title: Dummy variables, is necessary to standardize them?
body: <p>I have the following dataset represented like <code>numpy</code> array</p>
  
  <pre><code>direccion_viento_pos
  
      Out[32]:
  
      array([['S'],
             ['S'],
             ['S'],
             ...,
             ['SO'],
             ['NO'],
             ['SO']], dtype=object)
  </code></pre>
  
  <p>The dimension of this array is:</p>
  
  <pre><code>direccion_viento_pos.shape
  (17249, 8)
  </code></pre>
  
  <p>I am using python and scikit learn to encode these categorical variables in this way:</p>
  
  <pre><code>from __future__ import unicode_literals
  import pandas as pd
  import numpy as np
  # from sklearn import preprocessing
  # from matplotlib import pyplot as plt
  from sklearn.preprocessing import MinMaxScaler
  from sklearn.preprocessing import LabelEncoder, OneHotEncoder
  </code></pre>
  
  <p>Then I create a label encoder object:</p>
  
  <pre><code>labelencoder_direccion_viento_pos = LabelEncoder() 
  </code></pre>
  
  <p>I take the column position 0 (the unique column) of the <code>direccion_viento_pos</code> and apply the <code>fit_transform()</code> method addressing all their rows:</p>
  
  <pre><code> direccion_viento_pos[:, 0] = labelencoder_direccion_viento_pos.fit_transform(direccion_viento_pos[:, 0]) 
  </code></pre>
  
  <p>My <code>direccion_viento_pos</code> is of this way:</p>
  
  <pre><code>direccion_viento_pos[:, 0]
  array([5, 5, 5, ..., 7, 3, 7], dtype=object)
  </code></pre>
  
  <p>Until this moment, each row/observation of <code>direccion_viento_pos</code> have a numeric value, but I want solve the inconvenient of weight in the sense that there are rows with a value more higher than others.</p>
  
  <p>Due to this, I create the dummy variables, <a href="https://discuss.analyticsvidhya.com/t/what-is-a-dummy-variable/18960" rel="noreferrer">which according to this reference</a> are:</p>
  
  <blockquote>
    <p>A Dummy variable or Indicator Variable is an artificial variable created to represent an attribute with two or more distinct categories/levels</p>
  </blockquote>
  
  <p>Then, in my <code>direccion_viento_pos</code> context, I have 8 values</p>
  
  <ul>
  <li><code>SO</code> - Sur oeste</li>
  <li><code>SE</code> - Sur este</li>
  <li><code>S</code> - Sur </li>
  <li><code>N</code> - Norte</li>
  <li><code>NO</code> - Nor oeste</li>
  <li><code>NE</code> - Nor este</li>
  <li><code>O</code> - Oeste</li>
  <li><code>E</code> - Este</li>
  </ul>
  
  <p>This mean, 8 categories.
  Next, I create a <a href="http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html#sklearn.preprocessing.OneHotEncoder" rel="noreferrer" title="sklearn.preprocessing.OneHotEncoder">OneHotEncoder</a> object with the <code>categorical_features</code> attribute which specifies what features will be treated like categorical variables.</p>
  
  <p><code>onehotencoder = OneHotEncoder(categorical_features = [0])</code></p>
  
  <p>And apply this <code>onehotencoder</code> to our <code>direccion_viento_pos</code> matrix.</p>
  
  <p><code>direccion_viento_pos = onehotencoder.fit_transform(direccion_viento_pos).toarray()</code></p>
  
  <p>My <code>direccion_viento_pos</code> with their categorized variables has stayed so:</p>
  
  <pre><code>direccion_viento_pos
  
  array([[0., 0., 0., ..., 1., 0., 0.],
         [0., 0., 0., ..., 1., 0., 0.],
         [0., 0., 0., ..., 1., 0., 0.],
         ...,
         [0., 0., 0., ..., 0., 0., 1.],
         [0., 0., 0., ..., 0., 0., 0.],
         [0., 0., 0., ..., 0., 0., 1.]])
  </code></pre>
  
  <p>Then, until here, I've created dummy variables to each category.</p>
  
  <p><img src="https://cldup.com/W-GUXDLAXD-3000x3000.png" alt="Dirección del viento categorizada" title="Dirección del viento categorizada"></p>
  
  <p>I wanted to narrate this process, to arrive at my question.</p>
  
  <p>If these dummy encoder variables already in a 0-1 range, is necessary apply the MinMaxScaler feature scaling?</p>
  
  <p>Some say that it is not necessary to scale these fictitious variables. Others say that if necessary because we want accuracy in predictions</p>
  
  <p>I ask this question due to when I apply the <code>MinMaxScaler</code> with the <code>feature_range=(0, 1)</code>
  my values have been changed in some positions ... despite to still keep this scale.</p>
  
  <p>What is the best option which can I have to choose with respect to my dataset <code>direccion_viento_pos</code></p>