How to develop the Bayes classifier in R
I want to calculate the true Bayes classifier for simulated data generated from various known distributions. My question here is twofold. Firstly, is there any built-in procedure to calculate it in R for different models? (Though, it is possible to code it for a specific model based on its pdf). Secondly and more importantly, how to train the classifier and make predictions using training and test data, respectively. Following is a two-class problem with conditional distributions as multivariate Gaussian with equal true prior probabilities. Any satisfactory answer would be highly appreciated. Thanks!
Note: I don't have to use naive Bayes classifier instead.
library(mvtnorm)
n.train = 200
n.test = 100
n.train1 = n.train + 1
n.train2 = 2 * n.train
n.test1 = n.test + 1
n.test2 = 2 * n.test
mu0 = c(0, 0)
mu1 = c(1, 1)
sigma0 = matrix(c(1,1,1,4), ncol = 2)
sigma1 = 4 * sigma0
# X: training set
X <- matrix(0, nrow = n.train2, ncol = 3)
X[,3] <- rep(1:2, each = n.train)
X[1:n.train, 1:2] <- rmvnorm(n = n.train, mean = mu0, sigma = sigma0)
X[n.train1:n.train2, 1:2] <- rmvnorm(n = n.train, mean = mu1, sigma = sigma1)
# Y: test set
Y <- matrix(0, nrow = n.test2, ncol = 3)
Y[,3] <- rep(1:2, each = n.test)
Y[1:n.test, 1:2] <- rmvnorm(n = n.test, mean = mu0, sigma = sigma0)
Y[n.test1:n.test2, 1:2] <- rmvnorm(n = n.test, mean = mu1, sigma = sigma1)
r classification naivebayes supervised-learning
asked Nov 20 at 7:40
Jafer
184
add a comment |
I want to calculate the true Bayes classifier for simulated data generated from various known distributions. My question here is twofold. Firstly, is there any built-in procedure to calculate it in R for different models? (Though, it is possible to code it for a specific model based on its pdf). Secondly and more importantly, how to train the classifier and make predictions using training and test data, respectively. Following is a two-class problem with conditional distributions as multivariate Gaussian with equal true prior probabilities. Any satisfactory answer would be highly appreciated. Thanks!
Note: I don't have to use naive Bayes classifier instead.
library(mvtnorm)
n.train = 200
n.test = 100
n.train1 = n.train + 1
n.train2 = 2 * n.train
n.test1 = n.test + 1
n.test2 = 2 * n.test
mu0 = c(0, 0)
mu1 = c(1, 1)
sigma0 = matrix(c(1,1,1,4), ncol = 2)
sigma1 = 4 * sigma0
# X: training set
X <- matrix(0, nrow = n.train2, ncol = 3)
X[,3] <- rep(1:2, each = n.train)
X[1:n.train, 1:2] <- rmvnorm(n = n.train, mean = mu0, sigma = sigma0)
X[n.train1:n.train2, 1:2] <- rmvnorm(n = n.train, mean = mu1, sigma = sigma1)
# Y: test set
Y <- matrix(0, nrow = n.test2, ncol = 3)
Y[,3] <- rep(1:2, each = n.test)
Y[1:n.test, 1:2] <- rmvnorm(n = n.test, mean = mu0, sigma = sigma0)
Y[n.test1:n.test2, 1:2] <- rmvnorm(n = n.test, mean = mu1, sigma = sigma1)
r classification naivebayes supervised-learning
asked Nov 20 at 7:40
Jafer
184
add a comment |
I want to calculate the true Bayes classifier for simulated data generated from various known distributions. My question here is twofold. Firstly, is there any built-in procedure to calculate it in R for different models? (Though, it is possible to code it for a specific model based on its pdf). Secondly and more importantly, how to train the classifier and make predictions using training and test data, respectively. Following is a two-class problem with conditional distributions as multivariate Gaussian with equal true prior probabilities. Any satisfactory answer would be highly appreciated. Thanks!
Note: I don't have to use naive Bayes classifier instead.
library(mvtnorm)
n.train = 200
n.test = 100
n.train1 = n.train + 1
n.train2 = 2 * n.train
n.test1 = n.test + 1
n.test2 = 2 * n.test
mu0 = c(0, 0)
mu1 = c(1, 1)
sigma0 = matrix(c(1,1,1,4), ncol = 2)
sigma1 = 4 * sigma0
# X: training set
X <- matrix(0, nrow = n.train2, ncol = 3)
X[,3] <- rep(1:2, each = n.train)
X[1:n.train, 1:2] <- rmvnorm(n = n.train, mean = mu0, sigma = sigma0)
X[n.train1:n.train2, 1:2] <- rmvnorm(n = n.train, mean = mu1, sigma = sigma1)
# Y: test set
Y <- matrix(0, nrow = n.test2, ncol = 3)
Y[,3] <- rep(1:2, each = n.test)
Y[1:n.test, 1:2] <- rmvnorm(n = n.test, mean = mu0, sigma = sigma0)
Y[n.test1:n.test2, 1:2] <- rmvnorm(n = n.test, mean = mu1, sigma = sigma1)
r classification naivebayes supervised-learning
asked Nov 20 at 7:40
Jafer
184
I want to calculate the true Bayes classifier for simulated data generated from various known distributions. My question here is twofold. Firstly, is there any built-in procedure to calculate it in R for different models? (Though, it is possible to code it for a specific model based on its pdf). Secondly and more importantly, how to train the classifier and make predictions using training and test data, respectively. Following is a two-class problem with conditional distributions as multivariate Gaussian with equal true prior probabilities. Any satisfactory answer would be highly appreciated. Thanks!
Note: I don't have to use naive Bayes classifier instead.
library(mvtnorm)
n.train = 200
n.test = 100
n.train1 = n.train + 1
n.train2 = 2 * n.train
n.test1 = n.test + 1
n.test2 = 2 * n.test
mu0 = c(0, 0)
mu1 = c(1, 1)
sigma0 = matrix(c(1,1,1,4), ncol = 2)
sigma1 = 4 * sigma0
# X: training set
X <- matrix(0, nrow = n.train2, ncol = 3)
X[,3] <- rep(1:2, each = n.train)
X[1:n.train, 1:2] <- rmvnorm(n = n.train, mean = mu0, sigma = sigma0)
X[n.train1:n.train2, 1:2] <- rmvnorm(n = n.train, mean = mu1, sigma = sigma1)
# Y: test set
Y <- matrix(0, nrow = n.test2, ncol = 3)
Y[,3] <- rep(1:2, each = n.test)
Y[1:n.test, 1:2] <- rmvnorm(n = n.test, mean = mu0, sigma = sigma0)
Y[n.test1:n.test2, 1:2] <- rmvnorm(n = n.test, mean = mu1, sigma = sigma1)
r classification naivebayes supervised-learning
r classification naivebayes supervised-learning
asked Nov 20 at 7:40
Jafer
184
asked Nov 20 at 7:40
Jafer
184
edited Nov 22 at 12:14
asked Nov 20 at 7:40
Jafer
184
asked Nov 20 at 7:40
Jafer
184
asked Nov 20 at 7:40
Jafer
184
184
add a comment |
add a comment |
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