When you have an issue with your internet connection and dial-up customer care, an Intelligent computer assistant is the one you are first connected to. After dialling a bunch of numbers to make decisions as to what you seek, you are finally connected to a human support system. One may often think this to be just standardized voicemail; in reality, it is a real-life example of what a decision tree in machine learning entails; helping reach the right choice.
Now, what is a decision tree? Decision Tree is a useful machine learning program that can be used for solving both classification and regression problems. They are powerful analytical models that have the ability to comprehend data with minimal pre-processing time. It is a support tool that has a tree-like structure and suggests possible effects and costs of decisions.
A decision tree presents an algorithm regarding decision making in a flowchart like structure.
A tree can be understood by dividing the source set into subsets depending on a quality worth test. This cycle is rehashed on each determined subset in a recursive way known as recursive partitioning. The recursion is said to be complete when the subset at the node is the same value as that of the target variable subset or when splitting does not add any more value to the predictions made. The development/ construction of a decision tree classifier does not need any knowledge of the domain or boundary setting.
Decision trees can deal with high dimensional data. All in all, decision trees classifier has great precision and an inductive approach to imbibe knowledge on characterization.
There are often assumptions that are made while creating a decision tree. Some of them are:
Decision trees group or classify occurrences by arranging them down the tree; from root to different leaves nodes. This provides the occurrences’’ characterization or classification. An occurrence is classified by beginning at the root node of the tree, testing the traits indicated by this node, then progressing down the tree branch by comparing it to the attribute’s value. The process is repeated for the subtree that is rooted at the new node.
The following is an example of a binary tree model. Suppose you want to anticipate whether an individual is fit given that they have provided essential data such as their age, dietary pattern, physical activities, etc. The decision nodes here would be questions like, “what’s the age”, “does he work out?”, “Does he eat too many pizzas?”. What’s more, the leaves, which are essentially the results, are either “fit” or “unfit”. The two answers make it binary classification.
Another example of binary classification would be deciding on whether circumstances are right to play tennis one morning. The instances of outlook ( that is, rain or sunny), temperature (hot or cold), humidity (high or low), wind (strong or not), etc. will form branches of the decision tree. A disjunction of different collected constraints will provide attributed value to the instance in question and in the present case, the instance being suitable weather to play tennis.
In comparison to various decision-making tools, decision trees have several advantages. Some of the are:
However, as there are pros, there are cons to the decision tree making models as well. Some of them are:
From the above discussion, it is clear that decision trees can handle non-linear data sets in an effective manner. It plays as a decision-making catalyst in various life walks, including engineering, civil planning, business, and even law. On perusal of the advantages and disadvantages involved in adopting a decision tree model must be done based on its suitability to the problem statement at hand.
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