Accuracy Score ValueError: Can't Handle mix of binary and continuous target
<p>I'm using <code>linear_model.LinearRegression</code> from scikit-learn as a predictive model. It works and it's perfect. I have a problem to evaluate the predicted results using the <code>accuracy_score</code> metric.</p>
<p>This is my true Data :</p>
<pre><code>array([1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0])
</code></pre>
<p><strong>My predicted Data:</strong></p>
<pre><code>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])
</code></pre>
<p><strong>My code:</strong></p>
<pre><code>accuracy_score(y_true, y_pred, normalize=False)
</code></pre>
<p><strong>Error message:</strong></p>
<pre><code>ValueError: Can't handle mix of binary and continuous target
</code></pre>