# Can I decrease further the RMSE based on this feature?

I have a feature x, that I use to predict a probability y.

Some background on (x,y)

I can't go into too much details, but hopefully the following should be enough to explain what x and y are, at least conceptually [square and circles are NOT the actual label I am working with]:

y

y is the probability of an image being of Class 0 or 1, with:

• Class 0 means that the image contains a square.
• Class 1 means that the image contains a circle.

100 people watched the training images, and classified them. y is the result probability, so y=0 means there is definitely a square, y=1 means there is definitely a round.

x

x is a feature derived from the images, by trying to fit them to a model of a circle, and calculating the error. So for example when x is very low, the probability of the image having a circle is high (relatively).

plot(x,y) x,y (1000 values for each) pasted here: http://tny.cz/c320180d

Using mean(y) as a predictor, I get RMSE = 0.285204:

N = length(x)
average = mean(y)
RMSE = sqrt( 1/N * sum( (average-y)^2 ) )
RMSE
 0.285204


Then using a linear regression on log(x), I could improve a little bit the RMSE = 0.2694513:

log_x = log(x)
plot(log_x,y)
lm.result = lm(formula = y ~ log_x)
abline(lm.result, col="blue") # not working very well
linear_prediction = predict( lm.result, new, se.fit = TRUE)
prediction_linear_regression = matrix(0,N,1)
prediction_linear_regression = linear_prediction\$fit
RMSE_linear_regression = sqrt( 1/N * sum( (prediction_linear_regression-y)^2 ) )
RMSE_linear_regression
 0.2694513 Can the RMSE be further improved? What should I try?

• I can produce very similar looking plots by simply using plot(rlnorm(500, sdlog=0.85), runif(500)). I doubt that there is a dependency between x and y. – Roland Jan 28 '14 at 10:43
• @Roland I did not add the picture, but plot(log_x,y) on my data produces something that looks more "dependent" than your example. – Timothée HENRY Jan 28 '14 at 10:47
• Well, how do you expect people to help you, if you don't show an adequate representation of your data. What you should try depends on what your data is and what relationships you'd expect. However, I think you might have some success with a glm, e.g., using the quasibinomial family. – Roland Jan 28 '14 at 11:02
• @Roland In the mean time I did add the plot of (log_x,y). Also the raw data is available at the link given above. I just wasn't sure what to give. – Timothée HENRY Jan 28 '14 at 11:09
• What are x and y? – Glen_b -Reinstate Monica Jan 30 '14 at 21:42