vec)) } entropy(setosa_subset$Species)
