Decision Tree Simulator
Split a small dataset on each feature and compare entropy and information gain.
The full set has 5 No and 9 Yes, so entropy is 0.94 bits. ID3 picks the feature that reduces it most.
step 1 of 20
deciding now leaf (pure or majority) split node
Information gain
No split being evaluated on this step.
- Rows at this node
- 14
- Entropy
- 0.94 bits
Your dataset
| Outlook | Temp | Humidity | Wind | Play | |
|---|---|---|---|---|---|
| Sunny | Hot | High | Weak | No | |
| Sunny | Hot | High | Strong | No | |
| Overcast | Hot | High | Weak | Yes | |
| Rain | Mild | High | Weak | Yes | |
| Rain | Cool | Normal | Weak | Yes | |
| Rain | Cool | Normal | Strong | No | |
| Overcast | Cool | Normal | Strong | Yes | |
| Sunny | Mild | High | Weak | No | |
| Sunny | Cool | Normal | Weak | Yes | |
| Rain | Mild | Normal | Weak | Yes | |
| Sunny | Mild | Normal | Strong | Yes | |
| Overcast | Mild | High | Strong | Yes | |
| Overcast | Hot | Normal | Weak | Yes | |
| Rain | Mild | High | Strong | No |
Add a row
Rows outside the current node are dimmed. Add rows that contradict an existing pattern and watch the gains shift — that is overfitting arriving.
← → to step, or use the buttons above.