title: Decision tree algorithm for mixed numeric and nominal data
body: <p>my dataset contains a number of numeric and categorical attributes </p>
  
  <p>example: numericAttr1, numericAttr2, categoricalAttr1, numericalAttr3... where categoricalAttr values: categoricalAttrValue1, categoricalAttrValue2, categoricalAttrValue3.</p>
  
  <p>i was thinking to convert numerical data into categorical data (using binning algorithm) and apply ID3 algorithm to produce tree because ID3 deals only with discrete data. but, how to handle overfitting? and also is it correct to categorise numerical data and apply ID3 algorithm?</p>