AI Performance Metrics

What is accuracy in the context of AI performance metrics?

• Measure of the model's ability to correctly predict the negative class

• Measure of the model's ability to correctly predict only the positive class

• Measure of the model's ability to correctly predict the neutral class

• Measure of the model's ability to correctly predict both the positive and negative classes

Explain the concept of precision and recall in AI evaluation.

• Precision measures the accuracy of the positive predictions, while recall measures the accuracy of the negative predictions.

• Precision measures the accuracy of the negative predictions, while recall measures the ability of the model to find all the negative instances.

• Precision measures the accuracy of the positive predictions, while recall measures the ability of the model to find all the positive instances.

• Precision measures the ability of the model to find all the positive instances, while recall measures the accuracy of the positive predictions.

How is a confusion matrix used in AI evaluation?

• It is used to measure the speed of data processing in AI systems.

• It is used to calculate the average accuracy of a regression model.

• It is used to visualize the performance of a classification model by comparing the actual and predicted values.

• It is used to determine the amount of memory required for training a neural network.

What is the significance of ROC curve analysis in AI?

• It helps in determining the accuracy of a regression model

• It is used to measure the speed of data processing in AI

• It helps in evaluating the performance of a classification model.

• It is only applicable to unsupervised learning models

Discuss the importance of bias and fairness in AI metrics.

• Bias and fairness are important to ensure that AI metrics do not perpetuate discrimination or inequality.

• Bias and fairness are only important in human decision-making, not in AI metrics

• AI metrics should prioritize discrimination and inequality

• Bias and fairness have no impact on AI metrics

What are the limitations of using accuracy as the sole metric for evaluating AI models?

• Accuracy is always the most important metric for AI models

• Accuracy alone does not provide a complete picture of model performance.

• Using accuracy alone can lead to overfitting

• There are no limitations to using accuracy as the sole metric

How can precision and recall be used together to evaluate an AI model?

• By measuring the model's speed and accuracy

• By determining the model's cost-effectiveness

• By providing a more comprehensive understanding of the model's performance

• By evaluating the model's user interface

Explain the components of a confusion matrix and their relevance in AI evaluation.

• Accuracy, precision, recall, F1 score

• Sensitivity, specificity, precision, accuracy

• True positive, false positive, true negative, and false negative

• Positive true, negative true, positive false, negative false

What are the key factors to consider when analyzing an ROC curve in AI?

• Confusion matrix, bias-variance tradeoff, feature importance

• Mean squared error, R-squared, F1 score

• AUC, trade-off between true positive rate and false positive rate, threshold selection

• Accuracy, precision, recall

Why is it important to address bias and fairness in AI metrics, and how can it be achieved?

• To ensure that AI systems do not perpetuate discrimination or inequality. This can be achieved by using diverse and representative datasets, regularly auditing the AI models for bias, and involving diverse perspectives in the development process.

• By ignoring bias and fairness, AI systems can achieve better accuracy

• Achieving bias and fairness in AI metrics is impossible

• It is not important to address bias and fairness in AI metrics