Model Evaluation - Accuracy

• Definition

• Calculation

• How to code

• Related Courses

Accuracy is a classification metric used to measure your model’s performance. Accuracy is calculated as the number of predictions the model got correct, over the sum of all the predictions. 

A high accuracy model means it’s predictions are right most of the time, while low accuracy means it’s predictions are off. As a metric, you’ll want to have a high accuracy model, but beware of 

overfitting

.

The number of predictions a model gets correct is the sum of predictions that came true. 

This is represented by quadrants 1 and 4 of a 

, 515 and 8 respectively.

Next, the sum of all predictions is simply the sum of all quadrants.

Following the calculations we have (515 + 8) / (515 +5 + 32 +8), which is 523 / 560. This means our total accuracy of this model is 93.33%.

Accuracy is a metric that can be calculated by scratch or using a library called SKLearn. 

First, to calculate the total number of predictions, we take the length of y_pred, our predictions. 

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total = len(y_pred)

Then to verify if the values are matching, we go through each value and check if they match.

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correct = 0
for i in range(0, total):
  # Prediction matches
  if y_pred[i] === y_true[i]:
     correct += 1

Finally, we can calculate the accuracy as the number of matches, divided by the total.

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print("Correct:", correct)
print("Total:", total)
print("Accuracy:", correct / total)

SKLearn or SciKitLearn, is a Python library that handles calculating the accuracy of a model using the method 

. First, you supply it with two things, the set of predicted values and the set of actual values. Then, from there it will find the number of matches and it’s total size.

As an additional function of the 

accuracy_score

method, you may set the normalize flag to 

False

to get the count of correct instead.

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accuracy_score(y_true, y_pred, normalize=False)

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