sklearn.preprocessing.Binarizer() is a method which belongs to preprocessing module. It plays a key role in the discretization of continuous feature values.
Example #1:
A continuous data of pixels values of an 8-bit grayscale image have values ranging between 0 (black) and 255 (white) and one needs it to be black and white. So, using Binarizer() one can set a threshold converting pixel values from 0 – 127 to 0 and 128 – 255 as 1.
Example #2:
One has a machine record having “Success Percentage” as a feature. These values are continuous ranging from 10% to 99% but a researcher simply wants to use this data for prediction of pass or fail status for the machine based on other given parameters.
Syntax :
sklearn.preprocessing.Binarizer(threshold, copy)
Parameters :
threshold :[float, optional] Values less than or equal to threshold is mapped to 0, else to 1. By default threshold value is 0.0.
copy :[boolean, optional] If set to False, it avoids a copy. By default it is True.
Return :
Binarized Feature values
Below is the Python code explaining sklearn.Binarizer()
# Python code explaining how # to Binarize feature values """ PART 1 Importing Libraries """
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# Sklearn library from sklearn import preprocessing
""" PART 2 Importing Data """
data_set = pd.read_csv(
'C:\\Users\\dell\\Desktop\\Data_for_Feature_Scaling.csv')
data_set.head() # here Features - Age and Salary columns # are taken using slicing # to binarize values age = data_set.iloc[:, 1].values
salary = data_set.iloc[:, 2].values
print ("\nOriginal age data values : \n", age)
print ("\nOriginal salary data values : \n", salary)
""" PART 4 Binarizing values """
from sklearn.preprocessing import Binarizer
x = age
x = x.reshape(1, -1)
y = salary
y = y.reshape(1, -1)
# For age, let threshold be 35 # For salary, let threshold be 61000 binarizer_1 = Binarizer(35)
binarizer_2 = Binarizer(61000)
# Transformed feature print ("\nBinarized age : \n", binarizer_1.fit_transform(x))
print ("\nBinarized salary : \n", binarizer_2.fit_transform(y))
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Output :
Country Age Salary Purchased 0 France 44 72000 0 1 Spain 27 48000 1 2 Germany 30 54000 0 3 Spain 38 61000 0 4 Germany 40 1000 1 Original age data values : [44 27 30 38 40 35 78 48 50 37] Original salary data values : [72000 48000 54000 61000 1000 58000 52000 79000 83000 67000] Binarized age : [[1 0 0 1 1 0 1 1 1 1]] Binarized salary : [[1 0 0 0 0 0 0 1 1 1]]