The decision tree is an effective and widely utilized machine learning technique that is mostly used to perform classification and regression tasks. It is a predictive model tool that translates the observations made about an object to conclusions about the value it is aiming for. The structure of a decision tree is similar to an inverted tree where each node in the internal structure is a representation of a decision made that is based on a characteristic, every branch is the result of the choice, while every leaf node represents the expected value. The process of decision-making begins at the root node, and goes through to the leaf is reached. Data Science Course in Pune
The basic principle of the decision tree is explained in several steps:
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Data collection: The first step to building the decision tree is to gather an information set that consists of the characteristics (attributes) associated with the items you’d like to predict as well as the desired values you would like to forecast.
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The Best Attribute to Choose: Decision trees make decisions based on the value of attributes. The algorithm picks the attribute that gives the most efficient split, which produces the most homogeneous subsets of the variable to be studied. The process of determining the most effective attribute requires measuring the degree of impurity as well as the information gained for every attribute.
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Parting the Data: When the most effective attribute is determined the data set is then split into subsets according to the potential values for that attribute. Each subset is an individual branch of an initial decision point.
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Recursive Method: The process of selecting the most appropriate attribute and then splitting the data is repeated to each subset until a stop condition is satisfied. This stopping condition can be a predetermined level of the tree, or even a certain number of samples within the leaf node, or some other requirements.
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Leaf Nodes, as well as Decision Making The tree, continues to grow until it comes to a point at which no more splits are feasible or necessary. In this phase, the leaf nodes have been made and each leaf node symbolizes a predetermined outcome or label for a class. Data Science Classes in Pune
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Handling Numerical and Categorical Information: Decision trees can manage both numerical and categorical data. In the case of categorical data, this algorithm will check the equality of the data, while when dealing with numerical information, the algorithm examines whether the number is greater than or lower than the threshold.
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Pruning (Optional): Decision trees are vulnerable to overfitting. They get too specific training data and do poorly when faced with new data that is not seen. Pruning is a method used to combat overfitting by eliminating portions of the tree that are not able to provide a substantial predictive capability.
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Forecast: Once the decision tree is created and predictions are made for the new data is a matter of going through the tree from the root node to the leaf node, based on the rules of decision based on the value of those features that are input. Data Science Training in Pune
Decision trees offer several advantages they offer, such as ease of use, interpretation, and the ability to manage categorical and numerical data. However, they are vulnerable to fluctuations in data, and they may not perform well when dealing with complex relationships. Methods of ensemble such as Random Forests are often used to overcome these limitations by combining several decision trees to increase the overall predictive power.
