Python for Machine Learning | Preprocessing | Discretize Continuous features - P75

Python for Machine Learning | Preprocessing | Discretize Continuous features - P75

Topic to be covered - Discretize features

What is Discretization?

In statistics and machine learning, discretization refers to the process of converting or
partitioning continuous attributes, features or variables to discretized or nominal
attributes/features/variables/intervals.

Where we can use it?
This can be useful when creating probability mass functions – formally, in density estimation.
It is a form of discretization in general and also of binning, as in making a histogram.
Whenever continuous data is discretized, there is always some amount of discretization error.

What is the goal of Discretization?
The goal is to reduce the amount to a level considered negligible for the modeling purposes at hand.

'''

Coding Starts Here
===============

from sklearn.preprocessing import Binarizer
import numpy as np

count = np.array([[10],
[20],
[25],
[30],
[45]])

binarizer = Binarizer(15)

x_bin = binarizer.fit_transform(count)

import matplotlib.pyplot as plt

plt.hist(x_bin)
plt.show()

# Divide into different Bins

x = np.digitize(count, bins=[5,10,25,30,35,45])

plt.plot(x)
plt.show()

plt.hist(x)
plt.show()

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