CODEWITHSUNDEEP
pythonscikit-learn
praveen kumar yethirajula
praveen kumar yethirajula

Reputation: 179

Showing Value Error while logistic regression fit

from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.3,random_state=0)

log=LogisticRegression()

print (x_train.shape)                --(5, 13)
print (x_test.shape)                 --(3, 13)
print(y_train.shape)                 --(5,)
print(y_test.shape)                  --(3,)

log.fit(x_train,y_train)

please see the below I have followed from youtube and internet sources for the code and with the above code it is giving following error .Please help me out error :

ValueError                                Traceback (most recent call last)
<ipython-input-16-86c1075a1e93> in <module>
 ----> 1 log.fit(x_train,y_train)

  /srv/conda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py in fit(self, X, y,     sample_weight)
1287         X, y = check_X_y(X, y, accept_sparse='csr', dtype=_dtype, order="C",
1288                          accept_large_sparse=solver != 'liblinear')
-> 1289         check_classification_targets(y)
1290         self.classes_ = np.unique(y)
1291         n_samples, n_features = X.shape

   /srv/conda/lib/python3.6/site-packages/sklearn/utils/multiclass.py in check_classification_targets(y)
  169     if y_type not in ['binary', 'multiclass', 'multiclass-multioutput',
  170                       'multilabel-indicator', 'multilabel-sequences']:
  --> 171         raise ValueError("Unknown label type: %r" % y_type)
  172 
  173 

ValueError: Unknown label type: 'continuous'

Upvotes: 0

Views: 1425

Answers (1)

Amine Benatmane
Amine Benatmane

Reputation: 1261

Logistic regression is a statistical method for predicting binary classes. The dependent variable or target variable must be binary. In your case, you have "continuous" targets.

Types of Logistic Regression:

  • Binary Logistic Regression: The target variable has only two possible outcomes.

  • Multinomial Logistic Regression: The target variable has three or more nominal categories

  • Ordinal Logistic Regression: the target variable has three or more ordinal categories (Example: product rating from 1 to 5)

Upvotes: 2

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