Introduction
One of the areas of artificial intelligence that has advanced the fastest is machine learning. A large number of machine learning algorithms have especially become popular because of their openness. The Classification and Regression Trees (CART) algorithm, sometimes referred to as the Decision Tree method, is one of them.
A classification technique called the CART is necessary to create a decision tree based on Gini's impurity index. It offers a wide range of use cases and is a fundamental machine learning method. The term was first used by statistician Leo Breiman to refer to Decision Tree algorithms that may be applied to classification or regression predictive modelling problems.
Knowledge of Decision Tree
In the domains of statistics, data mining, and machine learning, a decision tree is a method for doing predictive analysis. The decision tree is the predictive model used in this case, and it is used to go from observations about an item, which are represented by branches, to the item's target value, which is represented by leaves, before coming to a conclusion. Among the most often used machine learning techniques are decision trees due to their readability and simplicity.
Three key components make up a decision tree's structure: Leaf nodes, Internal nodes, and Root nodes. The training data set is the initial node, or root node, in the diagram, followed by the internal node and the leaf node. As the point at which the node further splits depending on the best feature of the sub-group, the internal node serves as the decision-making node. The node that contains the decision is the leaf node or final node.
algorithm CART
The threshold value of an attribute is used to divide the nodes in the decision tree into subnodes. The Gini Index criteria is used by the CART algorithm to find the subnodes' subnodes with the best homogeneity.
The training set is the root node, which is divided into two by taking the best attribute and threshold value into account. Additionally, the subsets are divided according to the same rationale. This continues until the tree has either produced all of its potential leaves or found its final pure sub-set. Tree pruning is another name for this.
Benefits of the CART algorithm
Since the CART method is nonparametric, it is independent of the data from any particular distribution.
To more accurately assess the quality of fit, the CART method combines cross-validation with testing against a test data set.
With CART, the same variables may be used again in several tree areas. This ability may be used to show complex interdependencies between sets of variables.
Input variable outliers have no appreciable impact on CART.
Decision trees can be allowed to outgrow by loosing stopping constraints, and then they can be pruned down to their optimal size. By ending too soon, this strategy lessens the possibility of overlooking crucial structure in the data set.
Conclusion
One of the most potent machine learning algorithms, Random Forest, has the CART method as a component. The CART algorithm is structured as a sequence of questions, the answers to which determine whether or not to ask another. When there are no more questions, the final result of these questions is a tree-like structure with terminal nodes.
This approach is frequently applied for classifying data and doing regression to create decision trees. In data mining, decision trees are frequently used to build models that forecast the value of a target based on the values of numerous input variables.
