Supervised Learning - Classification Week 8 Challenge
2......What are the pros and cons of knn
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K- Nearest Neighbors or also known as K-NN belong to the family of supervised machine learning algorithms which means we use labeled (Target Variable) dataset to predict the class of new data point. The K-NN algorithm is a robust classifier which is often used as a benchmark for more complex classifiers such as Artificial Neural Network (ANN) or Support vector machine (SVM).
Advantages:-
- No Training Period- KNN modeling does not include training period as the data itself is a model which will be the reference for future prediction and because of this it is very time efficient in term of improvising for a random modeling on the available data.
- Easy Implementation- KNN is very easy to implement as the only thing to be calculated is the distance between different points on the basis of data of different features and this distance can easily be calculated using distance formula such as- Euclidian or Manhattan
- As there is no training period thus new data can be added at any time since it wont affect the model.
Disadvantages:-
- Does not work well with large dataset as calculating distances between each data instance would be very costly.
- Does not work well with high dimensionality as this will complicate the distance calculating process to calculate distance for each dimension.
- Sensitive to noisy and missing data
- Feature Scaling- Data in all the dimension should be scaled (normalized and standardized) properly .