Decision Tree Classifier component

Decision Tree learns a set of if/then rules from your data. It’s the most interpretable model in the toolbox — you can trace exactly why a prediction was made.

Configuration

Parameter	Description	Default
Max Depth	Maximum tree depth. Shallower trees generalize better. Leave empty for unlimited.	None
Min Samples Split	Minimum samples to split a node.	`2`
Min Samples Leaf	Minimum samples in a leaf node.	`1`
Criterion	Split quality measure: `gini` or `entropy`	`gini`
Random State	Seed for reproducibility.	`42`

Input / Output

	Type
Input	`X Train` + `Y Train`
Output	Trained Model

When to use

Good for understanding feature importance and building interpretable models. Does not require scaling. Prone to overfitting — use Max Depth to control tree size.

Support Vector Classifier (SVC) component Gradient Boosting Classifier component

​Configuration

​Input / Output

​When to use

Configuration

Input / Output

When to use