title: Is pd.get_dummies one-hot encoding?
body: <p><a href="https://stats.stackexchange.com/q/224051/99338">Given</a> the difference between one-hot encoding and dummy coding, is the <code>pandas.get_dummies</code> method one-hot encoding when using default parameters (i.e. <code>drop_first=False</code>)?</p>
  
  <p>If so, does it make sense that I remove the intercept from the logistic regression model? Here is an example:</p>
  
  <pre><code># I assume I have already my dataset in a DataFrame X and the true labels in y
  import pandas as pd
  from sklearn.linear_model import LogisticRegression
  from sklearn.model_selection import train_test_split
  
  X = pd.get_dummies(X)
  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .80)
  
  clf = LogisticRegression(fit_intercept=False)
  clf.fit(X_train, y_train)
  </code></pre>