110 lines
6.1 KiB
Plaintext
110 lines
6.1 KiB
Plaintext
ColumnDataClassifier invoked at Thu Oct 28 01:47:49 PDT 2010 with arguments:
|
|
-prop projects/core/data/edu/stanford/nlp/classify/iris2007.prop
|
|
Gold answer column is 0
|
|
numDatums: 130
|
|
numLabels: 3 [Iris-setosa, Iris-versicolor, Iris-virginica]
|
|
numFeatures (Phi(X) types): 5
|
|
QNMinimizer called on double function of 15 variables, using M = 15.
|
|
An explanation of the output:
|
|
Iter The number of iterations
|
|
evals The number of function evaluations
|
|
SCALING <D> Diagonal scaling was used; <I> Scaled Identity
|
|
LINESEARCH [## M steplength] Minpack linesearch
|
|
1-Function value was too high
|
|
2-Value ok, gradient positive, positive curvature
|
|
3-Value ok, gradient negative, positive curvature
|
|
4-Value ok, gradient negative, negative curvature
|
|
[.. B] Backtracking
|
|
VALUE The current function value
|
|
TIME Total elapsed time
|
|
|GNORM| The current norm of the gradient
|
|
{RELNORM} The ratio of the current to initial gradient norms
|
|
AVEIMPROVE The average improvement / current value
|
|
|
|
Iter ## evals ## <SCALING> [LINESEARCH] VALUE TIME |GNORM| {RELNORM} AVEIMPROVE
|
|
|
|
Iter 1 evals 1 <D> [113M 7.322E-4] 1.343E2 0.01s |1.294E2| {8.774E-1} 0.000E0
|
|
Iter 2 evals 5 <D> [M 1.000E0] 1.218E2 0.01s |1.203E2| {8.153E-1} 5.149E-2
|
|
Iter 3 evals 6 <D> [1M 2.579E-1] 9.447E1 0.01s |3.005E2| {2.038E0} 1.406E-1
|
|
Iter 4 evals 8 <D> [M 1.000E0] 6.653E1 0.02s |8.207E1| {5.564E-1} 2.548E-1
|
|
Iter 5 evals 9 <D> [M 1.000E0] 5.801E1 0.02s |6.732E1| {4.564E-1} 2.631E-1
|
|
Iter 6 evals 10 <D> [M 1.000E0] 5.165E1 0.02s |5.683E1| {3.853E-1} 2.668E-1
|
|
Iter 7 evals 11 <D> [M 1.000E0] 4.106E1 0.02s |1.640E1| {1.112E-1} 3.245E-1
|
|
Iter 8 evals 12 <D> [M 1.000E0] 3.117E1 0.02s |1.342E1| {9.098E-2} 4.137E-1
|
|
Iter 9 evals 13 <D> [M 1.000E0] 2.379E1 0.02s |7.857E0| {5.327E-2} 5.162E-1
|
|
Iter 10 evals 14 <D> [M 1.000E0] 2.081E1 0.03s |4.359E0| {2.955E-2} 5.454E-1
|
|
Iter 11 evals 15 <D> [M 1.000E0] 1.948E1 0.03s |2.471E0| {1.675E-2} 4.775E-1
|
|
Iter 12 evals 16 <D> [M 1.000E0] 1.822E1 0.03s |2.027E0| {1.374E-2} 3.804E-1
|
|
Iter 13 evals 17 <D> [1M 2.803E-1] 1.765E1 0.03s |3.416E0| {2.316E-2} 2.517E-1
|
|
Iter 14 evals 19 <D> [1M 3.647E-1] 1.742E1 0.04s |5.151E0| {3.492E-2} 2.118E-1
|
|
Iter 15 evals 21 <D> [M 1.000E0] 1.733E1 0.04s |5.687E0| {3.856E-2} 1.800E-1
|
|
Iter 16 evals 22 <D> [M 1.000E0] 1.715E1 0.04s |4.654E0| {3.155E-2} 1.268E-1
|
|
Iter 17 evals 23 <D> [13M 2.793E-1] 1.654E1 0.04s |2.496E1| {1.692E-1} 8.040E-2
|
|
Iter 18 evals 26 <D> [M 1.000E0] 1.625E1 0.05s |1.109E1| {7.520E-2} 4.218E-2
|
|
Iter 19 evals 27 <D> [M 1.000E0] 1.616E1 0.05s |6.277E0| {4.256E-2} 2.615E-2
|
|
Iter 20 evals 28 <D> [M 1.000E0] 1.611E1 0.05s |1.533E0| {1.039E-2} 1.902E-2
|
|
Iter 21 evals 29 <D> [1M 3.770E-1] 1.610E1 0.05s |7.873E-1| {5.337E-3} 1.197E-2
|
|
Iter 22 evals 31 <D> [M 1.000E0] 1.610E1 0.05s |4.135E-1| {2.804E-3} 8.769E-3
|
|
Iter 23 evals 32 <D> [M 1.000E0] 1.609E1 0.06s |5.164E-1| {3.501E-3} 7.497E-3
|
|
Iter 24 evals 33 <D> [M 1.000E0] 1.608E1 0.06s |9.113E-1| {6.179E-3} 7.047E-3
|
|
Iter 25 evals 34 <D> [M 1.000E0] 1.606E1 0.06s |1.449E0| {9.821E-3} 6.179E-3
|
|
Iter 26 evals 35 <D> [M 1.000E0] 1.603E1 0.06s |1.457E0| {9.876E-3} 2.920E-3
|
|
Iter 27 evals 36 <D> [M 1.000E0] 1.601E1 0.06s |6.130E-1| {4.156E-3} 1.393E-3
|
|
Iter 28 evals 37 <D> [M 1.000E0] 1.600E1 0.06s |1.423E-1| {9.646E-4} 9.225E-4
|
|
Iter 29 evals 38 <D> [M 1.000E0] 1.600E1 0.06s |3.959E-1| {2.684E-3} 6.343E-4
|
|
Iter 30 evals 39 <D> [M 1.000E0] 1.599E1 0.07s |3.814E-1| {2.586E-3} 6.082E-4
|
|
Iter 31 evals 40 <D> [M 1.000E0] 1.599E1 0.07s |1.383E-1| {9.376E-4} 6.128E-4
|
|
Iter 32 evals 41 <D> [1M 2.468E-1] 1.599E1 0.07s |6.242E-1| {4.232E-3} 5.882E-4
|
|
Iter 33 evals 43 <D> [M 1.000E0] 1.599E1 0.07s |5.413E-2| {3.670E-4} 5.399E-4
|
|
Iter 34 evals 44 <D> [M 1.000E0] 1.599E1 0.07s |9.543E-2| {6.470E-4} 3.936E-4
|
|
Iter 35 evals 45 <D> [M 1.000E0] 1.599E1 0.07s |7.560E-2| {5.125E-4} 2.130E-4
|
|
Iter 36 evals 46 <D> [M 1.000E0] 1.599E1 0.07s |2.585E-1| {1.752E-3} 1.090E-4
|
|
Iter 37 evals 47 <D> [1M 2.684E-1] 1.599E1 0.07s
|
|
QNMinimizer terminated due to average improvement: | newest_val - previous_val | / |newestVal| < TOL
|
|
Total time spent in optimization: 0.07s
|
|
Built this classifier: LinearClassifier [printing top 200 features]
|
|
(3-Value,Iris-virginica) 3.9660
|
|
(4-Value,Iris-virginica) 3.9054
|
|
(2-Value,Iris-setosa) 2.7759
|
|
(CLASS,Iris-versicolor) 2.5068
|
|
(1-Value,Iris-setosa) 1.3780
|
|
(1-Value,Iris-versicolor) 0.6052
|
|
(CLASS,Iris-setosa) 0.5928
|
|
(2-Value,Iris-versicolor) 0.0735
|
|
(3-Value,Iris-versicolor) -0.0444
|
|
(1-Value,Iris-virginica) -1.8754
|
|
(4-Value,Iris-setosa) -1.9822
|
|
(4-Value,Iris-versicolor) -1.9982
|
|
(2-Value,Iris-virginica) -2.8192
|
|
(CLASS,Iris-virginica) -3.1561
|
|
(3-Value,Iris-setosa) -3.8551
|
|
|
|
Output format: dataColumn1 goldAnswer classifierAnswer P(classifierAnswer)
|
|
5 Iris-setosa Iris-setosa 0.995615365125735
|
|
4.6 Iris-setosa Iris-setosa 0.9994804135630505
|
|
5.1 Iris-setosa Iris-setosa 0.9937095680980086
|
|
4.9 Iris-setosa Iris-setosa 0.9905109629700247
|
|
5.4 Iris-setosa Iris-setosa 0.9982151488134486
|
|
4.4 Iris-setosa Iris-setosa 0.9944214428148407
|
|
5.3 Iris-setosa Iris-setosa 0.9984497925740373
|
|
6.1 Iris-versicolor Iris-versicolor 0.8873152482428373
|
|
6 Iris-versicolor Iris-versicolor 0.9424246013278404
|
|
5.5 Iris-versicolor Iris-versicolor 0.9030026595536319
|
|
6.5 Iris-versicolor Iris-versicolor 0.928816167001929
|
|
6.8 Iris-versicolor Iris-versicolor 0.9569376555329442
|
|
6.2 Iris-versicolor Iris-versicolor 0.9857141927233324
|
|
6.7 Iris-virginica Iris-virginica 0.9698639532763317
|
|
6.4 Iris-virginica Iris-virginica 0.8982390073296296
|
|
5.7 Iris-virginica Iris-virginica 0.9920401400173403
|
|
6.7 Iris-virginica Iris-virginica 0.968576539063806
|
|
6.8 Iris-virginica Iris-virginica 0.9957320369272686
|
|
7.7 Iris-virginica Iris-virginica 0.9900526044768513
|
|
7.3 Iris-virginica Iris-virginica 0.9766204287594443
|
|
|
|
20 examples in test set
|
|
Cls Iris-setosa: TP=7 FN=0 FP=0 TN=13; Acc 1.000 P 1.000 R 1.000 F1 1.000
|
|
Cls Iris-versicolor: TP=6 FN=0 FP=0 TN=14; Acc 1.000 P 1.000 R 1.000 F1 1.000
|
|
Cls Iris-virginica: TP=7 FN=0 FP=0 TN=13; Acc 1.000 P 1.000 R 1.000 F1 1.000
|
|
Micro-averaged accuracy/F1: 1.00000
|
|
Macro-averaged F1: 1.00000
|