Classification $>$ Decision Tree $>$ Gain Ratio

Consider the training examples shown below for a binary classification problem. Construct a decision tree, only one level using Gain Ratio and multi$-$way splits.

$\backslash$table$[[$ID$,$Major,Income,Gender,Decision$],[1,$Computer,Low,M$,$No$],[2,$Industrial,Medium,M$,$No$],[3,$Industrial,Medium,M$,$No$],[4,$Mechanical,Medium,M$,$No$],[5,$Electronics,Medium,M$,$No$],[6,$Electronics,Medium,M$,$No$],[7,$Computer,Medium,F$,$No$],[8,$Computer,Medium,F$,$No$],[9,$Industrial,Medium,F$,$No$],[10,$Mechanical,High,F$,$No$],[11,$Mechanical,Low,M$,$Yes$],[12,$Electronics,Low,M$,$Yes$],[13,$Industrial,Low,M$,$Yes$],[14,$Electronics,High,M$,$Yes$],[15,$Computer,High,F$,$Yes$],[16,$Computer,High,F$,$Yes$],[17,$Industrial,High,F$,$Yes$],[18,$Industrial,High,F$,$Yes$],[19,$Industrial,High,F$,$Yes$],[20,$Computer,High,F$,$Yes$]]$ Consider the training examples shown below for a binary classification problem. Construct a decision tree, only one level using Gain Ratio and multi$-$way splits.