Decision Trees Applications

Decision Tree Overview In data mining techniques two kinds of basic learning processes are available namely supervised and unsupervised. when we talk about the supervised learning techniques the decision tree learning is one of the most essential technique of classification and prediction. A number of different kinds of decision tree algorithms are available i.e. ID3, C4.5, C5.0, CART, SLIQ and others. All these algorithms the used to generate the transparent data models. these data models can be evaluated using the paper and pencil. therefore that is an effective data modeling technique. Applications of Decision Trees Application of Decision Tree Algorithm in Healthcare Operations [1]: the decision trees are used to visualize the data patterns in form of tree data structure. that help to also prepare the relationship among the attributes and the final class labels. thus the patient’s different health attributes can help to understand the symptoms and possibility by comparing the historical data available with the similar attributes. Manufacturing and Production: in a large production based industries where the regulation of production and planning is required. the decision tree models helps for understanding the amount of production, time of production and other scenarios. that can be evaluated using the past scenarios of…

ID3 Decision Tree in Data Mining

ID3 Decision Tree Overview Engineered by Ross Quinlan the ID3 is a straightforward decision tree learning algorithm. The main concept of this algorithm is construction of the decision tree through implementing a top-down, greedy search by the provided sets for testing every attribute at each node of decision. With the aim of selecting the attribute which is most useful to classify a provided set of data, a metric is introduced named as Information Gain [1]. To acquire the finest way for classification of learning set, one requires to act for minimizing the fired question (i.e. to minimize depth of the tree). Hence, some functions are needed that is capable of determine which questions will offer the generally unbiased splitting. One such function is information gain metric. Entropy In order to define information gain exactly, we require discussing entropy first. Let’s assume, without loss of simplification, that the resultant decision tree classifies instances into two categories, we’ll call them ​\( [ P_{positive} ] and [ N_{negative} ] \)​ Given a set S, containing these positive and negative targets, the entropy of S related to this Boolean classification is: ​\( [ P_{positive} ] \)​: proportion of positive examples in S ​\( [ N_{negative} ]…

Introduction of Decision Trees

Decision Tree:Overview in different kinds of supervised data mining techniques the decision trees are one of the most popular classification and prediction technique. basically the training data samples are organized in form of tree data structure. where the nodes of tree shows the attributes of the data set and edges can be used for demonstrating the values of these attributes. additionally the leaf node of the tree contains the decisions of the classifier. example the decision tree as given in figure 1. Figure 1 decision tree example in the above given figure the decision tree model is demonstrated which contains decisions in terms of yes or no at the leaf nodes. similarly the humidity, outlook and wind are the attributes which are available in data set. additionally the relevant attribute attribute values that are frequently occurred during the evaluation of patterns. sometimes these trees can also used as the IF THEN ELSE rules. from the above given example a rule can be defined as: IF (Outlook = sun & Humidity = normal) then decision = yes Advantages the following are the key advantages of any decision tree: Decision tree are simple to understand and construct even after a brief exploration….

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