# Decision tree model

What is a Decision Tree? How does it work? It works for both categorical and continuous input and output variables. In this problem, we need to segregate students who play cricket in their leisure time based on highly significant input variable among all three. The vector x is composed of the features, x1, x2, x3 etc. An example tree which estimates the probability of kyphosis after surgery, given the age of the patient and the vertebra at which surgery was started.

The same tree is shown in three different ways. Left The colored leaves show the probability of kyphosis after surgery, and percentage of patients in the leaf. Middle The tree as a perspective plot. Right Aerial view of the middle plot. The probability of kyphosis after surgery is higher in the darker areas.

The treatment of kyphosis has advanced considerably since this rather small set of data was collected. Decision tree types[ edit ] Decision trees used in data mining are of two main types: Classification tree analysis is when the predicted outcome is the class to which the data belongs.

Regression tree analysis is when the predicted outcome can be considered a real number e. With increasing the number of samples in nodes and reducing the tree width, decision stream preserves statistically representative data and allows extremely deep graph architecture that can consist of hundreds of levels.

Boosted trees Incrementally building an ensemble by training each new instance to emphasize the training instances previously mis-modeled.

A typical example is AdaBoost. These can be used for regression-type and classification-type problems. While less expressive, decision lists are arguably easier to understand than general decision trees due to their added sparsity, permit non-greedy learning methods  and monotonic constraints to be imposed.

A decision tree is a flow-chart-like structure, where each internal non-leaf node denotes a test on an attribute, each branch represents the outcome of a test, and each leaf or terminal node holds a class label. The topmost node in a tree is the root node. There are many specific decision-tree algorithms.

Performs multi-level splits when computing classification trees. Statistics-based approach that uses non-parametric tests as splitting criteria, corrected for multiple testing to avoid overfitting. This approach results in unbiased predictor selection and does not require pruning.

Metrics[ edit ] Algorithms for constructing decision trees usually work top-down, by choosing a variable at each step that best splits the set of items. These generally measure the homogeneity of the target variable within the subsets. Some examples are given below.

These metrics are applied to each candidate subset, and the resulting values are combined e.The decision tree model and the composite score approach correctly classified a similar number of cases; however, the decision tree model was superior in classifying cases with sunburn (% correctly classified in the decision tree vs % with the composite score).

Decision Tree Consulting has been building robust, scalable applications for over a decade. We match companies with the right solutions to . A 1D regression with decision tree.

The decision trees is used to fit a sine curve with addition noisy observation. As a result, it learns local linear regressions approximating the sine curve. We can see that if the maximum depth of the tree (controlled by the max_depth parameter) is set too high.

Chapter 4 Classiﬁcation Decision Tree Induction This section introduces a decision tree classiﬁer, which is a simple yet widely used classiﬁcation technique. Boosted Decision Tree Regression. 01/11/; 7 minutes to read Contributors. 