MinMaxScaler inverse_transform diferente size array
So I have a Scaler:
scaler = MinMaxScaler(feature_range=(0, 1))
that has 9 columns, where column 0 is my Y, and i use my scaler to work all columns.
When I make the predict:
yhat = model.predict(test_X)
I want to use the same scaler so I can transform back my values to normal, but now my output only has 1 column, and my scaler has 9 and this is a problem.
So what I am hopping to find is a way where I can do something like, grab the scaler and tell him "inverse_transform using the [0] column to work my prediction out."
Is there a way to do this?
Or the only way is to do other Scaler for my Y column and use it?
python scikit-learn keras lstm
edited Nov 12 at 17:21
jkerian
12.9k23551
asked Nov 12 at 15:45
saga56
206
add a comment |
So I have a Scaler:
scaler = MinMaxScaler(feature_range=(0, 1))
that has 9 columns, where column 0 is my Y, and i use my scaler to work all columns.
When I make the predict:
yhat = model.predict(test_X)
I want to use the same scaler so I can transform back my values to normal, but now my output only has 1 column, and my scaler has 9 and this is a problem.
So what I am hopping to find is a way where I can do something like, grab the scaler and tell him "inverse_transform using the [0] column to work my prediction out."
Is there a way to do this?
Or the only way is to do other Scaler for my Y column and use it?
python scikit-learn keras lstm
edited Nov 12 at 17:21
jkerian
12.9k23551
asked Nov 12 at 15:45
saga56
206
Use two separate scaler, one for the 8 features ofXand a second one for the outputy.
– elcombato
Nov 12 at 16:32
add a comment |
So I have a Scaler:
scaler = MinMaxScaler(feature_range=(0, 1))
that has 9 columns, where column 0 is my Y, and i use my scaler to work all columns.
When I make the predict:
yhat = model.predict(test_X)
I want to use the same scaler so I can transform back my values to normal, but now my output only has 1 column, and my scaler has 9 and this is a problem.
So what I am hopping to find is a way where I can do something like, grab the scaler and tell him "inverse_transform using the [0] column to work my prediction out."
Is there a way to do this?
Or the only way is to do other Scaler for my Y column and use it?
python scikit-learn keras lstm
edited Nov 12 at 17:21
jkerian
12.9k23551
asked Nov 12 at 15:45
saga56
206
So I have a Scaler:
scaler = MinMaxScaler(feature_range=(0, 1))
that has 9 columns, where column 0 is my Y, and i use my scaler to work all columns.
When I make the predict:
yhat = model.predict(test_X)
I want to use the same scaler so I can transform back my values to normal, but now my output only has 1 column, and my scaler has 9 and this is a problem.
So what I am hopping to find is a way where I can do something like, grab the scaler and tell him "inverse_transform using the [0] column to work my prediction out."
Is there a way to do this?
Or the only way is to do other Scaler for my Y column and use it?
python scikit-learn keras lstm
python scikit-learn keras lstm
edited Nov 12 at 17:21
jkerian
12.9k23551
asked Nov 12 at 15:45
saga56
206
edited Nov 12 at 17:21
jkerian
12.9k23551
asked Nov 12 at 15:45
saga56
206
edited Nov 12 at 17:21
jkerian
12.9k23551
edited Nov 12 at 17:21
jkerian
12.9k23551
edited Nov 12 at 17:21
jkerian
12.9k23551
12.9k23551
asked Nov 12 at 15:45
saga56
206
asked Nov 12 at 15:45
saga56
206
asked Nov 12 at 15:45
saga56
206
206
Use two separate scaler, one for the 8 features ofXand a second one for the outputy.
– elcombato
Nov 12 at 16:32
add a comment |
Use two separate scaler, one for the 8 features ofXand a second one for the outputy.
– elcombato
Nov 12 at 16:32
Use two separate scaler, one for the 8 features of
X and a second one for the output y.– elcombato
Nov 12 at 16:32
Use two separate scaler, one for the 8 features of
X and a second one for the output y.– elcombato
Nov 12 at 16:32
add a comment |
1 Answer
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You can combine the new predicted y value with the x value,get a 9 column matrix and scale it back.But it would be just easier to use two different instances of MinmaxScaler for x and y , so that you can just scale the predicted output back by in-versing the scale for y.
answered Nov 12 at 17:03
kerastf
28610
Hi there! I ended up doing this. I've made two different isntances os MinManScaler for X and Y. Thanks for the tip!
– saga56
Nov 26 at 15:22
add a comment |
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1 Answer
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active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
You can combine the new predicted y value with the x value,get a 9 column matrix and scale it back.But it would be just easier to use two different instances of MinmaxScaler for x and y , so that you can just scale the predicted output back by in-versing the scale for y.
answered Nov 12 at 17:03
kerastf
28610
Hi there! I ended up doing this. I've made two different isntances os MinManScaler for X and Y. Thanks for the tip!
– saga56
Nov 26 at 15:22
add a comment |
You can combine the new predicted y value with the x value,get a 9 column matrix and scale it back.But it would be just easier to use two different instances of MinmaxScaler for x and y , so that you can just scale the predicted output back by in-versing the scale for y.
answered Nov 12 at 17:03
kerastf
28610
Hi there! I ended up doing this. I've made two different isntances os MinManScaler for X and Y. Thanks for the tip!
– saga56
Nov 26 at 15:22
add a comment |
You can combine the new predicted y value with the x value,get a 9 column matrix and scale it back.But it would be just easier to use two different instances of MinmaxScaler for x and y , so that you can just scale the predicted output back by in-versing the scale for y.
answered Nov 12 at 17:03
kerastf
28610
You can combine the new predicted y value with the x value,get a 9 column matrix and scale it back.But it would be just easier to use two different instances of MinmaxScaler for x and y , so that you can just scale the predicted output back by in-versing the scale for y.
answered Nov 12 at 17:03
kerastf
28610
answered Nov 12 at 17:03
kerastf
28610
answered Nov 12 at 17:03
kerastf
28610
answered Nov 12 at 17:03
kerastf
28610
28610
Hi there! I ended up doing this. I've made two different isntances os MinManScaler for X and Y. Thanks for the tip!
– saga56
Nov 26 at 15:22
add a comment |
Hi there! I ended up doing this. I've made two different isntances os MinManScaler for X and Y. Thanks for the tip!
– saga56
Nov 26 at 15:22
Hi there! I ended up doing this. I've made two different isntances os MinManScaler for X and Y. Thanks for the tip!
– saga56
Nov 26 at 15:22
Hi there! I ended up doing this. I've made two different isntances os MinManScaler for X and Y. Thanks for the tip!
– saga56
Nov 26 at 15:22
add a comment |
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Use two separate scaler, one for the 8 features of
Xand a second one for the outputy.– elcombato
Nov 12 at 16:32