What kind of machine learning problems can be solved using decision tree models?
Decision Tree algorithm belongs to the family of supervised learning algorithms. Unlike other supervised learning algorithms, the decision tree algorithm can be used for solving regression and classification problems too.
What is maximum depth of a decision tree?
It can also be described as the length of the longest path from the tree root to a leaf. The root node is considered to have a depth of 0. The Max Depth value cannot exceed 30 on a 32-bit machine.
Why are decision trees so hard to learn?
There are concepts that are hard to learn because decision trees do not express them easily, such as XOR, parity or multiplexer problems. Decision tree learners create biased trees if some classes dominate. It is therefore recommended to balance the dataset prior to fitting with the decision tree. 3.8.1. Classification ¶
How to control the number of samples in a decision tree?
Use min_samples_split or min_samples_leaf to control the number of samples at a leaf node. A very small number will usually mean the tree will overfit, whereas a large number will prevent the tree from learning the data. Try min_samples_leaf=5 as an initial value.
How are decision trees used in regression problems?
Decision trees can also be applied to regression problems, using the DecisionTreeRegressor class. As in the classification setting, the fit method will take as argument arrays X and y, only that in this case y is expected to have floating point values instead of integer values:
Are there any practical decision tree learning algorithms?
Consequently, practical decision-tree learning algorithms are based on heuristic algorithms such as the greedy algorithm where locally optimal decisions are made at each node. Such algorithms cannot guarantee to return the globally optimal decision tree.