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Cat vs Non-cat Classifier - Defining some utility functions - Get Accuracies

Let us now implement the function which calculates the accuracy. This function takes as arguments the predicted labels and the actuals of the corresponding dataset.

We do this in 2 steps:

  • Using np.abs(Y_predicted - Y_actual), we calculate the absolute difference between the actual labels and predicted labels.

  • Then, we use np.mean() and calculate accuracy.


  • np.abs gets the absolute value of each element in the input array.

  • np.mean returns the mean of the elements in the input array.

  • Let us assume y_actual and y_predicted are the actual labels and predicted labels respectively. Copy the following code.

    y_actual = np.array([1,1,1,0,1])
    print("y_actual :", y_actual )
    y_predicted = np.array([1,0,0,0,1])
    print("y_predicted :", y_predicted )
  • Get the absolute differences of the corresponding elements in y_actual and y_predicted using np.abs(), and store them in c.

    c = << your code comes here >>(y_actual - y_predicted)
  • Store the mean of the elements of c in c_mean using np.mean.

    c_mean = << your code goes here >>(c)
  • Accuracy could be calculated as:

    accuracy = 100 - (c_mean * 100)
  • This logic is written in the following get_accuracies function. Copy-paste the following get_accuracies function.

    def get_accuracies(Y_predicted, Y_actual):
        abs_diff = np.abs(Y_predicted - Y_actual)
        accuracy = 100 - np.mean(abs_diff) * 100
        return accuracy

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