Describe how and why you should partition your data when using classification techniques like k- nearest neighbors and logistic regression

The KNN (k-nearest neighbour) method can be used for both classification and regression problems. KNN is a type of instance-based learning, or lazy learning where the function is only approximated locally and all computation is deferred until classification. The KNN algorithm is amongst the simplest of all machine learning algorithms: an object is classified by a majority vote of its neighbors, with the object being assigned to the class most common amongst its k nearest neighbors (k is a positive integer, typically small). If k = 1, then the object is simply assigned to the class of its nearest neighbor.

Pros and cons of KNN:

Pros

1. Easy to understand

2. No assumptions about data

3. Can be applied to both classification and regression

4. Works easily on multi class problems

Cons

1. Memory intensive/ Computationally expensive

2. Sensitive to scale of data

3. Does not work well on rare event (skewed) target variable

4. Struggle when high number of independent variables