R in a Nutshell

Machine Learning Algorithms for Classification

Much like regression, there are problems where linear methods don’t work well for classification. This section describes some machine learning algorithms for classification problems.

k Nearest Neighbors

One of the simplest techniques for classification problems is k nearest neighbors. Here’s how the algorithm works:

1. The analyst specifies a “training” data set.

2. To predict the class of a new value, the algorithm looks for the k observations in the training set that are closest to the new value.

3. The prediction for the new value is the class of the “majority” of the k nearest neighbors.

To use k nearest neighbors in R, use the function knn in the class package:

libary(class)
knn(train, test, cl, k = 1, l = 0, prob = FALSE, use.all = TRUE)

Here is the description of the arguments to the knn function.

Argument	Description	Default
train	A matrix or data frame containing the training data.
test	A matrix or data frame containing the test data.
cl	A factor specifying the classification of observations in the training set.
k	A numeric value specifying the number of neighbors to consider.	1
l	When k > 0, specifies the minimum vote for a decision. (If there aren’t enough votes, the value doubt is returned.)	0
prob	If prob=TRUE, then the proportion of votes for the winning class is returned as attribute prob.	FALSE
use.all	Controls the handling of ties when selecting nearest neighbors. If use.all=TRUE, then all distances equal to the kth largest are included. If use.all=FALSE ...