On behavior of lbjava when dealing with real valued features.

There was speculations about lbjava failing when using real-valued features.

Thanks to @Slash0BZ (Ben Zhou) we did a comprehensive experiments, running a few example problems, with different number of real-valued features, across different algorithms, and different number of iterations.

NewsGroup (table for single real feature)

Condition\Algorithm	SparseAveragedPerceptron	SparseWinnow	PassiveAggresive	SparseConfidenceWeighted	BinaryMIRA
1 round w/o real features	48.916	92.597	19.038		33.739
1 round w/ real features	47.753	92.491	23.268		32.364
10 rounds w/o real features	82.390	91.539	24.802		76.891
10 rounds w/ real features	82.126	91.529	12.427		75.939
50 rounds w/o real features	84.823	91.592	14.120		77.208
50 rounds w/ real features	85.299	91.433	19.566		76.891
100 rounds w/o real features	85.828	91.433	12.956		76.574
100 rounds w real features	84.770	91.486	15.442		61.026

NewsGroup (table for the same amount of Gaussian random real features as discrete ones)

Condition\Algorithm	SparseAveragedPerceptron	SparseWinnow	PassiveAggresive	BinaryMIRA
1 round w/o real features	51.454	92.597	12.057	33.739
1 round w/ real features	17.980	6.081	14.913	14.225
10 rounds w/o real features	82.813	91.539	22.369	76.891
10 rounds w/ real features	52.829		42.517	45.743
50 rounds w/o real features	84.294	91.592	21.100	77.208
50 rounds w/ real features	75.727		67.054	75.198
100 rounds w/o real features	85.506	91.433	17.768	76.574
100 rounds w real features	77.631		74.828	74.194

Badges (table for single real feature)

Condition\Algorithm	SparsePerceptron	SparseWinnow	NaiveBayes
1 round w/o real features	100.0	95.745	100.0
1 round w/ real features	100.0	95.745	100.0
10 rounds w/o real features	100.0	100.0	100.0
10 rounds w/ real features	100.0	100.0	100.0
50 rounds w/o real features	100.0	100.0	100.0
50 rounds w/ real features	100.0	100.0	100.0
100 rounds w/o real features	100.0	100.0	100.0
100 rounds w real features	100.0	100.0	100.0

Badges (table for same amount of constant real features as discrete features)

Condition\Algorithm	SparsePerceptron	SparseWinnow	NaiveBayes
1 round w/o real features	100.0	95.745	100.0
1 round w/ real features	74.468	100.0	100.0
10 rounds w/o real features	100.0	100.0	100.0
10 rounds w/ real features	78.723	100.0	100.0
50 rounds w/o real features	100.0	100.0	100.0
50 rounds w/ real features	100.0	100.0	100.0
100 rounds w/o real features	100.0	100.0	100.0
100 rounds w real features	100.0	100.0	100.0

Badges (table for same amount of of random Gaussian real features as discrete features)

Condition\Algorithm	SparsePerceptron	SparseWinnow	NaiveBayes
1 round w/o real features	100.0	95.745	100.0
1 round w/ real features	55.319	56.383	100.0
10 rounds w/o real features	100.0	100.0	100.0
10 rounds w/ real features	62.766	100.0	100.0
50 rounds w/o real features	100.0	100.0	100.0
50 rounds w/ real features	74.468	87.234	100.0
100 rounds w/o real features	100.0	100.0	100.0
100 rounds w real features	86.170	100.0	100.0

The conclusion made here is that, as more number of real-valued features are added, more training iterations are need to train the system.