A Decision tree is the denotative representation of a decision-making process. Decision trees in artificial intelligence are used to arrive at conclusions based on the data available from decisions made in the past. Further, these conclusions are assigned values, deployed to predict the course of action likely to be taken in the future.
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Decision trees are statistical, algorithmic models of machine learning that interpret and learn responses from various problems and their possible consequences. As a result, decision trees know the rules of decision-making in specific contexts based on the available data. The learning process is continuous and based on feedback. This improves the outcome of learning over time. This kind of learning is called supervised learning. Therefore, decision tree models are support tools for supervised learning.
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Thus, decision trees provide a scientific decision-making process based on facts and values rather than intuition. In business, organizations use this process to make significant business decisions.
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Type of Decision Tree Models
These models can be used to solve problems depending upon the kind of data that requires prediction. They fall into the following categories:
- Prediction of continuous variables
- Prediction of categorical variables
1. Prediction of Continuous Variables
The prediction of continuous variables depends on one or more predictors. For instance, the prices of houses in an area may depend on many variables such as an address, availability of amenities like a swimming pool, number of rooms, etc. In this case, the decision tree will predict a house’s price based on various variable values. The predicted value will also be a variable value.
The decision tree model used to indicate such values is called a continuous variable decision tree. Continuous various decision trees solve regression-type problems. In such cases, labeled datasets are used to predict a continuous, variable, and numbered output.
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2. Prediction of Categorical Variables
The prediction of categorical variables is also based on other categorical or continuous variables. However, instead of predicting a value, this problem is about classifying a new dataset into the available classes of datasets. For example, analyzing a comment on Facebook to classify text as negative or supportive. Performing diagnosis for illness based on a patient’s symptoms is also an example of a categorical variable decision tree model. Categorical variable decision trees solve classification-type problems where the output is a class instead of a value.
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How Decision Trees in Artificial Intelligence Are Created
As the name suggests, the decision tree algorithm is in the form of a tree-like structure. Yet, it is inverted. A decision tree starts from the root or the top decision node that classifies data sets based on the values of carefully selected attributes.
The root node represents the entire dataset. This is where the first step in the algorithm selects the best predictor variable. It makes it a decision node. It also classifies the whole dataset into various classes or smaller datasets.
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The set of criteria for selecting attributes is called Attribute Selection Measures (ASM). ASM is based on selection measures, including information gain, entropy, Gini index, Gain ratio, and so on. These attributes, also called features, create decision rules that help in branching. The branching process splits the root node into sub-nodes, splitting further into more sub-nodes until leaf nodes are formed. Leaf nodes cannot be divided further.
Also Read: Classification in Decision Tree
Determining whether a given picture is that of a cat or a dog is a typical example of classification. Here, the features or attributes could be the presence of claws or paws, length of ears, type of tongue, etc. The dataset will be split further into smaller classes based on these input variables until the result is obtained.
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Decision trees are classic and natural learning models. They are based on the fundamental concept of divide and conquer. In the world of artificial intelligence, decision trees are used to develop learning machines by teaching them how to determine success and failure. These learning machines then analyze incoming data and store it.
Then, they make innumerable decisions based on past learning experiences. These decisions form the basis for predictive modeling that helps to predict outcomes for problems. In business, organizations use these techniques to make innumerable small and big business decisions leading to giant gains or losses.
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What is a decision tree model in AI?
Decision trees have been around for a long time but have been recently gaining prominence in the machine learning and data science community. A decision tree is a model that learns from data and helps you predict the class of an object based on a set of features. A decision tree is an acyclic graph in which each node represents a test on an input feature and each branch represents the outcome of a test. By traversing down the tree, the root node contains the prediction for the entire tree
What are the applications of decision trees?
The models are used in several fields. In computational biology, they are often used to predict biological functions of proteins or DNA sequences. In epidemiology, they are used to determine the risk of disease transmission by affected individuals. In data mining, they are used to predict future events based on previous events. In finance, they are used to determine the risk of default, and in software engineering, they are used to determine the priority of software defects.
What are the issues faced by decision tree algorithm?
The biggest problem with decision tree models is that, in many cases, all possible trees are not enumerated, even when the number of possible states (nodes) is infinite, such as in the case of an unknown BLEU score. As we know a decision tree is a machine learning algorithm. It is a binary decision tree. A tree where each internal node represents a test on a variable and each leaf node represents a class label. The decision tree has to make decisions on a particular data tuple. This representation is not very good when it comes to making a decision on a continuous variable. Also it has a limitation to work on data that can be encoded as a categorical variable.