A decision tree is a flowchart-like structure in which each internal node represents a "test" on an attribute (e.g. whether a coin flip comes up heads or tails), each branch represents the outcome of the test, and each leaf node represents a class label (decision taken after computing all attributes).
A decision tree is a very specific type of probability tree that enables you to make a decision about some kind of process. For example, you might want to choose between manufacturing item A or item B, or investing in choice 1, choice 2, or choice 3.
Decision trees help you to evaluate your options. Decision Trees are excellent tools for helping you to choose between several courses of action. They provide a highly effective structure within which you can lay out options and investigate the possible outcomes of choosing those options.
A Decision tree is the denotative representation of a decision-making process. Decision trees in artificial intelligence are used to arrive at conclusions based on the data available from decisions made in the past. .
Therefore, decision tree models are support tools for supervised learning.
A decision tree combines some decisions, whereas a random forest combines several decision trees.
Thus, it is a long process, yet slow. Whereas, a decision tree is fast and operates easily on large data sets, especially the linear one.
The random forest model needs rigorous training.
Simple to understand and to interpret. Trees can be visualised.
Requires little data preparation. Other techniques often require data normalisation, dummy variables need to be created and blank values to be removed. Note however that this module does not support missing values.
The cost of using the tree (i.e., predicting data) is logarithmic in the number of data points used to train the tree.
Able to handle both numerical and categorical data. However scikit-learn implementation does not support categorical variables for now. Other techniques are usually specialised in analysing datasets that have only one type of variable.
See algorithms for more information.
Able to handle multi-output problems.
Uses a white box model. If a given situation is observable in a model, the explanation for the condition is easily explained by boolean logic.
By contrast, in a black box model (e.g., in an artificial neural network), results may be more difficult to interpret.
Possible to validate a model using statistical tests. That makes it possible to account for the reliability of the model.
Performs well even if its assumptions are somewhat violated by the true model from which the data were generated.
