Scala and Spark for Big Data Analytics

OneHotEncoder

A one-hot encoding maps a column of label indices to a column of binary vectors, with at most a single value. This encoding allows algorithms that expect continuous features, such as Logistic Regression, to use categorical features. Suppose you have some categorical data in the following format (the same that we used for describing the StringIndexer in the previous section):

Figure 14: DataFrame for applying OneHotEncoder

Now, we want to index the name column so that the most frequent name in the dataset (that is, Jason in our case) gets index 0. However, what's the use of just indexing them? In other words, you can further vectorize ...