How to calculate error rate from confusion matrix in python?

To calculate the error rate from a confusion matrix in Python, you can use the following steps:

STEP 1: Calculate the number of true positive predictions, true negative predictions, false positive predictions, and false negative predictions using the values in the confusion matrix.

STEP 2: Calculate the total number of predictions by adding the true positive, true negative, false positive, and false negative predictions.

STEP 3: Calculate the error rate by dividing the sum of the false positive and false negative predictions by the total number of predictions and multiplying by 100 to get the error rate in percent.

Here is an example of how to do this in Python:
# Confusion matrix
# | TP | FP |
# | FN | TN |

# Calculate the number of true positive predictions
true_positives = 30

# Calculate the number of true negative predictions
true_negatives = 40

# Calculate the number of false positive predictions
false_positives = 10

# Calculate the number of false negative predictions
false_negatives = 20

# Calculate the total number of predictions
total_predictions = true_positives + true_negatives + false_positives + false_negatives

# Calculate the error rate
error_rate = (false_positives + false_negatives) / total_predictions * 100

# Print the error rate
print("Error rate: {:.2f}%".format(error_rate))
Output:
Error rate: 30.00%
In this example, the error rate is calculated to be 30.00%. You can use this method to calculate the error rate from any confusion matrix.

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