GridSearchCV fitting
I'm having problems to fit my classifier using binarized labels.
clf_linear = GridSearchCV(SVC(kernel='linear', class_weight='balanced'),
param_grid, cv=5)
clf_linear = clf_linear.fit(X_train_pca, y_train)
y_train was binarized by the following method:
y_train = label_binarize(y_train, classes=[1, 2, 3])
I got the following error:
File "C:Pythonlibsite-packagessklearnutilsvalidation.py", line 788, in column_or_1d
raise ValueError("bad input shape {0}".format(shape))
ValueError: bad input shape (545, 3)
The input label shape is (682, 3) not (545, 3).
My professor told me to use binarized labels in gridSearchCV, but reading scikit-learn docs I think I can't do this.
python scikit-learn gridsearchcv
asked Nov 25 '18 at 17:56
Arthur BernardoArthur Bernardo
33
add a comment |
I'm having problems to fit my classifier using binarized labels.
clf_linear = GridSearchCV(SVC(kernel='linear', class_weight='balanced'),
param_grid, cv=5)
clf_linear = clf_linear.fit(X_train_pca, y_train)
y_train was binarized by the following method:
y_train = label_binarize(y_train, classes=[1, 2, 3])
I got the following error:
File "C:Pythonlibsite-packagessklearnutilsvalidation.py", line 788, in column_or_1d
raise ValueError("bad input shape {0}".format(shape))
ValueError: bad input shape (545, 3)
The input label shape is (682, 3) not (545, 3).
My professor told me to use binarized labels in gridSearchCV, but reading scikit-learn docs I think I can't do this.
python scikit-learn gridsearchcv
asked Nov 25 '18 at 17:56
Arthur BernardoArthur Bernardo
33
add a comment |
I'm having problems to fit my classifier using binarized labels.
clf_linear = GridSearchCV(SVC(kernel='linear', class_weight='balanced'),
param_grid, cv=5)
clf_linear = clf_linear.fit(X_train_pca, y_train)
y_train was binarized by the following method:
y_train = label_binarize(y_train, classes=[1, 2, 3])
I got the following error:
File "C:Pythonlibsite-packagessklearnutilsvalidation.py", line 788, in column_or_1d
raise ValueError("bad input shape {0}".format(shape))
ValueError: bad input shape (545, 3)
The input label shape is (682, 3) not (545, 3).
My professor told me to use binarized labels in gridSearchCV, but reading scikit-learn docs I think I can't do this.
python scikit-learn gridsearchcv
asked Nov 25 '18 at 17:56
Arthur BernardoArthur Bernardo
33
I'm having problems to fit my classifier using binarized labels.
clf_linear = GridSearchCV(SVC(kernel='linear', class_weight='balanced'),
param_grid, cv=5)
clf_linear = clf_linear.fit(X_train_pca, y_train)
y_train was binarized by the following method:
y_train = label_binarize(y_train, classes=[1, 2, 3])
I got the following error:
File "C:Pythonlibsite-packagessklearnutilsvalidation.py", line 788, in column_or_1d
raise ValueError("bad input shape {0}".format(shape))
ValueError: bad input shape (545, 3)
The input label shape is (682, 3) not (545, 3).
My professor told me to use binarized labels in gridSearchCV, but reading scikit-learn docs I think I can't do this.
python scikit-learn gridsearchcv
python scikit-learn gridsearchcv
asked Nov 25 '18 at 17:56
Arthur BernardoArthur Bernardo
33
asked Nov 25 '18 at 17:56
Arthur BernardoArthur Bernardo
33
asked Nov 25 '18 at 17:56
Arthur BernardoArthur Bernardo
33
asked Nov 25 '18 at 17:56
Arthur BernardoArthur Bernardo
33
asked Nov 25 '18 at 17:56
Arthur BernardoArthur Bernardo
33
33
add a comment |
add a comment |
1 Answer
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Doesn't matter its 682,3 or 545,3. Why the target has 3 columns? Your y (targets) should be 1-d array for SVC. You dont need to do the label_binarize operation. Keep y_train as it is.
Doing this:
y_train = label_binarize(y_train, classes=[1, 2, 3])
Will convert the y_train to label-indicator matrix. That is used for multi-label classification problems (where the sample can have more than one class at a time). Its not used for multi-class problems.
Keep the y_train as it is to keep it as one-dimensional array and SVC will handle the rest.
answered Nov 26 '18 at 14:11
Vivek KumarVivek Kumar
16.6k42156
Thank you @Vivek, I was trying to do this because my roc curve seems to be strange and talking with my professor he suggested me to do this. The roc curve is not as smooth as he wants it to be.
– Arthur Bernardo
Nov 26 '18 at 14:54
@ArthurBernardo ROC curve is defined for binary tasks. So you will need to convert your multi-class problem into binary class so doinglabel_binarizeis correct here. Internally SVC does the same. But for that, you will need to useOneVsRestClassifierto handle that. Simply using one-hot encoded labels turns on multi-label classification, which is not supported bySVC. Have you seen this:scikit-learn.org/stable/auto_examples/model_selection/… ?
– Vivek Kumar
Nov 26 '18 at 14:57
Yes, I saw this topic. I'm plotting ROC curve like this. The result is this graphic link. Do you see something wrong with these curves? I'm classifying the three faces with more samples in this dataset link. My doubt is if this curves should be more smooth. Thanks again for your help @VivekKumar.
– Arthur Bernardo
Nov 26 '18 at 16:55
add a comment |
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Doesn't matter its 682,3 or 545,3. Why the target has 3 columns? Your y (targets) should be 1-d array for SVC. You dont need to do the label_binarize operation. Keep y_train as it is.
Doing this:
y_train = label_binarize(y_train, classes=[1, 2, 3])
Will convert the y_train to label-indicator matrix. That is used for multi-label classification problems (where the sample can have more than one class at a time). Its not used for multi-class problems.
Keep the y_train as it is to keep it as one-dimensional array and SVC will handle the rest.
answered Nov 26 '18 at 14:11
Vivek KumarVivek Kumar
16.6k42156
Thank you @Vivek, I was trying to do this because my roc curve seems to be strange and talking with my professor he suggested me to do this. The roc curve is not as smooth as he wants it to be.
– Arthur Bernardo
Nov 26 '18 at 14:54
@ArthurBernardo ROC curve is defined for binary tasks. So you will need to convert your multi-class problem into binary class so doinglabel_binarizeis correct here. Internally SVC does the same. But for that, you will need to useOneVsRestClassifierto handle that. Simply using one-hot encoded labels turns on multi-label classification, which is not supported bySVC. Have you seen this:scikit-learn.org/stable/auto_examples/model_selection/… ?
– Vivek Kumar
Nov 26 '18 at 14:57
Yes, I saw this topic. I'm plotting ROC curve like this. The result is this graphic link. Do you see something wrong with these curves? I'm classifying the three faces with more samples in this dataset link. My doubt is if this curves should be more smooth. Thanks again for your help @VivekKumar.
– Arthur Bernardo
Nov 26 '18 at 16:55
add a comment |
Doesn't matter its 682,3 or 545,3. Why the target has 3 columns? Your y (targets) should be 1-d array for SVC. You dont need to do the label_binarize operation. Keep y_train as it is.
Doing this:
y_train = label_binarize(y_train, classes=[1, 2, 3])
Will convert the y_train to label-indicator matrix. That is used for multi-label classification problems (where the sample can have more than one class at a time). Its not used for multi-class problems.
Keep the y_train as it is to keep it as one-dimensional array and SVC will handle the rest.
answered Nov 26 '18 at 14:11
Vivek KumarVivek Kumar
16.6k42156
Thank you @Vivek, I was trying to do this because my roc curve seems to be strange and talking with my professor he suggested me to do this. The roc curve is not as smooth as he wants it to be.
– Arthur Bernardo
Nov 26 '18 at 14:54
@ArthurBernardo ROC curve is defined for binary tasks. So you will need to convert your multi-class problem into binary class so doinglabel_binarizeis correct here. Internally SVC does the same. But for that, you will need to useOneVsRestClassifierto handle that. Simply using one-hot encoded labels turns on multi-label classification, which is not supported bySVC. Have you seen this:scikit-learn.org/stable/auto_examples/model_selection/… ?
– Vivek Kumar
Nov 26 '18 at 14:57
Yes, I saw this topic. I'm plotting ROC curve like this. The result is this graphic link. Do you see something wrong with these curves? I'm classifying the three faces with more samples in this dataset link. My doubt is if this curves should be more smooth. Thanks again for your help @VivekKumar.
– Arthur Bernardo
Nov 26 '18 at 16:55
add a comment |
Doesn't matter its 682,3 or 545,3. Why the target has 3 columns? Your y (targets) should be 1-d array for SVC. You dont need to do the label_binarize operation. Keep y_train as it is.
Doing this:
y_train = label_binarize(y_train, classes=[1, 2, 3])
Will convert the y_train to label-indicator matrix. That is used for multi-label classification problems (where the sample can have more than one class at a time). Its not used for multi-class problems.
Keep the y_train as it is to keep it as one-dimensional array and SVC will handle the rest.
answered Nov 26 '18 at 14:11
Vivek KumarVivek Kumar
16.6k42156
Doesn't matter its 682,3 or 545,3. Why the target has 3 columns? Your y (targets) should be 1-d array for SVC. You dont need to do the label_binarize operation. Keep y_train as it is.
Doing this:
y_train = label_binarize(y_train, classes=[1, 2, 3])
Will convert the y_train to label-indicator matrix. That is used for multi-label classification problems (where the sample can have more than one class at a time). Its not used for multi-class problems.
Keep the y_train as it is to keep it as one-dimensional array and SVC will handle the rest.
answered Nov 26 '18 at 14:11
Vivek KumarVivek Kumar
16.6k42156
answered Nov 26 '18 at 14:11
Vivek KumarVivek Kumar
16.6k42156
answered Nov 26 '18 at 14:11
Vivek KumarVivek Kumar
16.6k42156
answered Nov 26 '18 at 14:11
Vivek KumarVivek Kumar
16.6k42156
16.6k42156
Thank you @Vivek, I was trying to do this because my roc curve seems to be strange and talking with my professor he suggested me to do this. The roc curve is not as smooth as he wants it to be.
– Arthur Bernardo
Nov 26 '18 at 14:54
@ArthurBernardo ROC curve is defined for binary tasks. So you will need to convert your multi-class problem into binary class so doinglabel_binarizeis correct here. Internally SVC does the same. But for that, you will need to useOneVsRestClassifierto handle that. Simply using one-hot encoded labels turns on multi-label classification, which is not supported bySVC. Have you seen this:scikit-learn.org/stable/auto_examples/model_selection/… ?
– Vivek Kumar
Nov 26 '18 at 14:57
Yes, I saw this topic. I'm plotting ROC curve like this. The result is this graphic link. Do you see something wrong with these curves? I'm classifying the three faces with more samples in this dataset link. My doubt is if this curves should be more smooth. Thanks again for your help @VivekKumar.
– Arthur Bernardo
Nov 26 '18 at 16:55
add a comment |
Thank you @Vivek, I was trying to do this because my roc curve seems to be strange and talking with my professor he suggested me to do this. The roc curve is not as smooth as he wants it to be.
– Arthur Bernardo
Nov 26 '18 at 14:54
@ArthurBernardo ROC curve is defined for binary tasks. So you will need to convert your multi-class problem into binary class so doinglabel_binarizeis correct here. Internally SVC does the same. But for that, you will need to useOneVsRestClassifierto handle that. Simply using one-hot encoded labels turns on multi-label classification, which is not supported bySVC. Have you seen this:scikit-learn.org/stable/auto_examples/model_selection/… ?
– Vivek Kumar
Nov 26 '18 at 14:57
Yes, I saw this topic. I'm plotting ROC curve like this. The result is this graphic link. Do you see something wrong with these curves? I'm classifying the three faces with more samples in this dataset link. My doubt is if this curves should be more smooth. Thanks again for your help @VivekKumar.
– Arthur Bernardo
Nov 26 '18 at 16:55
Thank you @Vivek, I was trying to do this because my roc curve seems to be strange and talking with my professor he suggested me to do this. The roc curve is not as smooth as he wants it to be.
– Arthur Bernardo
Nov 26 '18 at 14:54
Thank you @Vivek, I was trying to do this because my roc curve seems to be strange and talking with my professor he suggested me to do this. The roc curve is not as smooth as he wants it to be.
– Arthur Bernardo
Nov 26 '18 at 14:54
@ArthurBernardo ROC curve is defined for binary tasks. So you will need to convert your multi-class problem into binary class so doing
label_binarize is correct here. Internally SVC does the same. But for that, you will need to use OneVsRestClassifier to handle that. Simply using one-hot encoded labels turns on multi-label classification, which is not supported by SVC. Have you seen this:scikit-learn.org/stable/auto_examples/model_selection/… ?– Vivek Kumar
Nov 26 '18 at 14:57
@ArthurBernardo ROC curve is defined for binary tasks. So you will need to convert your multi-class problem into binary class so doing
label_binarize is correct here. Internally SVC does the same. But for that, you will need to use OneVsRestClassifier to handle that. Simply using one-hot encoded labels turns on multi-label classification, which is not supported by SVC. Have you seen this:scikit-learn.org/stable/auto_examples/model_selection/… ?– Vivek Kumar
Nov 26 '18 at 14:57
Yes, I saw this topic. I'm plotting ROC curve like this. The result is this graphic link. Do you see something wrong with these curves? I'm classifying the three faces with more samples in this dataset link. My doubt is if this curves should be more smooth. Thanks again for your help @VivekKumar.
– Arthur Bernardo
Nov 26 '18 at 16:55
Yes, I saw this topic. I'm plotting ROC curve like this. The result is this graphic link. Do you see something wrong with these curves? I'm classifying the three faces with more samples in this dataset link. My doubt is if this curves should be more smooth. Thanks again for your help @VivekKumar.
– Arthur Bernardo
Nov 26 '18 at 16:55
add a comment |
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