Machine learning employs classification, a bifurcated process of learning and forecasting. The model construction is based on learned data, while it predicts the outcome for given data. The Decision Tree is one such commonly-used, simplistic method for classification. Classified as a supervised learning algorithm, the Decision Tree serves both classification and regression problem-solving. Utilization of Decision Trees comes in hand for formulating a training model, harnessing basic decision rules from past data, for predicting target variable's class or value.
For class label prediction via Decision Trees, the process commences at the root, comparing the root content with the record's attribute. Depending upon the comparison, going forth involves following the branch corresponding to the value and moving to the next node.
Decision Tree Types: They are distinguished based on the target variable type.
The two types include:
- Categorical Variable Decision Tree: This type of Decision Tree involves a categorical target variable.
- Continuous Variable Decision Tree: This Decision Tree type incorporates a continuous target variable.
The process of classification using decision trees implies sorting them from root to a leaf/terminal node with the terminal node providing the classification. Each node in the tree is a test case representing a property, and every descendant edge from a node corresponds to the potential solutions for the test case. Such a process is a repetitive loop happening for each subtree rooted at the new node.
The Strategy for it to Work
The precision of a Decision Tree relies heavily upon making strategic splits. The criteria differ for classification and regression trees.
Several metrics are used by Decision Trees to decide upon the division of a node into multiple sub-nodes. As the sub-nodes are created, their uniformity tends to increase and node purity improves with an elevation in the target variable. Post considering all the relevant parameters, a Decision Tree branches the nodes into sub-nodes, selecting the split that results in highly uniform sub-nodes. The target variables type also influences the algorithm selection while working on a Decision Tree.
Benefits of Decision Trees
Decision Trees are apt for explaining complex processes in simpler terms, making understanding swift and convenient even for first-time users. It uncovers a balanced view of the decision-making process, considering risks and-based rewards and facilitating probability- and fact-based decisions rather than vulnerability to individual prejudices.
Decision Trees clarify rewards and risks with a predictive framework outlining multiple alternatives and supporting the decision-making process. It helps protect the decisions against unwarranted risks or unfavorable outcomes.
The non-linear nature of Decision Trees allows it to be adaptable for exploring and forecasting different outcomes, regardless of their occurrence in time.
Utilizing Decision Trees significantly augments decision-making skills, promoting a holistic view of the entire process – right from defining the root decision, weighing risks and rewards, different courses of action (branches), to potential outcomes (leaves).