Note: I need all of the 10 answers exact and accurate, not any crap with bull shit answers or excel data.
I will not finish the job before validating the answers or not even accept any bid without a guaranteed example of solution(s). See this note and if you are confident enough to answer all the questions accurately and come up with at least one solution before bidding, only then you can work for it or else don’t waste my time.
Need help with the following assignment have attached the required data and materials of the professor’s lecture. It needs to be performed in RStudio. All of the detailed instructions and necessary documents are attached.
Note: I need all of the 10 answers exact and accurate. I will not finish the job before validating the answers or not even accept any bid without a guaranteed example of solution(s)
Instructions and Assignment:
In this assignment you will train a Naïve Bayes classifier on categorical data and predict individuals’ incomes. Import the nbtrain.csv file. Use the first 9010 records as training data and the remaining 1000 records as testing data.
In this assignment you will train a Naïve Bayes classifier on categorical data and predict individuals’ incomes. Import the nbtrain.csv file. Use the first 9010 records as training data and the remaining 1000 records as testing data. 1. Read the nbtrain.csv file into the R environment. 2. Construct the Naïve Bayes classifier from the training data, according to the formula “income ~ age + sex + educ”. To do this, use the “naiveBayes” function from the “e1071” package. Provide the model’s a priori and conditional probabilities. 3. Score the model with the testing data and create the model’s confusion matrix. Also, calculate the overall, 10-50K, 50-80K, and GT 80K misclassification rates. Explain the variation in the model’s predictive power across income classes. 4. Use the first 9010 records as training data and the remaining 1000 records as testing data. 5. What is propose of separating the data into a training set and testing set? 6. Construct the classifier according to the formula “sex ~ age + educ + income”, and calculate the overall, female, and male misclassification rates. Explain the misclassification rates? 7. Divide the training data into two partitions, according to sex, and randomly select 3500 records from each partition. Reconstruct the model from part (a) from these 7000 records. Provide the model’s a priori and conditional probabilities. 8. How well does the model classify the testing data? Explain why. 9. Repeat step (b) 4 several times. What effect does the random selection of records have on the model’s performance? 10. What conclusions can one draw from this exercise?
Delivering a high-quality product at a reasonable price is not enough anymore.
That’s why we have developed 5 beneficial guarantees that will make your experience with our service enjoyable, easy, and safe.
You have to be 100% sure of the quality of your product to give a money-back guarantee. This describes us perfectly. Make sure that this guarantee is totally transparent.Read more
Each paper is composed from scratch, according to your instructions. It is then checked by our plagiarism-detection software. There is no gap where plagiarism could squeeze in.Read more
Thanks to our free revisions, there is no way for you to be unsatisfied. We will work on your paper until you are completely happy with the result.Read more
Your email is safe, as we store it according to international data protection rules. Your bank details are secure, as we use only reliable payment systems.Read more
By sending us your money, you buy the service we provide. Check out our terms and conditions if you prefer business talks to be laid out in official language.Read more
Let us help you get a good grade on your paper. Get professional help and free up your time for more important courses. Let us handle your;