Machine Learning :
Machine Learning is a subset of AI, which have the ability to learn from the data trained to it and make predictions from that data. Machine learning contains a set of algorithms that work on a huge amount of data. Data is fed to algorithms to train , and on the basis of training, they build the model & perform a specific task.
Supervised Data :
There are three types of machine learning supervised, unsupervised, and reinforcement learning. Decision Tree is the most powerful algorithm that falls under the category of supervised algorithms. supervised machine learning uses training data sets to achieve desired results. These data sets contain inputs and the correct output that helps the model to learn faster.
Tree models in Machine Learning :
Tree-based models are built based on if-then rules to generate predictions from one or more decision trees.Decision tree, Random Forest, Gradient Boosting falls under tree models in ml. We can use tree based models for both regression and classification.
Decision Tree :
Decision tree can be used for both classification and regression tasks. Decision trees are extremely useful for data analytics and machine learning because they break down complex data into more manageable parts. They’re often used in these fields for prediction analysis, data classification, and regression.
To explain in simple terms decision tree is a binary tree that recursively spilts dataset until we left with leaf node. Decision tree is a graphical representation of all the possible solutions to a decision based on given conditions.
Structure of Decision Tree :
It has a hierarchical, tree structure, which consists of a root node, branches, internal nodes and leaf nodes. If we go through the below fig. we can say the first Decision node will be considered as the root node and it is a parent node to the following decision nodes. Internal nodes are the child nodes of root node and parent nodes for the leaf nodes. Leaf nodes have no child nodes. Decision nodes are given based on the decisions or if-else conditions. We are getting possible results based on the decisions made.
Let us learn thoroughly about decision tree with an example. Let is imagine a professor want to grade his students results based on the marks obtained by them. If the grading system goes as Distinction with first class for marks greater than 70, Distinction for marks greater than 50 and less than 70, exam passed for marks greater than 35 and retake an exam for marks less than 35. It is graphically represented in the below decision tree mode very clearly.
From the above requirement we created a decision tree, and gave a condition if marks are greater than 70 then grade will be "Distinction with First Class", else it is diverted to another decision node with condition marks less than 50, if the condition satisfies it means the marks are between 50 and 70 so the decision fall under "Distinction", else it is diverted to another decision node with condition marks less than 35, if it is true then the decision falls under "Retake exam". Else if student got marks greater than 35 and less than 50 so he comes under Exam passed category.
Types of Decision Tree :
Decision trees can be divided into two types categorical variable and continuous variable decision trees.
1.Categorical variable : In categorical variable decision tree data is placed into a single category based on the decisions at the nodes throughout the tree.
2.Continuous variable : In continuous variable decision tree the decision or outcome variable depends on other decisions or type of choice taken in the decision node.
Decision tree is one of the powerful algorithm for regression and classification. It have a specific type of probability tree that enables you to make a decision about some kind of process.