How to use “”cross_validate“” and “”inverse_transform“” at the same time
as title, how to use these 2 methods at the same time?
for example here is my sample code
scores =cross_validate(estimator, x, y, cv=kfold, scoring = scoring,
return_train_score=True)
Here I have 2 cases: y with StandardScaler() and y with np.log1p
How can I inverse_transform and np.expm1 each of them, respectively?
If I don't do so, the prediction would be based on standardized y or log y, which I assume is wrong.
python scikit-learn cross-validation
edited Nov 25 '18 at 7:55
DYZ
26.9k62049
asked Nov 25 '18 at 7:32
chiouthebiyachiouthebiya
12
add a comment |
as title, how to use these 2 methods at the same time?
for example here is my sample code
scores =cross_validate(estimator, x, y, cv=kfold, scoring = scoring,
return_train_score=True)
Here I have 2 cases: y with StandardScaler() and y with np.log1p
How can I inverse_transform and np.expm1 each of them, respectively?
If I don't do so, the prediction would be based on standardized y or log y, which I assume is wrong.
python scikit-learn cross-validation
edited Nov 25 '18 at 7:55
DYZ
26.9k62049
asked Nov 25 '18 at 7:32
chiouthebiyachiouthebiya
12
add a comment |
as title, how to use these 2 methods at the same time?
for example here is my sample code
scores =cross_validate(estimator, x, y, cv=kfold, scoring = scoring,
return_train_score=True)
Here I have 2 cases: y with StandardScaler() and y with np.log1p
How can I inverse_transform and np.expm1 each of them, respectively?
If I don't do so, the prediction would be based on standardized y or log y, which I assume is wrong.
python scikit-learn cross-validation
edited Nov 25 '18 at 7:55
DYZ
26.9k62049
asked Nov 25 '18 at 7:32
chiouthebiyachiouthebiya
12
as title, how to use these 2 methods at the same time?
for example here is my sample code
scores =cross_validate(estimator, x, y, cv=kfold, scoring = scoring,
return_train_score=True)
Here I have 2 cases: y with StandardScaler() and y with np.log1p
How can I inverse_transform and np.expm1 each of them, respectively?
If I don't do so, the prediction would be based on standardized y or log y, which I assume is wrong.
python scikit-learn cross-validation
python scikit-learn cross-validation
edited Nov 25 '18 at 7:55
DYZ
26.9k62049
asked Nov 25 '18 at 7:32
chiouthebiyachiouthebiya
12
edited Nov 25 '18 at 7:55
DYZ
26.9k62049
asked Nov 25 '18 at 7:32
chiouthebiyachiouthebiya
12
edited Nov 25 '18 at 7:55
DYZ
26.9k62049
edited Nov 25 '18 at 7:55
DYZ
26.9k62049
edited Nov 25 '18 at 7:55
DYZ
26.9k62049
26.9k62049
asked Nov 25 '18 at 7:32
chiouthebiyachiouthebiya
12
asked Nov 25 '18 at 7:32
chiouthebiyachiouthebiya
12
asked Nov 25 '18 at 7:32
chiouthebiyachiouthebiya
12
12
add a comment |
add a comment |
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