Contents
Is the depth of a decision tree interpretable?
Decision trees are very interpretable — as long as they are short. The number of terminal nodes increases quickly with depth. The more terminal nodes and the deeper the tree, the more difficult it becomes to understand the decision rules of a tree. A depth of 1 means 2 terminal nodes.
What is the maximum number of nodes in a decision tree?
A depth of 1 means 2 terminal nodes. Depth of 2 means max. 4 nodes. Depth of 3 means max. 8 nodes. The maximum number of terminal nodes in a tree is 2 to the power of the depth. For the examples in this chapter, I used the rpart R package that implements CART (classification and regression trees).
What’s the maximum number of terminal nodes in a python tree?
The maximum number of terminal nodes in a tree is 2 to the power of the depth. For the examples in this chapter, I used the rpart R package that implements CART (classification and regression trees). CART is implemented in many programming languages, including Python.
What are the nodes of a regression tree?
A regression tree consists of internal nodes – corresponding to attributes, edges – corresponding to subsets of attribute values, and terminal nodes (leaves) – corresponding to functions mapping from attribute space to the continuous target value. The simplest and most popular function is a constant function.
How to predict the target in a decision tree?
The first 4 columns are the first 4 features that we will use to predict the target, the iris species, represented by the last column with numerical values : 0 for setosa, 1 for versicolor, 2 for virginica. In total, we have 150 observations (150 rows), 50 observations for each iris species : the dataset is balanced.
How to interpret a decision tree in machine learning?
The interpretation is simple: Starting from the root node, you go to the next nodes and the edges tell you which subsets you are looking at. Once you reach the leaf node, the node tells you the predicted outcome.
How are categorical features and encoding in decision trees?
One hot encoding requires.. as many splits as there are categories, which means a crazy lot and so much it stops very quickly because if you do one split, one part of the split will be frozen (because it is a perfect rule dataset) => it gives this escalator-like shape, thus very poorly performing.