Accuracy Score ValueError: Can't Handle mix of binary and continuous target

Question

I'm using linear_model.LinearRegression from scikit-learn as a predictive model. It works and it's perfect. I have a problem to evaluate the predicted results using the accuracy_score metric. This is my true Data :

array([1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0])

My predictive Data:

array([ 0.07094605,  0.1994941 , 0.19270157,  0.13379635, 0.04654469,

    0.09212494,  0.19952108, 0.12884365,  0.15685076, -0.01274453,

    0.32167554,  0.32167554, -0.10023553,  0.09819648, -0.06755516,

    0.25390082,  0.17248324])

My code:

accuracy_score(y_true, y_pred, normalize=False)

Error message:

ValueError: Can't handle mix of binary and continuous target

Help ? Thank you.

Answer 1

Linear regression is a very poor classifier because it can’t classify two classes clearly. So It is not recommended to use for classification problems.

You can fix this error by using the following code:

accuracy_score(y_true, y_pred.round(), normalize=False)

If you wish to learn more about Machine Learning, then check out this Machine Learning Tutorial for more insights.

Hope this answer helps.