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
14 июл 2024
