Multi class classification cross_val_score for precision and recall giving me same result
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In my Y values , I have 4 classes. So , clearly this is a multiclass classification problem .
I am using MultinomialNB as a model. And I am doing 10-fold cross validation , but , I am getting same value for Precision , Recall and F1 .
Here is my code :
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn.naive_bayes import MultinomialNB
from sklearn.multiclass import OneVsRestClassifier
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold
from sklearn.preprocessing import LabelEncoder
scaler = MinMaxScaler()
X_train, X_test, y_train, y_test = train_test_split(yelp_joined_data, Y_label_encoded)
X_train=scaler.fit_transform(X_train)
X_test = scaler.fit_transform(X_test)
# fix random seed
seed = 7
numpy.random.seed(seed)
kfold = KFold(n_splits=10, shuffle=True, random_state=seed)
clf = OneVsRestClassifier(MultinomialNB(alpha=0.01))
prec_res_test=cross_val_score(clf, X_train, y_train.values.ravel(), cv=kfold, n_jobs=1,scoring='precision_micro')
prec_res_test.mean() ## value coming as 0.5090949570838452
recall_res_test=cross_val_score(clf, X_train, y_train.values.ravel(), cv=kfold, n_jobs=1,scoring='recall_micro')
recall_res_test.mean() # value coming as 0.5090949570838452
rf1_res_test=cross_val_score(clf, X_train, y_train.values.ravel(), cv=kfold, n_jobs=1,scoring='f1_micro')
rf1_res_test.mean() # value coming as 0.5090949570838452
I can not use usual precision here as scoring parameter , since this is a multiclass problem.
Can anyone please help me where I am doing wrong ?
python machine-learning scikit-learn neural-network multiclass-classification
asked Nov 9 at 16:02
DukeLover
111214
add a comment |
up vote
0
down vote
favorite
In my Y values , I have 4 classes. So , clearly this is a multiclass classification problem .
I am using MultinomialNB as a model. And I am doing 10-fold cross validation , but , I am getting same value for Precision , Recall and F1 .
Here is my code :
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn.naive_bayes import MultinomialNB
from sklearn.multiclass import OneVsRestClassifier
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold
from sklearn.preprocessing import LabelEncoder
scaler = MinMaxScaler()
X_train, X_test, y_train, y_test = train_test_split(yelp_joined_data, Y_label_encoded)
X_train=scaler.fit_transform(X_train)
X_test = scaler.fit_transform(X_test)
# fix random seed
seed = 7
numpy.random.seed(seed)
kfold = KFold(n_splits=10, shuffle=True, random_state=seed)
clf = OneVsRestClassifier(MultinomialNB(alpha=0.01))
prec_res_test=cross_val_score(clf, X_train, y_train.values.ravel(), cv=kfold, n_jobs=1,scoring='precision_micro')
prec_res_test.mean() ## value coming as 0.5090949570838452
recall_res_test=cross_val_score(clf, X_train, y_train.values.ravel(), cv=kfold, n_jobs=1,scoring='recall_micro')
recall_res_test.mean() # value coming as 0.5090949570838452
rf1_res_test=cross_val_score(clf, X_train, y_train.values.ravel(), cv=kfold, n_jobs=1,scoring='f1_micro')
rf1_res_test.mean() # value coming as 0.5090949570838452
I can not use usual precision here as scoring parameter , since this is a multiclass problem.
Can anyone please help me where I am doing wrong ?
python machine-learning scikit-learn neural-network multiclass-classification
asked Nov 9 at 16:02
DukeLover
111214
add a comment |
up vote
0
down vote
favorite
up vote
0
down vote
favorite
In my Y values , I have 4 classes. So , clearly this is a multiclass classification problem .
I am using MultinomialNB as a model. And I am doing 10-fold cross validation , but , I am getting same value for Precision , Recall and F1 .
Here is my code :
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn.naive_bayes import MultinomialNB
from sklearn.multiclass import OneVsRestClassifier
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold
from sklearn.preprocessing import LabelEncoder
scaler = MinMaxScaler()
X_train, X_test, y_train, y_test = train_test_split(yelp_joined_data, Y_label_encoded)
X_train=scaler.fit_transform(X_train)
X_test = scaler.fit_transform(X_test)
# fix random seed
seed = 7
numpy.random.seed(seed)
kfold = KFold(n_splits=10, shuffle=True, random_state=seed)
clf = OneVsRestClassifier(MultinomialNB(alpha=0.01))
prec_res_test=cross_val_score(clf, X_train, y_train.values.ravel(), cv=kfold, n_jobs=1,scoring='precision_micro')
prec_res_test.mean() ## value coming as 0.5090949570838452
recall_res_test=cross_val_score(clf, X_train, y_train.values.ravel(), cv=kfold, n_jobs=1,scoring='recall_micro')
recall_res_test.mean() # value coming as 0.5090949570838452
rf1_res_test=cross_val_score(clf, X_train, y_train.values.ravel(), cv=kfold, n_jobs=1,scoring='f1_micro')
rf1_res_test.mean() # value coming as 0.5090949570838452
I can not use usual precision here as scoring parameter , since this is a multiclass problem.
Can anyone please help me where I am doing wrong ?
python machine-learning scikit-learn neural-network multiclass-classification
asked Nov 9 at 16:02
DukeLover
111214
In my Y values , I have 4 classes. So , clearly this is a multiclass classification problem .
I am using MultinomialNB as a model. And I am doing 10-fold cross validation , but , I am getting same value for Precision , Recall and F1 .
Here is my code :
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn.naive_bayes import MultinomialNB
from sklearn.multiclass import OneVsRestClassifier
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold
from sklearn.preprocessing import LabelEncoder
scaler = MinMaxScaler()
X_train, X_test, y_train, y_test = train_test_split(yelp_joined_data, Y_label_encoded)
X_train=scaler.fit_transform(X_train)
X_test = scaler.fit_transform(X_test)
# fix random seed
seed = 7
numpy.random.seed(seed)
kfold = KFold(n_splits=10, shuffle=True, random_state=seed)
clf = OneVsRestClassifier(MultinomialNB(alpha=0.01))
prec_res_test=cross_val_score(clf, X_train, y_train.values.ravel(), cv=kfold, n_jobs=1,scoring='precision_micro')
prec_res_test.mean() ## value coming as 0.5090949570838452
recall_res_test=cross_val_score(clf, X_train, y_train.values.ravel(), cv=kfold, n_jobs=1,scoring='recall_micro')
recall_res_test.mean() # value coming as 0.5090949570838452
rf1_res_test=cross_val_score(clf, X_train, y_train.values.ravel(), cv=kfold, n_jobs=1,scoring='f1_micro')
rf1_res_test.mean() # value coming as 0.5090949570838452
I can not use usual precision here as scoring parameter , since this is a multiclass problem.
Can anyone please help me where I am doing wrong ?
python machine-learning scikit-learn neural-network multiclass-classification
python machine-learning scikit-learn neural-network multiclass-classification
asked Nov 9 at 16:02
DukeLover
111214
asked Nov 9 at 16:02
DukeLover
111214
asked Nov 9 at 16:02
DukeLover
111214
asked Nov 9 at 16:02
DukeLover
111214
asked Nov 9 at 16:02
DukeLover
111214
111214
add a comment |
add a comment |
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