Does the number of classifiers on stacking classifier have to be equal to the number of columns of my...
$begingroup$
I'm trying to compete in Kaggle's give some credit contest for classification, the training data set contains 11 features and after my feature engineering I ended having 11 features. I want to use a stacking classifier approach with
mlxtend.classifier.StackingClassifier by using 4 different classifiers, but when trying to predict the test datata set I got the error: ValueError: query data dimension must match training data dimension
Below you will find my code.
Could you please help me to understand the correct usage of a stacking classifiers?
%%time
models=[KNeighborsClassifier(weights='distance'),
GaussianNB(),SGDClassifier(loss='hinge'),XGBClassifier()]
calibrated_models=Calibrated_classifier(models,return_names=False)
meta=LogisticRegression()
stacker=StackingCVClassifier(classifiers=calibrated_models,meta_classifier=meta,use_probas=True).fit(X.values,y.values)
Remark: In my code I just programmed a function to return a list with calibrated classifiers StackingCVClassifier I have checked this is not causing the error
Remark 2: I had already tried to perform a stacker from scratch with the same results so I had thought It was something wrong with my own stacker
from sklearn.linear_model import LogisticRegression
def StackingClassifier(X,y,models,stacker=LogisticRegression(),return_data=True):
names,ls=,
predictions=pd.DataFrame()
for model in models:
names.append(str(model)[:str(model).find('(')])
for i,model in enumerate(models):
model.fit(X,y)
ls=model.predict_proba(X)[:,1]
predictions[names[i]]=ls
if return_data:
return predictions
else:
return stacker.fit(predictions,y)
machine-learning python scikit-learn ensemble-modeling kaggle
asked 1 min ago
MorenoMoreno
1114
$endgroup$
add a comment |
$begingroup$
I'm trying to compete in Kaggle's give some credit contest for classification, the training data set contains 11 features and after my feature engineering I ended having 11 features. I want to use a stacking classifier approach with
mlxtend.classifier.StackingClassifier by using 4 different classifiers, but when trying to predict the test datata set I got the error: ValueError: query data dimension must match training data dimension
Below you will find my code.
Could you please help me to understand the correct usage of a stacking classifiers?
%%time
models=[KNeighborsClassifier(weights='distance'),
GaussianNB(),SGDClassifier(loss='hinge'),XGBClassifier()]
calibrated_models=Calibrated_classifier(models,return_names=False)
meta=LogisticRegression()
stacker=StackingCVClassifier(classifiers=calibrated_models,meta_classifier=meta,use_probas=True).fit(X.values,y.values)
Remark: In my code I just programmed a function to return a list with calibrated classifiers StackingCVClassifier I have checked this is not causing the error
Remark 2: I had already tried to perform a stacker from scratch with the same results so I had thought It was something wrong with my own stacker
from sklearn.linear_model import LogisticRegression
def StackingClassifier(X,y,models,stacker=LogisticRegression(),return_data=True):
names,ls=,
predictions=pd.DataFrame()
for model in models:
names.append(str(model)[:str(model).find('(')])
for i,model in enumerate(models):
model.fit(X,y)
ls=model.predict_proba(X)[:,1]
predictions[names[i]]=ls
if return_data:
return predictions
else:
return stacker.fit(predictions,y)
machine-learning python scikit-learn ensemble-modeling kaggle
asked 1 min ago
MorenoMoreno
1114
$endgroup$
add a comment |
$begingroup$
I'm trying to compete in Kaggle's give some credit contest for classification, the training data set contains 11 features and after my feature engineering I ended having 11 features. I want to use a stacking classifier approach with
mlxtend.classifier.StackingClassifier by using 4 different classifiers, but when trying to predict the test datata set I got the error: ValueError: query data dimension must match training data dimension
Below you will find my code.
Could you please help me to understand the correct usage of a stacking classifiers?
%%time
models=[KNeighborsClassifier(weights='distance'),
GaussianNB(),SGDClassifier(loss='hinge'),XGBClassifier()]
calibrated_models=Calibrated_classifier(models,return_names=False)
meta=LogisticRegression()
stacker=StackingCVClassifier(classifiers=calibrated_models,meta_classifier=meta,use_probas=True).fit(X.values,y.values)
Remark: In my code I just programmed a function to return a list with calibrated classifiers StackingCVClassifier I have checked this is not causing the error
Remark 2: I had already tried to perform a stacker from scratch with the same results so I had thought It was something wrong with my own stacker
from sklearn.linear_model import LogisticRegression
def StackingClassifier(X,y,models,stacker=LogisticRegression(),return_data=True):
names,ls=,
predictions=pd.DataFrame()
for model in models:
names.append(str(model)[:str(model).find('(')])
for i,model in enumerate(models):
model.fit(X,y)
ls=model.predict_proba(X)[:,1]
predictions[names[i]]=ls
if return_data:
return predictions
else:
return stacker.fit(predictions,y)
machine-learning python scikit-learn ensemble-modeling kaggle
asked 1 min ago
MorenoMoreno
1114
$endgroup$
I'm trying to compete in Kaggle's give some credit contest for classification, the training data set contains 11 features and after my feature engineering I ended having 11 features. I want to use a stacking classifier approach with
mlxtend.classifier.StackingClassifier by using 4 different classifiers, but when trying to predict the test datata set I got the error: ValueError: query data dimension must match training data dimension
Below you will find my code.
Could you please help me to understand the correct usage of a stacking classifiers?
%%time
models=[KNeighborsClassifier(weights='distance'),
GaussianNB(),SGDClassifier(loss='hinge'),XGBClassifier()]
calibrated_models=Calibrated_classifier(models,return_names=False)
meta=LogisticRegression()
stacker=StackingCVClassifier(classifiers=calibrated_models,meta_classifier=meta,use_probas=True).fit(X.values,y.values)
Remark: In my code I just programmed a function to return a list with calibrated classifiers StackingCVClassifier I have checked this is not causing the error
Remark 2: I had already tried to perform a stacker from scratch with the same results so I had thought It was something wrong with my own stacker
from sklearn.linear_model import LogisticRegression
def StackingClassifier(X,y,models,stacker=LogisticRegression(),return_data=True):
names,ls=,
predictions=pd.DataFrame()
for model in models:
names.append(str(model)[:str(model).find('(')])
for i,model in enumerate(models):
model.fit(X,y)
ls=model.predict_proba(X)[:,1]
predictions[names[i]]=ls
if return_data:
return predictions
else:
return stacker.fit(predictions,y)
machine-learning python scikit-learn ensemble-modeling kaggle
machine-learning python scikit-learn ensemble-modeling kaggle
asked 1 min ago
MorenoMoreno
1114
asked 1 min ago
MorenoMoreno
1114
asked 1 min ago
MorenoMoreno
1114
asked 1 min ago
MorenoMoreno
1114
asked 1 min ago
MorenoMoreno
1114
1114
add a comment |
add a comment |
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