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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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