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• Training Data: The classification model is trained on a dataset known as training data. This dataset contains labelled
examples, where each data instance is paired with a class label. The model learns the relationship between features
and class labels from this data.
• Classification Model: An algorithm or technique is used to create the classification model. The model learns from the
training data to predict class labels for new, unseen data instances, generalising patterns and relationships from the
training data to make accurate predictions.
• Prediction or Inference: After training, the classification model is used to predict class labels for new data instances.
This process, known as prediction or inference, that uses the learned patterns and relationships from the training data.
Task #Collaboration & Teamwork
Form a group of 5 students. Each group should think and come up with one use case from the classroom
environment or their home/society, where they would like to apply classification algorithm to solve the problem.
Real Life Applications of Classification
In classification, our data is categorised into a preferred and distinct number of classes while assigning label to each
class. Applications of classification in real life include:
• Speech recognition
• Handwriting recognition
• Face tagging as done by Facebook
• Detecting fraudulent transactions in banks
• Predicting whether advertisements on a website to be clicked or not
• Product classification
• Document classification
#Digital Literacy
Video Session
• Scan the QR code or visit the following link to watch the video: Classification in Machine Learning
https://www.youtube.com/watch?v=xG-E--Ak5jg
• Scan the QR code or visit the following link to watch the video: Difference between
Classification and Regression
https://www.youtube.com/watch?v=9rRLo4nFc3A
After watching both the videos, answer the following questions:
1. Write any two differences between Classification and Regression.
2. What is classification model according to the video?
340 Touchpad Artificial Intelligence (Ver. 3.0)-XI

