The error distribution for different K values follows a parabolic curve with a single minimum. In the former, it is 18 while in the latter the value is 20. The optimum values of K for 10-fold CV as well as LOOCV are very close. Knn.pred=knn(train.X,train.X,train.wage,k=i)
#Install islr package in r for mac code#
Sample R code for Training and Test Split 50:50 # Training and Test Split 50:50 Note that Job class is a binary variable and Education is an ordinal variable with 5 levels. Only White data is considered for prediction. Case I: Holdout sample: Training and Test For an accurate prediction, an optimum value of K is to be determined. For a large K, the prediction is not expected to be accurate, since the bias will be high. As a result error in the training data is small, but the test error is expected to be high. If K > 1, then the most frequent class among the nearest neighbors is assigned to the new observation.įor K = 1 in the training data, there is always overfitting. If K = 1, the new observation is classified as having the same class as the nearest neighbor. K can take any value from 1 onwards, depending on the size of the data set. The predictors are used to compute the similarity. Briefly, KNN is a simple classifier which classifies a new observation based on similarity measure computed amongst 'nearest neighbors'.
The learning algorithm used for prediction is the K-nearest neighbor algorithm which will be considered later in the course. Of the predictors included in the data year, age, education, and job class are considered as having an impact on the wage. The objective is to predict accurately whether a worker will have raw wage above 100 or not. The data has been used before in Lesson 1 and it was noted that there are 3000 observations of which 2480 are White and the rest Black, Asian or Other. Wage data is available in the ISLR package in R.
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