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For  example,  suppose  you  have  a  dataset  of  1000  images  of  flowers  in  your  garden.  Without  any  additional
              information about the flowers such as names, colours, or other features, it would be challenging to discern any
              patterns in this dataset. By employing a learning-based approach with an AI model, the machine could discover
              various patterns based on the features of these 1000 images. It might cluster the data based on colour, size, shape,
              etc. It might also come up with some very unusual clustering algorithm, which you might not have even thought of!


                                                                                      Learning-based Approach


                                                                  Unlabelled Data



                                                                Used to Train Dataset        Model  Output





















                                                       Output is clustered based on patterns observed by the machine:
                                                           Left is based on colour, while Right is based on shape.



              Decision Tree—Rule-based Approach
              Decision trees are  tools that  follow  a  rule-based  approach  that  uses a  tree-like  model of  decisions and  their
              possible consequences. It is a kind of flow chart, where the flow starts at the root node and ends with a decision
              made at the leaves. It is used to depict conditions and their outcomes. It is one of the most widely used and
              practical methods for supervised learning.

              The decision tree starts from the root node just like the structure of a tree with two different ways or conditions:
              Yes or No. The forks or diversions are known as Branches of the tree. The branches either lead to another decision/
              question node or they lead to another condition for decision, which is known as the leaf node. If you look closely
              at the image, it looks like an inverted tree with roots above and leaves below. That's why it's called the decision tree.
              So let's revise some important terms related to the decision tree:
                 • Root node: A root node is the first node of a decision tree and it represents the entire set of data.
                 • Branching: Dividing the node at one level into two or more sub-nodes at the next level.
                 • Decision node: Dividing a node further into another level sub-node.

                 • Leaf node: A node that does not split further.
                 • Parent node: A node that is a level above a sub-node.
                 • Child node: A sub-node that falls under another node.




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