Softmax function :The softmax function is used to highlight the highest values while suppress the other lowest values. Softmax function can also be corollorily understood as normalising the output to [0,1]
Converts the score array to perfect probabilities .For example, Let’s say, A record belongs to three classes i.e class1, class2 and class 3 with the following score
scores = [1 , 2, 3] => SOFTMAX FUNCTION = [0.16, 0.33, 0.5] # The output has most of the weights where the value 3 is.
Softmax functions are the functions that are use to compute any score to probability.
Python Codes for Softmax function:
scores = [3.0, 1.0, 0.2] import numpy as np def softmax(x): """Compute softmax values for each sets of scores in x.""" return np.exp(x) / sum(np.exp(x)) print(softmax(scores))
One-Hot Encoding: Is a process of translating the categorical feature into multi-space binary features e.g