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This approach is followed when the dataset fed to the machine is unlabelled and too random. The machine tries
        to extract similar features and clusters them in the same datasets. In the end, the machine tells the trends which
        are observed in the training data.

        For example, suppose you have a dataset of 1000 images of flowers in your garden.  Now you do not have
        any clue as to what trend is being followed in this dataset as you don’t know their names, colour or any other
        feature. Thus, you would put this into a learning approach-based AI machine and the machine would come up
        with various patterns it has observed in the features of these 1000 images. It might cluster the data on the basis
        of colour, size, shape, etc. It might also come up with some very unusual clustering algorithm which you might
        not have even thought of!


                                                                                   Learning Approach


                                                            Unlabelled Data



                                                          Used to Train Dataset        Model  Output





















                                                 Output is clustered based on patterns observed by the machine:
                                                      Left is based on color, 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 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.


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