Machine Learning - Normale Data Distributie

Normal Data Distribution (Normal Data Distribution)

In the previous chapter, we learned how to create a completely random array of a given size between two given values.

In this chapter, we will learn how to create an array that focuses on a given value.

In probability theory, after the formula for this type of data distribution was proposed by the mathematician Carl Friedrich Gauss, this type of data distribution is called normal data distribution or Gaussian data distribution.

Example

Typical Normal Data Distribution:

import numpy
import matplotlib.pyplot as plt
x = numpy.random.normal(5.0, 1.0, 100000)
plt.hist(x, 100)
plt.show()

Result:


Run Instance

Note:Since the normal distribution curve has a characteristic bell-shaped shape, it is also called a bell-shaped curve.

Histogram Explanation

We use numpy.random.normal() The array created by the method (with 100000 values) is drawn as a histogram with 100 columns.

We specify the mean as 5.0 and the standard deviation as 1.0.

This means that these values should be concentrated around 5.0 and rarely deviate from the average by 1.0.

As can be seen from the histogram, most values are between 4.0 and 6.0, with the highest value about 5.0.