I have 165000 observations. Each observation is 180 component vector. One component in this vector is an integer in range from 0 to 200.
Every vector is constructed from readings of 180 sensors. Sensors fail unpredictably, so some vector components may be undefined. On average 40% - 60% of random vector components are undefined in every vector. I am trying to find an approximation function for each component of these vectors. What other methods for prediction with incomplete data can be used for a task I just described? >From Ted Dunning <ted.dunn...@gmail.com> >Subject Re: Function approximation in Mahout? >Date Wed, 21 Apr 2010 14:34:58 GMT > >Mahout does not have a lot of regression capabilities at this time, other >than various forms of binomial regression (SVM, logistic regression, >decision forests) but other forms of regression are relatively lacking. > >Commons math has some capabilities, but not in a particularly scalable form. > >What size is your problem? > >On Tue, Apr 20, 2010 at 2:07 PM, Dmitri O.Kondratiev <doko...@gmail.com>wrote: > >> Hello, >> Does Mahout support any function approximation frameworks, such as greedy >> function approximation with gradient boosting (TreeNet)? >> http://en.wikipedia.org/wiki/TreeNet#Names >> >> Thanks! >>
