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Questions and Answers
What is a classifier?
What is a classifier?
- A statistical measure of the accuracy of an estimate
- A method for estimating predictive accuracy
- An algorithm that assigns a classification to unseen instances (correct)
- A tabular way of presenting classifier performance information
Which method involves using separate training and test sets to estimate predictive accuracy?
Which method involves using separate training and test sets to estimate predictive accuracy?
- Repeated Train and Test
- Method 2: k-fold Cross-validation
- Standard Error
- Method 1: Separate Training and Test Sets (correct)
What is the purpose of a confusion matrix?
What is the purpose of a confusion matrix?
- To understand standard error
- To illustrate experiments to estimate predictive accuracy
- To explore the methods for estimating predictive accuracy
- To present classifier performance information in a tabular way (correct)
What is the purpose of standard error in estimating predictive accuracy?
What is the purpose of standard error in estimating predictive accuracy?
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What does k-fold cross-validation involve?
What does k-fold cross-validation involve?
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Study Notes
Classification Concepts
- A classifier is a model that assigns a class or label to new, unseen data based on patterns learned from training data.
Evaluating Model Performance
- Holdout Method: Involves using separate training and test sets to estimate predictive accuracy, where the model is trained on the training set and its performance is evaluated on the test set.
- The Confusion Matrix is a table used to evaluate the performance of a classifier, comparing predicted classes against actual classes, and provides metrics such as accuracy, precision, and recall.
Measuring Predictive Accuracy
- Standard Error: Represents the amount of variation in the estimate of predictive accuracy, providing a range of values within which the true accuracy is likely to lie.
- K-fold Cross-Validation: Involves dividing the dataset into k subsets, training the model on k-1 subsets, and evaluating its performance on the remaining subset, repeating this process k times to obtain a more robust estimate of predictive accuracy.
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