naive-bayes | Naïve Bayes classifier | Machine Learning library

For studying purposes, I've tried to implement this "lesson" using python but "without" sckitlearn or something similar.

My attempt code is the follow:

I'm doing a naive-bayes algorithm in R. The main goal is to predict a variable's value. But in this specific task, I'm trying to see which column is better at predicting it. This is an example of what works (but in the real dataset doing it manually isn't an option):

I am trying to understand how to create a dataframe of factors to predict an outcome using naive_bayes. All the examples I have seen take a single dataframe and split it into two dfs(training and test). This does work for me:

I am trying to classify multiclass data at the token-level using scikit-learn. I already have a train and test split. The tokens occurs in batches of the same class, e.g. first 10 tokens belonging to class0, the next 20 belonging to class4 and so on. The data is in the following \t seperated format:

Alright, Im following https://medium.com/@phylypo/text-classification-with-scikit-learn-on-khmer-documents-1a395317d195 and https://scikit-learn.org/stable/tutorial/text_analytics/working_with_text_data.html trying to classify text based on category. My dataframe is laid out like this and named result:

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:

I need to count probability of each word against each category. I tried this code, but the result not as my expected. It didn't show the if the count value is 0.

I have 2 table:

• tb_thesis --> id_thesis, title, topics
• tb_words --> id_word, id_thesis, word (this table contains tb_thesis which has been explode into single words)

Not long ago I asked a question about the Accord.net Naive Bayes algorithm throwing an error. It turned out that this was due to me using Discrete value input columns but not giving enough training data for all the values I had listed for the column.

Now I am getting the exact same error, only this time it is being triggered only when I use a Continuous value for my output column. Particularly an output column of integer data type. Because it is an integer, the Codification class is not translating it so the values get passed directly into the Naive Bayes algorithm, and the algorithm apparently cannot handle that.

If I manually change the column data type to a string and send it through the Codification class to get codified then send the results of that through the algorithm it works correctly.

Is there any particular reason why this algorithm can't handle Continuous data types as outputs? Is there some setting I need to enable to make this work?

Some sample code: