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 '18 at 7:40
JaferJafer
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 '18 at 7:40
JaferJafer
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 '18 at 7:40
JaferJafer
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 '18 at 7:40
JaferJafer
184
asked Nov 20 '18 at 7:40
JaferJafer
184
edited Nov 22 '18 at 12:14
Jafer
asked Nov 20 '18 at 7:40
JaferJafer
184
asked Nov 20 '18 at 7:40
JaferJafer
184
asked Nov 20 '18 at 7:40
JaferJafer
184
184
add a comment |
add a comment |
0
active
oldest
votes
Your Answer
StackExchange.ifUsing("editor", function () {
StackExchange.using("externalEditor", function () {
StackExchange.using("snippets", function () {
StackExchange.snippets.init();
});
});
}, "code-snippets");
StackExchange.ready(function() {
var channelOptions = {
tags: "".split(" "),
id: "1"
};
initTagRenderer("".split(" "), "".split(" "), channelOptions);
StackExchange.using("externalEditor", function() {
// Have to fire editor after snippets, if snippets enabled
if (StackExchange.settings.snippets.snippetsEnabled) {
StackExchange.using("snippets", function() {
createEditor();
});
}
else {
createEditor();
}
});
function createEditor() {
StackExchange.prepareEditor({
heartbeatType: 'answer',
autoActivateHeartbeat: false,
convertImagesToLinks: true,
noModals: true,
showLowRepImageUploadWarning: true,
reputationToPostImages: 10,
bindNavPrevention: true,
postfix: "",
imageUploader: {
brandingHtml: "Powered by u003ca class="icon-imgur-white" href="https://imgur.com/"u003eu003c/au003e",
contentPolicyHtml: "User contributions licensed under u003ca href="https://creativecommons.org/licenses/by-sa/3.0/"u003ecc by-sa 3.0 with attribution requiredu003c/au003e u003ca href="https://stackoverflow.com/legal/content-policy"u003e(content policy)u003c/au003e",
allowUrls: true
},
onDemand: true,
discardSelector: ".discard-answer"
,immediatelyShowMarkdownHelp:true
});
}
});
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstackoverflow.com%2fquestions%2f53388289%2fhow-to-develop-the-bayes-classifier-in-r%23new-answer', 'question_page');
}
);
Post as a guest
Required, but never shown
0
active
oldest
votes
0
active
oldest
votes
active
oldest
votes
active
oldest
votes
Thanks for contributing an answer to Stack Overflow!
- Please be sure to answer the question. Provide details and share your research!
But avoid …
- Asking for help, clarification, or responding to other answers.
- Making statements based on opinion; back them up with references or personal experience.
To learn more, see our tips on writing great answers.
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstackoverflow.com%2fquestions%2f53388289%2fhow-to-develop-the-bayes-classifier-in-r%23new-answer', 'question_page');
}
);
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
