Instance-Based Vs Model-Based Learning | Types of Machine Learning

The Machine Learning systems which are categorized as instance-based learning are the systems that learn the training examples by heart and then generalize to new instances based on some similarity measure. It is called instance-based because it builds the hypotheses from the training instances. It is also known as memory-based learning or lazy learning. The time complexity of this algorithm depends upon the size of training data. The worst-case time complexity of this algorithm is O (n), where n is the number of training instances.

A system is called model-based when it learns from the data and creates a model, which has some parameters and it predicts the output by using this data trained model.

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⌚Time Stamps⌚

00:00 – Intro
00:50 – Instance vs Model Based Learning
03:00 – Instance based Learning
07:45 – Model based Learning
11:20 – Differences
16:30 – Outro

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