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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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