Can continuous variables have negative values?
Many values are not possible, such as negative values (e.g., the Joneses cannot have −2 children) or decimal values (e.g., the Smiths cannot have 2.2 children). In contrast, a continuous variable is defined as a variable that can take on any value within a certain range given a precise enough measurement instrument.
Why log is not defined for negative values?
The logarithm of a negative number is not defined as a negative number is equal to the odd power of a negative number. For X to be negative in the earlier relation, a has to be a negative number and b has to be odd. If a were negative, for most values of X, there wouldn’t be a corresponding value for b.
What to do with negative values in regression?
Thus many readers should have learned about cube roots in secondary school but inverse hyperbolic sines are more likely to seem exotic or mysterious. In the case of negative values, you can use the PowerTransformer (method=’yeo-johnson’) method from sklearn. It is capable of handling positive and negative values, also values of zero.
Is there a transformation I can do with positive, negative values?
I can’t do a natural log transformation because of the 0 and negative values, can’t square or cube it due to 0 values, and the Box-Cox transformation works only for positive and 0 values. Is there a transformation I can do that works for all of these?
Can you do a natural log transformation with negative values?
I have a multiple linear regression model with several dependent variables that have positive, negative, and zero values, and are not normally distributed. I can’t do a natural log transformation because of the 0 and negative values, can’t square or cube it due to 0 values, and the Box-Cox transformation works only for positive and 0 values.
Is it possible to get negative predicted values?
Which is totally wrong since I cannot have negative value for my dependent variable. It seems like because of my normalization where I got negative value for my feature, I ended up with a negative predicted value for some tuple. How can is it possible and how can I fix this ?