What happens with increasing amounts of random noise

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Reference no: EM131944406

Problem

1. Experiment with the effects of feature scaling in linear regression. For a given data set with at least two features (dimensions), multiply all the values of one feature by 10k , for -10 ≤ k ≤ 10. Does this operation cause a loss of numerical accuracy in fitting?

2. Experiment with the effects of highly correlated features in linear regression. For a given (x, y) data set, replicate the value of x with small but increasing amounts of random noise. What is returned when the new column is perfectly correlated with the original? What happens with increasing amounts of random noise?

Reference no: EM131944406

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