CODEWITHSUNDEEP
pythonmachine-learningnaivebayes
user1960836
user1960836

Reputation: 1782

Getting errors when trying to calculate accuracy on GaussianNB

I am trying to get the accuracy of my created model. My code looks like this

from sklearn.datasets import fetch_california_housing
from sklearn.model_selection import train_test_split
import numpy as np

data = fetch_california_housing()
c = np.array([1 if y > np.median(data['target']) else 0 for y in data['target']])
X_train, X_test, c_train, c_test = train_test_split(data['data'], c, random_state=0)

gaussian=GaussianNB().fit(X_train, c_train)
pred=gaussian.predict(X_test)    
metrics.accuracy_score(X_test, pred)

The error is thrown by this line: metrics.accuracy_score(X_test, pred) Saying

ValueError: Classification metrics can't handle a mix of continuous-multioutput and binary targets

I searched for solutions, but unable to find any. Some posts I saw from others that had this problem, say that one can't use metric.accuracy... because this is for classification problems. But mine IS a classification problem.

I also tried another approach: pred=score(X_test, pred) whih gives error:

TypeError: 'numpy.float64' object is not callable

Thanks for any help

---------------------Update-------------

X_test

[[   4.1518       22.            5.66307278 ...    4.18059299
    32.58       -117.05      ]
 [   5.7796       32.            6.10722611 ...    3.02097902
    33.92       -117.97      ]
 [   4.3487       29.            5.93071161 ...    2.91011236
    38.65       -121.84      ]
 ...
 [   3.6296       16.            3.61684211 ...    1.88631579
    34.2        -118.61      ]
 [   5.5133       37.            4.59322034 ...    3.00847458
    33.9        -118.34      ]
 [   4.7639       36.            5.26181818 ...    2.90545455
    37.66       -122.44      ]]

Pred

[1 1 1 ... 1 1 1]

Upvotes: 0

Views: 411

Answers (1)

Mark Moretto
Mark Moretto

Reputation: 2348

Seems to work with c_test:

accuracy_score(c_test, pred) # 0.743798

Another method is:

1 - ((c_test != pred).sum() / X_test.shape[0]) # 0.743798

Upvotes: 1

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