Question

Explain at least two scenarios where you would set the cutoff value very low (e.g. under 0.1) in a confusion matrix. (approx.. 200 words). Please provide two different scenarios.

Answer 1

Confusion Matrix :

The Confusion Matrix the type of summary of the prediction of the results that we are going to execute on some of the classification of the problems.

And the confusion matrix can be stated and be calculated by the number of correct and incorrect predictions that are to be summarized with the count and some value and that are broken by the each class.

And this type of format is called as the confusion Matrix.

Calculation Process :

1 .Firstly , We need to set the data set with the expected output values.

2 .Make the prediction for each and every value that you are going to write.

3 .From the gives values the expected outcomes will be like

• The number of the correct value prediction for each class.
• we need to write the no.of incorrect prediction for the other class

4 .Here are the example that is Going to solve the Confusion Matrix :

Scenario 1:

We are taking the list of 10 records with their expected outcomes.

Expected	Predicted
Roja	Rani
Roja	Roja
Rani	Rani
Roja	Roja
Rani	Roja
Rani	Rani
Rani	Rani
Roja	Roja
Roja	Rani
Rani	Rani

Correct Predictions:

Roja classified as Rani = 3

Rani classified as Rani = 4

Incorrect Predictions:

Roja classified as Rani = 2

Rani classified as Rani = 1

2-class confusion matrix:

Scenario 2:

If a classification system has been trained to distinguish between cats, dogs and rabbits, a confusion matrix will summarize the results of testing the algorithm for further inspection. Assuming a sample of 27 animals - 8 cats, 6 dogs, and 13 rabbits, the resulting confusion matrix could look like the table below:

In this confusion matrix, of the 8 actual cats, the system predicted that three were dogs, and of the six dogs, it predicted that one was a rabbit and two were cats. We can see from the matrix that the system in question has trouble distinguishing between cats and dogs, but can make the distinction between rabbits and other types of animals pretty well. All correct predictions are located in the diagonal of the table (highlighted in bold), so it is easy to visually inspect the table for prediction errors, as they will be represented by values outside the diagonal.