 # 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.

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.

INSTRUCTIONS
• 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
``````

No hints are availble for this assesment

Answer is not availble for this assesment

Note - Having trouble with the assessment engine? Follow the steps listed here