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umka2103 [35]
1 year ago
6

For rational functions and functions with square roots, the domain can be all real numbers except (1) anything that will make th

e
Mathematics
1 answer:
liraira [26]1 year ago
8 0

For rational functions and functions with square roots, the domain can be all real numbers except (1) anything that will make them because the square root of only non-negative values exists and that of negative values does not.

<h3>What are the domain and range of the function?</h3>

The domain of the function includes all possible x values of a function, and the range includes all possible y values of the function.

Let the functions with square roots be f(x) = √x.

The domain of this function is x ≥ 0,

Since the real number system does not exist the square root of negative numbers.

Therefore, For rational functions and functions with square roots, the domain can be all real numbers except (1) anything that will make them because the square root of only non-negative values exists and that of negative values does not.

Learn more about the domain and the range here:

brainly.com/question/21027387

#SPJ1

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Answer:

In the step-by-step

Step-by-step explanation:

This is the code i created using the R software and the packages "caret" and "e0171".

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library(e1071)

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x1 <- rnorm(60)

x2 <- rnorm(60)

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index <- createDataPartition(data1$spamvec,

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training_data <- data1[index,]

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fitControl <- trainControl(method = "cv",

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tune.grid <- expand.grid(C = seq(0, 10, .1))

# Scaling the predictors

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spam_training_cs      <- predict(preProcess_cs, training_data)

spam_testing_cs       <- predict(preProcess_cs, testing_data)

# Training a Naive Bayes to predict binary outcome

Naive_Bayes_Model=naiveBayes(spamvec ~.,

                            data=spam_training_cs,

                            tuneGrid = tune.grid,

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# Confusion matrix

prediction <- predict(Naive_Bayes_Model, spam_testing_cs)

confusionMatrix(prediction, spam_testing_cs$spamvec, positive = "spam")

confM <- confusionMatrix(prediction, spam_testing_cs$spamvec, positive = "spam")

accuracy <- confM$overall[1]

accuracy

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Step-by-step explanation:

we have

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