What is the accuracy_score function in Sklearn?
Overview
In Python, the accuracy_score function of the sklearn.metrics package calculates the accuracy score for a set of predicted labels against the true labels.
To use the accuracy_score function, we’ll import it into our program, as shown below:
from sklearn.metrics import accuracy_score
Syntax
The syntax of the accuracy_score function is as follows:
sklearn.metrics.accuracy_score(y_true, y_pred, *, normalize=True, sample_weight=None)
Parameters
The accuracy_score function accepts the following parameters:
y_true: These are the true labels.y_pred: These are the predicted labels.normalize: If this value isTrue, then the fraction of correct predictions is returned; otherwise, the number of correct predictions is returned. By default,normalizeisTrue.sample_weight: These are any sample weights to be used in calculating the accuracy.
Return value
This function returns either the fraction of the correct predictions or the number of correct predictions, depending on the value of the normalize parameter.
Example
The code below shows how to use the accuracy_score function in Python.
from sklearn.metrics import accuracy_score# define true labelstrue_labels = ["a", "c", "b", "a"]# define corresponding predicted labelspred_labels = ["c", "c", "b", "a"]# find accuracy scoresaccuracy = accuracy_score(true_labels, pred_labels)print("The accuracy of prediction is: ", accuracy)# find number of accurate predictionsaccurate_predictions = accuracy_score(true_labels, pred_labels, normalize=False)print("The number of accurate predictions is: ", accurate_predictions)
Explanation
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Line 1: We import the
accuracy_scorefunction from thesklearn.metricslibrary. -
Lines 4-7: We define the true labels and predicted labels.
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Line 10: We use the
accuracy_scorefunction to find the fraction of correctly classified labels. Astrue_labelsandpred_labelshave only 1 value that does not match and 3 values that match, theaccuracy_scorefunction returns0.75. -
Line 14: We use the
accuracy_scorefunction withnormalizeset toFalse, so it returns the number of correctly classified labels,3.
