Decision Tree Algorithm is an artificial intelligence technique used to make complex decisions by generating a flow chart like structure for solving decision problems.
Decision Tree Algorithm is one of the most widely used machine learning techniques. It is used for both regression and classification problems. This book first gives an introduction to DBfs, which is used to test the performance of DTA using standard datasets. It then lays out the mathematics behind DTA, explains how to perform building trees, and explores extensions of this algorithm.
Using Python Pandas library you can connect to any dataset. You can split the columns into Input(s) and output column.
Decision tree algorithm takes inputs and output to fit the model and then apply logic on it.
This is one of the most effective algorithm to solve classification related problems.
How to import decision tree algorithm in Jupyter Notebook using Python.
from sklearn.tree import DecisionTreeClassifier
Decision tree algorithms are widely used in pattern recognition, data mining and machine learning for many real-world decision making problems such as classification, regression and feature selection. Decision trees are among the most popular and versatile tools in machine learning. It has been used in such wide areas such as data mining and pattern recognition, financial and healthcare industries, and recently also in social network analysis.
Decision tree algorithms are a class of decision-making algorithms for problem solving and data processing. Decision trees depict the possible outcomes and their respective probabilities, which aid in root cause analysis and provide insight into the probability of events.
This module helps the user to learn the basics of decision tree, decision tree construction algorithm, accuracy assessment, applications of decision trees. It also teaches pragmatic aspects of its use to perform prediction, risk assessment and ordering.
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