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DT_Kombination_IEMO_MSP
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109 lines (86 loc) · 4.27 KB
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DT trainiert auf IEMO, getestet auf MSP:
Accuracy: 0.2757000195809673
precision recall f1-score support
A 0.20 0.32 0.25 746
H 0.26 0.66 0.37 1261
N 0.54 0.13 0.21 2293
S 0.48 0.03 0.06 807
accuracy 0.28 5107
macro avg 0.37 0.29 0.22 5107
weighted avg 0.41 0.28 0.23 5107
[[ 241 467 35 3]
[ 330 836 92 3]
[ 537 1429 307 20]
[ 112 534 137 24]]
pred: (array(['A', 'H', 'N', 'S'], dtype='<U1'), array([1220, 3266, 571, 50]))
test: (array(['A', 'H', 'N', 'S'], dtype='<U1'), array([ 746, 1261, 2293, 807]))
train: (array(['A', 'H', 'N', 'S'], dtype='<U1'), array([1090, 1615, 1695, 1075]))
DT trainiert auf MSP, getested auf IEMO:
Accuracy: 0.3522644265887509
precision recall f1-score support
A 0.39 0.16 0.23 1091
H 0.40 0.29 0.34 1623
N 0.35 0.39 0.37 1690
S 0.31 0.59 0.41 1072
accuracy 0.35 5476
macro avg 0.37 0.36 0.34 5476
weighted avg 0.37 0.35 0.34 5476
[[178 411 278 224]
[106 466 607 444]
[100 228 656 706]
[ 69 54 320 629]]
pred: (array(['A', 'H', 'N', 'S'], dtype='<U1'), array([ 453, 1159, 1861, 2003]))
test: (array(['A', 'H', 'N', 'S'], dtype='<U1'), array([1091, 1623, 1690, 1072]))
train: (array(['A', 'H', 'N', 'S'], dtype='<U1'), array([ 746, 1260, 2289, 811]))
Beide Datensätze zusammengefasst:
Accuracy: 0.47895229186155286
precision recall f1-score support
A 0.45 0.46 0.46 550
H 0.44 0.36 0.40 885
N 0.51 0.60 0.55 1201
S 0.48 0.43 0.45 571
accuracy 0.48 3207
macro avg 0.47 0.46 0.46 3207
weighted avg 0.48 0.48 0.47 3207
[[254 133 138 25]
[180 317 312 76]
[104 214 722 161]
[ 22 56 250 243]]
pred: (array(['A', 'H', 'N', 'S'], dtype='<U1'), array([ 560, 720, 1422, 505]))
test: (array(['A', 'H', 'N', 'S'], dtype='<U1'), array([ 550, 885, 1201, 571]))
train: (array(['A', 'H', 'N', 'S'], dtype='<U1'), array([1307, 2023, 2820, 1332]))
______________________________________________________________________________________________
DT trainiert auf IEMO, getestet auf MSP mit Begrenzung der Klassen auf 754 Datenpunkte:
Accuracy: 0.3020557029177719
precision recall f1-score support
A 0.25 0.34 0.29 754
H 0.27 0.51 0.35 754
N 0.46 0.30 0.36 754
S 0.46 0.06 0.10 754
accuracy 0.30 3016
macro avg 0.36 0.30 0.28 3016
weighted avg 0.36 0.30 0.28 3016
[[254 462 33 5]
[283 387 78 6]
[345 142 227 40]
[116 441 154 43]]
pred: (array(['A', 'H', 'N', 'S'], dtype='<U1'), array([ 998, 1432, 492, 94]))
test: (array(['A', 'H', 'N', 'S'], dtype='<U1'), array([754, 754, 754, 754]))
train: (array(['A', 'H', 'N', 'S'], dtype='<U1'), array([1103, 1636, 1708, 1084]))
DT trainiert auf MSP mit Begrenzung der Klassen auf 754 Datenpunkte, getested auf IEMO:
Accuracy: 0.3192912674019165
precision recall f1-score support
A 0.42 0.24 0.30 1103
H 0.38 0.14 0.20 1636
N 0.33 0.27 0.30 1708
S 0.28 0.75 0.41 1084
accuracy 0.32 5531
macro avg 0.35 0.35 0.30 5531
weighted avg 0.35 0.32 0.29 5531
[[ 263 308 275 257]
[ 264 228 397 747]
[ 100 55 459 1094]
[ 5 11 252 816]]
pred: (array(['A', 'H', 'N', 'S'], dtype='<U1'), array([ 632, 602, 1383, 2914]))
test: (array(['A', 'H', 'N', 'S'], dtype='<U1'), array([1103, 1636, 1708, 1084]))
train: (array(['A', 'H', 'N', 'S'], dtype='<U1'), array([754, 754, 754, 754]))