naivebayes | Chapter 6 Supervised Sentiment Analysis using Naive Bayes | Natural Language Processing library

I want to use an HTML select element to change which thymeleaf fragment is displayed.

the html code:

I want to count the numbers of true values between two String from my training data, however, the code I implemented only counts the number of instances that are true as opposed to the total sum that are true.

I encountered some weird behavior using a recipe and a workflow to descriminate spam from valid texts using a naiveBayes classifier. I was trying to replicate using tidymodels and a workflow the results the 4th chapter of the book Machine learning with R: https://github.com/PacktPublishing/Machine-Learning-with-R-Second-Edition/blob/master/Chapter%2004/MLwR_v2_04.r

While I was able to reproduce the analysis either with add_variables() or add_formula() or with no workflow, the workflow using the add_recipe() function did not work.

I'm working on NaiveBayes classifier and I can predict values for a single data point using the model trained, but I want to get the probability value.

The data classified into two classes only. and the predict function returns 0 or 1.

I am trying to understand an example of code given here: https://www.perlmonks.org/?node_id=1083257 and the difference between the directly created hash references given in the example and one that I alternatively create first as a hash. When I run the following code:

I have a data set with many factor/categorical/nominal columns/variables/features. I need to create a multinomial naive bayes classifier for this data. I tried using the caret library but I don't think that was doing a multinomial naive bayes, I think it was doing gaussian naive bayes, details here. I have now discovered multinomial_naive_bayes() which seems to be perfect. It seems to handle nulls in the predictor variables and a variable with only 1 value without complaining.

The issue is, I can't figure out how to do my "post processing/analysis" of the model generated by the multinomial_naive_bayes() function. I want to get a caret style confusionMatrix on the model and also on the prediction output vs the test data to assess the classifier. I would also like to generate a ROC curve. How can I do this?

I have included the sample/reference/example from the documentation of multinomial_naive_bayes() below, how would I update this code to get my confusionMatricies and ROC curve.

From: R Package 'naivebayes', section: multinomial_naive_bayes pg 10

I am trying to build a simple Naive Bayes classifer for mushroom data. I want to use all of the variables as categorical predictors to predict if a mushroom is edible.

I am using caret package.

Here is my code in full:

I'm creating a Shiny app in Rstudio and I had trouble inserting an input$char into a ggplot boxplot, where input$char was the variable for y-axis, and the x-axis was not an input variable, but instead from the data frame. The issue was that the data wouldn't appear in the graph, but the input function of the dropdown menu still changed the y-axis in the graph when the app was loaded. Example code:

So I have this JSX element that I am attempting to render in the Class component. It is essentially a visual provided by D3's React library. However, I am receiving this error upon attempting to render the D3 visual:

Unhandled Rejection (Error): Element type is invalid: expected a string (for built-in components) or a class/function (for composite components) but got: undefined. You likely forgot to export your component from the file it's defined in, or you might have mixed up default and named imports.

Below are some relevant code snippets of where the error is occurring:

The builder function to pass all the necessary props to the D3 library