Reference no: EM131267028

The predictive task in this question is to predict the level of corruption in a country based on a range of macro-economic and social features. The table below lists some countries described by the following descriptive features:
The target feature is the Corruption Perception Index (CPI). The CPI measures the perceived levels of corruption in the public sector of countries and ranges from 0 (highly corrupt) to 100 (very clean)

We will use Russia as our query country for this question. The table below lists the descriptive features for Russia.

a. What value would a 3-nearest neighbor prediction model using Euclidean distance return for the CPI of Russia?

b. What value would a weighted k-NN prediction model return for the CPI of Russia? Use k = 16 (i.e., the full dataset) and a weighting scheme of the reciprocal of the squared Euclidean distance between the neighbor and the query.

c. The descriptive features in this dataset are of different types. For example, some are percentages, others are measured in years, and others are measured in counts per 1,000. We should always consider normalizing our data, but it is particularly important to do this when the descriptive features are measured in different units. What value would a 3-nearest neighbor prediction model using Euclidean distance return for the CPI of Russia when the descriptive features have been normalized using range normalization?

d. What value would a weighted k-NN prediction model-with k = 16 (i.e., the full dataset) and using a weighting scheme of the reciprocal of the squared Euclidean distance between the neighbor and the query-return for the CPI of Russia when it is applied to the range-normalized data?

e. The actual 2011 CPI for Russia was 2.4488. Which of the predictions made was the most accurate? Why do you think this was?