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

The sklearn.metrics.accuracy_score(y_true, y_pred) method defines y_pred as:

y_pred : 1d array-like, or label indicator array / sparse matrix. Predicted labels, as returned by a classifier.

Which means y_pred has to be an array of 1's or 0's (predicated labels). They should not be probabilities.

The predicated labels (1's and 0's) and/or predicted probabilites can be generated using the LinearRegression() model's methods predict() and predict_proba() respectively.

1. Generate predicted labels:

LR = linear_model.LinearRegression()
y_preds=LR.predict(X_test)
print(y_preds)

output:

[1 1 0 1]

y_preds can now be used for the accuracy_score() method: accuracy_score(y_true, y_pred)

2. Generate probabilities for labels:

Some metrics such as 'precision_recall_curve(y_true, probas_pred)' require probabilities, which can be generated as follows:

LR = linear_model.LinearRegression()
y_preds=LR.predict_proba(X_test)
print(y_preds)

output:

[0.87812372 0.77490434 0.30319547 0.84999743]

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