Contents
How do you handle bimodal distribution in machine learning?
Here are a couple of approaches and their gotchas:
- You could treat them as binary values(as Kevin) mentions.
- You could split them into two different variables(high_var1 and low_var1) and do multi-variable regression .
- Ignore this bimodal variable.
How do you explain a bimodal histogram?
A histogram of a bimodal data set will exhibit two peaks or humps. For example, a histogram of test scores that are bimodal will have two peaks. These peaks will correspond to where the highest frequency of students scored.
How can we deal with bimodal variables in indepednet?
The assumption of identical of indepednet normal distribution is for residuals rather than the dependent variable. The raw data of outcome might not be normal or unimod. If we only have y and x: If the independent variable X is binary and has significant effect on the dependent variable Y, the dependent variable will be bimodal.
What to do with data that are bimodal at two tails of the distribution?
What to do with data that are bimodal at two tails of the distribution? I am in a weird position where I prespecified a plan to use linear regression to analyze my data, and stated I would use transformations to address any assumption violations.
How can we deal with bimodal variable frequen?
One frequen case is that the distribution is produced by a mixture of populations. Another way of obtaining two modes appears when the support of the variable is restricted to an interval.
Why is regression bad for bimodal data?
To see why this might be bad, take a true linear regression y i = a + b x i + e i (assume a, b > 0 for simplicity). Now suppose we trim all values y i above 15 to 15. What happens is for the large y i > 15 is that the corresponding large x i no longer sits on the straight line, and sits on a slope of roughly zero (not the “true slope” b ).