> set.seed(1234) > ind <- sample(2, nrow(iris), replace=TRUE, prob=c(0.7, 0.3)) > trainData <- iris[ind==1,] > testData <- iris[ind==2,] The above statements
Inverse Normal Distribution
The method used for finding the corresponding z-critical value in a normal distribution using the known probability is said to be an inverse normal distribution. The inverse normal distribution is a continuous probability distribution with a family of two parameters.
Mean, Median, Mode
It is a descriptive summary of a data set. It can be defined by using some of the measures. The central tendencies do not provide information regarding individual data from the dataset. However, they give a summary of the data set. The central tendency or measure of central tendency is a central or typical value for a probability distribution.
Z-Scores
A z-score is a unit of measurement used in statistics to describe the position of a raw score in terms of its distance from the mean, measured with reference to standard deviation from the mean. Z-scores are useful in statistics because they allow comparison between two scores that belong to different normal distributions.
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Usually, before modeling, the iris data is split into two subsets: training (70%) and test (30%). |
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The iris data is split below into two subsets: training (30%) and test (70%). |
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It replaces the iris dataset with two subsets: training (the first) and test (the second). |
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It replaces the iris dataset with two subsets: training (70%) and test (30%). |
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