Feature Hashing

Sample of Feature Hashing for Machine Learning

Feature Hashing란?

In machine learning, feature hashing, also known as the hashing trick (by analogy to the kernel trick), is a fast and space-efficient way of vectorizing features, i.e. turning arbitrary features into indices in a vector or matrix.[1][2] It works by applying a hash function to the features and using their hash values as indices directly, rather than looking the indices up in an associative array. WIKIPEDIA

Why is it necessary? (Necessity)

Feature are required when machine learning is in operation.

Since the lengths of the features are different, it is necessary to process them.

Example

from sklearn.feature_extraction import FeatureHasher

h = FeatureHasher(n_features=n, input_type="string")
f = h.transform(data)

Example of feature hashing

# 1

# Length    5         7        4          9
Input = ['Apple', 'Pumpkin', 'Corn', 'Blueberry']

# Output of equal length
Output = 
 [[ 0.  0.  2.  0.  0.  0. -2.  0.  0.  1.]
  [ 0. -3.  1.  0. -1.  0.  0.  0.  0.  0.]
  [ 0. -1.  0.  1. -1.  1.  0.  0.  0.  0.]
  [ 0. -1.  1.  0.  1.  2.  0.  0.  0.  2.]]

# 2

# Added 'Apple1' to list of Input
Input = ['Apple', 'Pumpkin', 'Corn', 'Blueberry', 'Apple1']

Output = 
[[ 0.  0.  2.  0.  0.  0. -2.  0.  0.  1.]
 [ 0. -3.  1.  0. -1.  0.  0.  0.  0.  0.]
 [ 0. -1.  0.  1. -1.  1.  0.  0.  0.  0.]
 [ 0. -1.  1.  0.  1.  2.  0.  0.  0.  2.]
 [ 0.  0.  2.  0.  0.  0. -2. -1.  0.  1.]] # The results have changed, but they are similar