Decision Tree (DT) Model

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A Decision Tree (DT) Model is a prediction model that follows a decision tree pattern (composed of if-then nodes and edges with decision choices at the decision tree leaf nodes).



References

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2017b

Some advantages of decision trees are:
  • 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. 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.
The disadvantages of decision trees include:
  • Decision-tree learners can create over-complex trees that do not generalise the data well. This is called overfitting. Mechanisms such as pruning (not currently supported), setting the minimum number of samples required at a leaf node or setting the maximum depth of the tree are necessary to avoid this problem.
  • Decision trees can be unstable because small variations in the data might result in a completely different tree being generated. This problem is mitigated by using decision trees within an ensemble.
  • The problem of learning an optimal decision tree is known to be NP-complete under several aspects of optimality and even for simple concepts. 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. This can be mitigated by training multiple trees in an ensemble learner, where the features and samples are randomly sampled with replacement.
  • 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.

2017c

  • (Wikipedia, 2017) ⇒ https://en.wikipedia.org/wiki/Decision_tree_learning Retrieved:2017-10-15.
    • Decision tree learning uses a decision tree (as a predictive model) to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves). It is one of the predictive modelling approaches used in statistics, data mining and machine learning. Tree models where the target variable can take a discrete set of values are called classification trees ; in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees.

      In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making. In data mining, a decision tree describes data (but the resulting classification tree can be an input for decision making). This page deals with decision trees in data mining.

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  1. Breiman L, Friedman JH, Olshen R, Stone C (1984) Classification and regression trees. Wadsworth & Brooks, Pacific Grove
  2. Kass GV (1980) An exploratory technique for investigating large quantities of categorical data. Appl Stat 29:119–127
  3. Hunt EB, Marin J, Stone PJ (1966) Experiments in induction. Academic, New York
  4. Quinlan JR (1983) Learning efficient classification procedures and their application to chess end games. In: Michalski RS, Carbonell JG, Mitchell TM (eds) Machine learning. An artificial intelligence approach, Tioga, Palo Alto, pp 463–482
  5. Quinlan JR (1986) Induction of decision trees. Mach Learn 1:81–106
  6. Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32.
  7. Freund Y, Schapire RE (1996) Experiments with a new boosting algorithm. In: Saitta L (ed) Proceedings of the 13th International Conference on Machine Learning, Bari. Morgan Kaufmann, pp 148–156