Interactive Decision Tree Simulator

Step 1 of 6

Initial Dataset

We start with our root node containing the entire dataset. Let's look at what features give us the best split.

Root Node

The topmost node that represents the entire dataset before any splits occur.

Decision Node / Splitting

A node that tests a condition (e.g., "Outlook = Sunny") and splits the data into further branches.

Leaf Node

A terminal node that does not split further. It provides the final prediction or classification.

Entropy & Information Gain

Entropy: Measures how mixed (impure) the data is. 0 means all data belongs to one class (pure).

Information Gain: The reduction in entropy achieved by splitting data on a specific feature. The tree always chooses the feature with the highest Info Gain.

Full "Play Tennis" Dataset

Day	Outlook	Temp	Humidity	Wind	Play
1	Sunny	Hot	High	Weak	No
2	Sunny	Hot	High	Strong	No
3	Overcast	Hot	High	Weak	Yes
4	Rain	Mild	High	Weak	Yes
5	Rain	Cool	Normal	Weak	Yes
6	Rain	Cool	Normal	Strong	No
7	Overcast	Cool	Normal	Strong	Yes
8	Sunny	Mild	High	Weak	No
9	Sunny	Cool	Normal	Weak	Yes
10	Rain	Mild	Normal	Weak	Yes
11	Sunny	Mild	Normal	Strong	Yes
12	Overcast	Mild	High	Strong	Yes
13	Overcast	Hot	Normal	Weak	Yes
14	Rain	Mild	High	Strong	No

Root Data

Decision Split

Predict: YES

Predict: NO

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