Class 10 Evaluation

What is a confusion matrix and how is it used in evaluation?

• It is used to evaluate the performance of a time series model.

• It is used to evaluate the performance of a clustering model.

• It is used to evaluate the performance of a classification model.

• It is used to evaluate the performance of a regression model.

Define accuracy and explain its significance in evaluation.

• Accuracy is the measure of how far a result is from the true value. It is not significant in evaluation.

• Accuracy is the measure of how close a result comes to the true value or the correct answer. It is significant in evaluation as it helps to determine the reliability and correctness of the results or data.

• Accuracy is the measure of how close a result comes to the true value. It is significant in evaluation as it helps to determine the speed of the results or data.

• Accuracy is the measure of how close a result comes to the true value. It is significant in evaluation as it helps to determine the size of the results or data.

What is precision and how is it calculated in the context of evaluation?

• Precision is the measure of the accuracy of the negative predictions made by a model. It is calculated by dividing the number of true negative predictions by the sum of true negative and false negative predictions.

• Precision is the measure of the accuracy of the positive predictions made by a model. It is calculated by dividing the number of true positive predictions by the sum of true positive and false positive predictions.

• Precision is the measure of the accuracy of the positive predictions made by a model. It is calculated by dividing the number of false positive predictions by the sum of true positive and false positive predictions.

• Precision is the measure of the accuracy of both positive and negative predictions made by a model. It is calculated by dividing the number of true positive predictions by the sum of true positive and false negative predictions.

Explain the concept of recall and its importance in evaluation.

• Recall is the ability of a model to find irrelevant cases within a dataset. It is important in evaluation as it measures the accuracy of the model's predictions.

• Recall is the ability of a model to find all the relevant cases within a dataset. It is important in evaluation as it measures the completeness of the model's predictions.

• Recall is the ability of a model to find all the relevant cases within a dataset. It is important in evaluation as it measures the accuracy of the model's predictions.

• Recall is the ability of a model to find all the irrelevant cases within a dataset. It is important in evaluation as it measures the precision of the model's predictions.

What is the F1 score and why is it considered a better metric than accuracy?

• The F1 score is a measure of a test's accuracy that considers only the false positives of the test.

• The F1 score is a measure of a test's accuracy that considers both the precision and recall of the test. It is considered a better metric than accuracy because it takes into account both false positives and false negatives.

• The F1 score is a measure of a test's accuracy that considers only the recall of the test.

• The F1 score is a measure of a test's accuracy that considers only the precision of the test.

Why is evaluation necessary in the context of AI models?

• To assess the performance, accuracy, and effectiveness of AI models.

• To ignore the performance of AI models

• To make the AI models more confusing

• To waste time and resources

List and define the terminologies commonly used in evaluation.

• Standardized Testing, Grading Curve, Bell Curve, Percentile Rank, Grade Equivalent, Stanine, Z-score

• Assessment, Measurement, Evaluation, Testing, Scoring, Grading, Psychometrics, Psychometrician

• Criterion, Norm-referenced, Formative, Summative, Authentic, Validity, Reliability, Rubric

• Multiple Choice, Descriptive, Objective, Subjective, Quantitative, Qualitative, Correlation, Causation

Explain the components of an evaluation matrix for an AI model.

• Color, size, weight, material

• Metrics such as accuracy, precision, recall, F1 score, confusion matrix, ROC curve, and AUC score.

• Temperature, humidity, pressure, wind speed

• Algebra, geometry, calculus, statistics

How is true positive (TP) defined in the context of evaluation?

• Number of incorrectly identified positive cases

• Number of incorrectly identified negative cases

• Number of correctly identified negative cases

• Number of correctly identified positive cases

What is the significance of false negative (FN) in evaluation?

• False negative (FN) indicates a missed diagnosis or incorrect evaluation.

• False negative (FN) indicates a correct diagnosis or evaluation

• False negative (FN) indicates a successful diagnosis or evaluation

• False negative (FN) has no significance in evaluation