Sklearn Onehot Code __ Machine Learning

1. One hot encoder

Sklearn.preprocessing.OneHotEncoder

One hot encoder can encode not only the label, but also the categorical feature:

>>> from sklearn.preprocessing import onehotencoder
>>> enc = onehotencoder ()

>>>  Enc.fit ([[0, 0, 3], [1, 1, 0], [0, 2, 1], [1, 0, 2]])  

>>> enc.n_values_
Array ([2, 3, 4])

>>> Enc.feature_indices_
Array ([0, 2, 5, 9])

>>> enc.transform ([[0, 1, 1]]). ToArray ()
Array ([[1.,  0.,  0.,  1.,  0.,  0.,  1.,  0.,  0.]

1 2 3 4 5 6 7 8 9 10 11 12-13

The data set passed in for the Onehotencoder class:

[[0, 0, 3], 
[1, 1, 0], 
[0, 2, 1], 
[1, 0, 2]]

1 2 3 4

Each column represents a property, after the fit operation: An object ENC n_values_ member variable that records the maximum number of values for each attribute, such as the first property in this example: 0, 1, 0, 1⇒2,0, 1, 2, 0⇒3,3, 0, 1, 2⇒4;
The number of bits that each attribute (feature) occupies under one hot code, and the Feature_indices_ of the object enc, which records the index position of the attribute under the new one hot code.
Feature_indices_ is the cumulative value of N_values_, but the first of feature_indices is 0;

Further encode the new eigenvector by a fit, one hot encoder:

>>> enc.transform ([[0, 1, 1]]). ToArray ()
Array ([[1.,  0., 0., 1., 0.,  0  ., 1.,  0.,  0.]])

1 2 first 2 digits 1, 0, encode 0 digits 3, 0, 1 for 0, end 1 digits 4, 0, 1, 0 encode 0;