While not a machine learning package, this statistics library utilizes the Gnu Scientific Library to provide me with the two features upon which this task depended: Multiple Regression and Pearson r Correlation Coefficient. Here are some examples of both at work:

# Calculate correlation coefficient b=(1..100).collect { rand(100)}.to_scale Statsample::Bivariate.pearson(a,b) # Multiple Regression a=1000.times.collect {rand}.to_scale b=1000.times.collect {rand}.to_scale c=1000.times.collect {rand}.to_scale ds={'a'=>a,'b'=>b,'c'=>c}.to_dataset ds['y']=ds.collect{|row| row['a']*5+row['b']*3+row['c']*2+rand()} lr=Statsample::Regression.multiple(ds,'y') puts lr.summary Summary for regression of a,b,c over y ************************************************************* Engine: Statsample::Regression::Multiple::AlglibEngine Cases(listwise)=1000(1000) r=0.986 r2=0.973 Equation=0.504+5.011a + 2.995b + 1.988cSource: http://ruby-statsample.rubyforge.org/