# Getting Negative Information Gain (using Entropy as a measure of disorder)

I'm creating a Decision Tree and at the very root level itself, I'm getting negative Information Gain.

As per my knowledge, Information Gain is always > 0....

Please look at the node below.....

    [69+,42-]
/   \
/     \
[56+,33-]  [11+,11-]

IG = H([69+, 42-]) - H([56+,33-],[11+,11-])

= {(-69/111 * lg(69/111) -42/111 * lg(42/111))
- 89/111 * ( -56/89*lg(56/89) -33/89*lg(33/89))
- 22/111 * ( -11/22*lg(11/22) -11/22*lg(11/22))}

= -0.004


It turns out that for every feature, the IG < 0.

What should I do to decide the feature at the root node?

Thanks