Hello troubleshooters! this article is a progressive part of the series of 100days of machine learning do consider checking out the previous nodes of this on-going series where I tried to make the topics simpler from the scratch. In today's article we would discussing about the machine learning models on the basis of their learning behavior. Let's dig right into the topic :

INSTANCE BASED 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.

Some of the instance-based learning algorithms are :

1. K Nearest Neighbor (KNN)
2. Self-Organizing Map (SOM)
3. Learning Vector Quantization (LVQ)
4. Locally Weighted Learning (LWL)

MODEL BASED LEARNING

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.

INSTANCE Vs MODEL BASED LEARNING

In general, instance-based learning algorithms are better for problems where the training data is small and the patterns are simple. Model-based learning algorithms are better for problems where the training data is large and the patterns are complex.

SHARING SOME ILLUSTRATIONS TO MAKE ABOVE TOPICS SIMPLER ext

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So yeah that's all for the mentioned topic hope it would help out. In the upcoming article series would head towards challenges in the field of machine learning!

Read till here thanks a bunch:)