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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. 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!




                                     Unlabelled   Learning-based
                                       Data       Approach     Output
                                     Used to Train
                                       Dataset
                                                    Model
                                                                  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               height > 5.9
                   data.                                                          Yes         No
                    • Branching: Dividing  the node at one level into
                   two or more sub-nodes at the next level.                 Male                weight <= 150
                    • Decision  node: Dividing  a node further into                           No           Yes
                   another level sub-node.
                    • Leaf node: A node that does not split further.                foot size >= 10           Female
                    • Parent node: A node that is a level above a sub-            Yes         No
                   node.
                    • Child node: A sub-node that falls under another        Male               Female
                   node.

                 Let us understand about decision trees with the help of an example, where you need to identify the person is male
                 or female based on the height, weight, and foot size.
                 Following are some of the important points to consider while designing a decision tree:

                    • There can be a possibility of multiple decision trees that lead to correct prediction for a single dataset. The
                   simplest one should be chosen.


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