-
Notifications
You must be signed in to change notification settings - Fork 0
/
model_summary.txt
70 lines (65 loc) · 4.31 KB
/
model_summary.txt
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
*--Resumen de mejores modelos--*
En data no resampleada -->
Pipeline(steps=[('imputer',
ColumnTransformer(remainder='passthrough',
transformers=[('knnimputer-1',
KNNImputer(missing_values=9.0,
n_neighbors=1),
['CH07', 'CH08', 'CH11', 'V1',
'V2', 'V3', 'V5', 'V6', 'V7',
'V8', 'V11', 'V12', 'V13',
'V14', 'PP07I_jefx']),
('knnimputer-2',
KNNImputer(missing_values=99.0,
n_neighbors=1),
['IV2', 'II1']),
('knnimputer-3',
KNNImputer(missin...
handle_unknown='infrequent_if_exist',
sparse_output=False),
['REGION', 'CH03', 'CH07',
'CH15', 'CH09', 'CH16',
'ESTADO', 'ESTADO_jefx',
'ESTADO_conyuge',
'PP02E'])],
verbose_feature_names_out=False)),
('reduce_dim', 'passthrough'),
('classifier',
BaggingClassifier(estimator=LogisticRegression(C=0.01,
class_weight='balanced',
penalty='l1',
solver='liblinear'),
n_estimators=22))]).
Su f1_score fue de 0.47
En data resampleada con AllKNN-->
Pipeline(steps=[('imputer',
ColumnTransformer(remainder='passthrough',
transformers=[('knnimputer-1',
KNNImputer(missing_values=9.0,
n_neighbors=1),
['CH07', 'CH08', 'CH11', 'V1',
'V2', 'V3', 'V5', 'V6', 'V7',
'V8', 'V11', 'V12', 'V13',
'V14', 'PP07I_jefx']),
('knnimputer-2',
KNNImputer(missing_values=99.0,
n_neighbors=1),
['IV2', 'II1']),
('knnimputer-3',
KNNImputer(missin...
handle_unknown='infrequent_if_exist',
sparse_output=False),
['REGION', 'CH03', 'CH07',
'CH15', 'CH09', 'CH16',
'ESTADO', 'ESTADO_jefx',
'ESTADO_conyuge',
'PP02E'])],
verbose_feature_names_out=False)),
('reduce_dim', 'passthrough'),
('classifier',
BaggingClassifier(estimator=LogisticRegression(C=0.0096875,
class_weight='balanced',
penalty='l1',
solver='liblinear'),
n_estimators=14))]).
Su f1_score fue de 0.45