LogitBoost: Base classifiers and their weights:
Iteration 1
Class 1 (class=won)
Decision Stump
Classifications
rimmx = t : 2.0
rimmx != t : -0.33843797856049007
rimmx is missing : 0.08886107634543179
Two-class case: second classifier predicts additive inverse of first classifier and is not explicitly computed.
Iteration 2
Class 1 (class=won)
Decision Stump
Classifications
wknck = t : -1.170752730379179
wknck != t : 0.8190510371480552
wknck is missing : 0.09702868965849262
Two-class case: second classifier predicts additive inverse of first classifier and is not explicitly computed.
Iteration 3
Class 1 (class=won)
Decision Stump
Classifications
bxqsq = t : -1.5315548328070518
bxqsq != t : 0.5514058404466711
bxqsq is missing : -0.038865384657999706
Two-class case: second classifier predicts additive inverse of first classifier and is not explicitly computed.
Iteration 4
Class 1 (class=won)
Decision Stump
Classifications
rimmx = f : -0.3191282753222436
rimmx != f : 1.8048435050102227
rimmx is missing : -0.010868786576717818
Two-class case: second classifier predicts additive inverse of first classifier and is not explicitly computed.
Iteration 5
Class 1 (class=won)
Decision Stump
Classifications
wkna8 = t : -2.52298293662905
wkna8 != t : 0.254935875159362
wkna8 is missing : 0.07564506017432254
Two-class case: second classifier predicts additive inverse of first classifier and is not explicitly computed.
Iteration 6
Class 1 (class=won)
Decision Stump
Classifications
bxqsq = f : 0.19081106267596482
bxqsq != f : -0.7120420818571985
bxqsq is missing : -0.012377956948632184
Two-class case: second classifier predicts additive inverse of first classifier and is not explicitly computed.
Iteration 7
Class 1 (class=won)
Decision Stump
Classifications
rimmx = t : 1.4607483085569923
rimmx != t : -0.15445318884317794
rimmx is missing : -0.014359481036576204
Two-class case: second classifier predicts additive inverse of first classifier and is not explicitly computed.
Iteration 8
Class 1 (class=won)
Decision Stump
Classifications
wknck = t : -0.7211061762549681
wknck != t : 0.38844684182421724
wknck is missing : 0.04320129041557483
Two-class case: second classifier predicts additive inverse of first classifier and is not explicitly computed.
Iteration 9
Class 1 (class=won)
Decision Stump
Classifications
bkxbq = t : 0.44047062821297245
bkxbq != t : -0.40728983090751314
bkxbq is missing : -0.01607656510323878
Two-class case: second classifier predicts additive inverse of first classifier and is not explicitly computed.
Iteration 10
Class 1 (class=won)
Decision Stump
Classifications
katri = w : 0.9526746826050289
katri != w : -0.16088273345004342
katri is missing : -0.010048775528178884
Two-class case: second classifier predicts additive inverse of first classifier and is not explicitly computed.
Iteration 11
Class 1 (class=won)
Decision Stump
Classifications
wkpos = t : 0.2557672255286528
wkpos != t : -0.6398488718876022
wkpos is missing : 0.016508488906930508
Two-class case: second classifier predicts additive inverse of first classifier and is not explicitly computed.
Iteration 12
Class 1 (class=won)
Decision Stump
Classifications
bxqsq = t : -0.6812063538903412
bxqsq != t : 0.1921190239250559
bxqsq is missing : -0.002292983795406514
Two-class case: second classifier predicts additive inverse of first classifier and is not explicitly computed.
Iteration 13
Class 1 (class=won)
Decision Stump
Classifications
rimmx = t : 1.511429204024245
rimmx != t : -0.14842283909361362
rimmx is missing : -0.012151742946304868
Two-class case: second classifier predicts additive inverse of first classifier and is not explicitly computed.
Iteration 14
Class 1 (class=won)
Decision Stump
Classifications
wknck = f : 0.23146492134270322
wknck != f : -0.39431929797228893
wknck is missing : 0.04400956601685348
Two-class case: second classifier predicts additive inverse of first classifier and is not explicitly computed.
Iteration 15
Class 1 (class=won)
Decision Stump
Classifications
r2ar8 = f : 0.5056101301440562
r2ar8 != f : -0.2247976375919549
r2ar8 is missing : -0.011933796085694809
Two-class case: second classifier predicts additive inverse of first classifier and is not explicitly computed.
Iteration 16
Class 1 (class=won)
Decision Stump
Classifications
rimmx = t : 1.3993842559645777
rimmx != t : -0.054082142270726725
rimmx is missing : 0.012868303655418528
Two-class case: second classifier predicts additive inverse of first classifier and is not explicitly computed.
Iteration 17
Class 1 (class=won)
Decision Stump
Classifications
bxqsq = f : 0.17882877383042042
bxqsq != f : -0.8701027423415522
bxqsq is missing : 0.022245269553010757
Two-class case: second classifier predicts additive inverse of first classifier and is not explicitly computed.
Iteration 18
Class 1 (class=won)
Decision Stump
Classifications
mulch = f : 0.040296673002494836
mulch != f : -1.2617098105247284
mulch is missing : -0.019009530656660337
Two-class case: second classifier predicts additive inverse of first classifier and is not explicitly computed.
Iteration 19
Class 1 (class=won)
Decision Stump
Classifications
rxmsq = t : -0.9163602096349522
rxmsq != t : 0.06294069173665827
rxmsq is missing : -0.015169525364312791
Two-class case: second classifier predicts additive inverse of first classifier and is not explicitly computed.
Iteration 20
Class 1 (class=won)
Decision Stump
Classifications
qxmsq = f : -0.05603887108223932
qxmsq != f : 1.3487572487682649
qxmsq is missing : 0.0020763674207622805
Two-class case: second classifier predicts additive inverse of first classifier and is not explicitly computed.
Number of performed iterations: 20