There are two types of features, <font color="#1E90FF"><strong>numerical feature</strong></font> and <font color="#1E90FF"><strong>categorical feature</strong></font>.<br>
In the categorical feature, each observation is one of a discrete set of possible values.<br>
Note that <strong>some features may have to be handled as the categorical feature even though they are looks like numerical features</strong> (e.g. Country names may be being replaced to country codes).<br>
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Most machine learning algorithms cannot handle the categorical feature directly.<br>
So, <strong>the categorical features commonly transform to the numerical features</strong>.<br>
MALSS automatically transform the categorical features to the numerical features using <font color="#1E90FF"><strong>dummy variables</strong></font>.<br>