Contents
Why do we need decision tree?
Decision trees provide an effective method of Decision Making because they: Clearly lay out the problem so that all options can be challenged. Allow us to analyze fully the possible consequences of a decision. Provide a framework to quantify the values of outcomes and the probabilities of achieving them.
How is decision tree trained?
Decision Tree models are created using 2 steps: Induction and Pruning. Induction is where we actually build the tree i.e set all of the hierarchical decision boundaries based on our data. Because of the nature of training decision trees they can be prone to major overfitting.
How are decisions made in a decision tree?
To reach to the leaf, the sample is propagated through nodes, starting at the root node. In each node a decision is made, to which descendant node it should go. A decision is made based on the selected sample’s feature.
What kind of nodes are in a decision tree?
There are typically two types of leaf nodes: square leaf nodes, which indicate another decision to be made, and circle leaf nodes, which indicate a chance event or unknown outcome. When formed together, these elements loosely resemble a tree, which is where the diagram gets its name. USE THIS DECISION TREE TEMPLATE
What does it mean to have a non linear decision tree?
Decision trees are non-linear, which means there’s a lot more flexibility to explore, plan and predict several possible outcomes to your decisions, regardless of when they actually occur.
When does branching occur in a decision tree?
Branching or ‘splitting’ is what we call it when any node divides into two or more sub-nodes. These sub-nodes can be another internal node, or they can lead to an outcome (a leaf/ end node.) Sometimes decision trees can grow quite complex. In these cases, they can end up giving too much weight to irrelevant data.