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.
Note:
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
Note - Having trouble with the assessment engine? Follow the steps listed here
Please login to comment
Be the first one to comment!