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

Here is an example of how to do this in Python:

**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 matrixOutput:

# | 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))

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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