Why is the KNN Algorithm known as Lazy Learner? How to find the best value for K in the KNN algorithm? Justify your(5+10=15 marks)answer

The KNN algorithm is known as a "Lazy Learner" because it does not learn a discriminative function from the training data. Instead, it memorizes the training instances and uses them to classify new instances based on their similarity to the training instances. This means that the algorithm does not make any assumptions about the underlying data distribution and does not perform any explicit training or model building.

To find the best value for K in the KNN algorithm, we can use a technique called cross-validation. Cross-validation involves splitting the training data into multiple subsets, or folds. For each fold, we train the KNN algorithm with different values of K and evaluate its performance on the remaining folds. We repeat this process for different values of K and select the value that gives the best performance, usually measured by metrics such as accuracy or F1 score.

The justification for using cross-validation to find the best value for K is that it helps us avoid overfitting or underfitting the model. If we choose a very small value of K, the model may be too sensitive to noise in the data and result in overfitting. On the other hand, if we choose a very large value of K, the model may become too generalized and result in underfitting. By using cross-validation, we can find the optimal value of K that balances the trade-off between bias and variance, leading to better generalization performance on unseen data.

In conclusion, the KNN algorithm is known as a "Lazy Learner" because it does not learn a discriminative function from the training data. To find the best value for K in the KNN algorithm, we can use cross-validation to evaluate the performance of different values of K and select the one that gives the best trade-off between bias and variance.