The probability density function (pdf) represents the email list probability of observing a continuous random variable in a region of space. Recall that for a one-dimensional random variable x, pdf f (x) follows the following properties:the probability that a variable will take the next value probability of variables taking exactly equal values estimating such a pdf from an observation sample is a common problem in machine learning. This is useful in many outlier detection algorithms that estimate the "True" distribution based on the sample observations and classify some of the existing or new observations as outliers.
For example, an auto insurance company interested in catching fraud may look at billing requirements for each type of bodywork, such as bumper replacement, and mark too high a potential fraud. As another example, a child psychologist looks at how long it email list takes to complete a particular task among different children and marks children who are too long or too short for potential research. Can do. In this blog post, I 'll show you how to learn a pdf from an observation sample . This allows you to calculate the probability of each observation and determine if it is common or rare. Density estimation using histogram first, generate random data for the demonstration. Set Seed(123) data <- c(rnorm(200, 10, 20), r
norm(200, 60, 30), runif(200, 120, 180)) # 600 email list pointsthen use the histograms to visualize them for understanding, as shown in figure 1. # plot 1 hist(data, breaks=50, freq=f, main="Univariate distribution", xlab="Data value")# plot 2 hist(data, breaks=20, freq=f, main="", xlab="20 data bins", col='red', border='red')par(new=t) hist(data, breaks=100, freq=f, main="Univariate distribution", xlab=null, xaxt='n', yaxt='n')figure 1-50 visualization of data using bin histogram data visualization using a 50-bin histogram the histogram is a graph for visualizing the data, but you can see that it is the first estimate of the density. More specifically, by dividing the data into bins and assuming that the density is constant within that bin and has a value equal to the number of observations classified in that bin as a percentage of the total number of observations. , you c