Naive-Bayes-classifier | Naive Bayes classifier is classification algorithm | Natural Language Processing library
kandi X-RAY | Naive-Bayes-classifier Summary
kandi X-RAY | Naive-Bayes-classifier Summary
Naive Bayes classifier is classification algorithm. It uses Naive based Bernoulli and Multinomial equation to classify documents(Text) as ham or spam.
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Top functions reviewed by kandi - BETA
- Main function
- Create multinomial model
- Berni model
- Train the trained test data from the trained files
- Get the number of words that match a given category
- Return the number of words that have a given category
- Returns the number of doc docs with the given category
- Get the number of doc docs
- Returns true if the given word is present
- Read stopwords file
Naive-Bayes-classifier Key Features
Naive-Bayes-classifier Examples and Code Snippets
Community Discussions
Trending Discussions on Naive-Bayes-classifier
QUESTION
For studying purposes, I've tried to implement this "lesson" using python but "without" sckitlearn or something similar.
My attempt code is the follow:
...ANSWER
Answered 2020-Nov-12 at 11:43You haven't multiplied by the priors p(Sport) = 3/5
and p(Not Sport) = 2/5
. So just updating your answers by these ratios will get you to the correct result. Everything else looks good.
So for example you implement p(a|Sports) x p(very|Sports) x p(close|Sports) x p(game|Sports)
in your math.prod(p)
calculation but this ignores the term p(Sport)
. So adding this in (and doing the same for the not sport condition) fixes things.
In code this can be achieved by:
QUESTION
I wanted to know if really AODE may be better than Naive Bayes in its way, as the description says:
https://cran.r-project.org/web/packages/AnDE/AnDE.pdf
--> "AODE achieves highly accurate classification by averaging over all of a small space."
https://www.quora.com/What-is-the-difference-between-a-Naive-Bayes-classifier-and-AODE
--> "AODE is a weird way of relaxing naive bayes' independence assumptions. It is no longer a generative model, but it relaxes the independence assumptions in a slightly different (and less principled) way than logistic regression does. It replaces the convex optimization problem used in training a logistic regression classifier by a quadratic (on the number of features) dependency on both training and test times."
But when I experiment it, I found that the predict results seems off, I implemented it with these codes:
...ANSWER
Answered 2020-Mar-12 at 13:00If you check out the vignette for the function:
train: data.frame : training data. It should be a data frame. AODE works only discretized data. It would be better to discreetize the data frame before passing it to this function.However, aode discretizes the data if not done before hand. It uses an R package called discretization for the purpose. It uses the well known MDL discretization technique.(It might fail sometimes)
By default, the discretization function from arules cuts it into 3, which may not be enough for iris. So I first reproduce the result you have with the discretization by arules:
QUESTION
I have applied svm on my dataset. my dataset is multi-label means each observation has more than one label.
while KFold cross-validation
it raises an error not in index
.
It shows the index from 601 to 6007 not in index
(I have 1...6008 data samples).
This is my code:
...ANSWER
Answered 2018-Aug-16 at 05:53train_index
, test_index
are integer indices based on the number of rows. But pandas indexing dont work like that. Newer versions of pandas are more strict in how you slice or select data from them.
You need to use .iloc
to access the data. More information is available here
This is what you need:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install Naive-Bayes-classifier
You can use Naive-Bayes-classifier like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the Naive-Bayes-classifier component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .
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