Model Evaluation and Selection in Data Mining: Quiz and Flashcards

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Questions and Answers

What is the primary purpose of evaluating different classification models?

To predict the ability of different models to accurately classify independent test data (correct)

To compare the accuracy of different algorithms

To reduce the size of the dataset

To select the simplest model

What technique is used to ensure that each class is properly represented in both training and test sets?

K-fold cross-validation

Leave-one-out cross-validation

Random sampling

Stratification (correct)

What is the purpose of dividing the dataset into a training set and a test set?

To evaluate the performance of the classification model (correct)

To create a new classification model

To discover patterns in the data

To reduce the size of the dataset

What is the advantage of using stratification in tenfold cross-validation?

It ensures that each class is properly represented in both training and test sets Signup and view all the answers

What is the purpose of using a test set in model evaluation?

To evaluate the performance of the classification model on unseen data Signup and view all the answers

What is the purpose of using k-fold cross-validation?

To evaluate the performance of the classification model on independent test data Signup and view all the answers

What is the main advantage of Leave-one-out Cross Validation?

It uses the greatest possible amount of data for training in each round Signup and view all the answers

What is the number of rounds in Leave-one-out Cross Validation?

n Signup and view all the answers

What is the disadvantage of Leave-one-out Cross Validation?

It has a high computational cost Signup and view all the answers

What is the predictive accuracy of the classification algorithm in Leave-one-out Cross Validation?

The mean predictive accuracy Signup and view all the answers

What is the number of examples used for testing in each round of Leave-one-out Cross Validation?

1 Signup and view all the answers

What is the purpose of repeating the stratified tenfold cross-validation process 10 times?

To reduce the effect of uneven representation of examples in training and test sets Signup and view all the answers

What is the advantage of using stratified tenfold cross-validation over a single training/test set partition?

It provides a statistically more robust accuracy estimate Signup and view all the answers

What is the purpose of performing stratified tenfold cross-validation for each classification algorithm?

To select the classification algorithm with the highest predictive accuracy Signup and view all the answers

What is a disadvantage of using stratified tenfold cross-validation?

It is computationally expensive Signup and view all the answers

What is the purpose of re-training the selected algorithm on all the data?

To increase the predictive performance of the final classification model Signup and view all the answers

What is the purpose of stratified division of data in stratified tenfold cross-validation?

To ensure that the class values are proportionally represented in each fold Signup and view all the answers

What is the purpose of using a training set and a test set in model evaluation?

To evaluate the ability of a classification model to accurately classify independent test data Signup and view all the answers

How does tenfold cross-validation work?

Randomly divide the data into 10 equal parts, using one fold as a test set and the remaining 9 folds as a training set, with stratification in both sets Signup and view all the answers

Why is it important to use stratification in cross-validation?

To ensure each class is properly represented in both training and test sets Signup and view all the answers

What is the advantage of using k-fold cross-validation over a single training/test set partition?

It provides a more robust evaluation of the model by averaging the performance over multiple folds Signup and view all the answers

What is the purpose of evaluating different classification models?

To discover patterns from a single data set and predict the ability of different models to accurately classify independent test data Signup and view all the answers

Why is it important to evaluate a model's performance on unseen data?

To estimate the model's future performance on new, unseen data Signup and view all the answers

What is the advantage of using Leave-one-out Cross Validation, especially for small datasets?

It uses the greatest possible amount of data for training in each round, increasing the chance to create an accurate classifier. Signup and view all the answers

What is the primary disadvantage of Leave-one-out Cross Validation?

High computational cost. Signup and view all the answers

What happens in each round of Leave-one-out Cross Validation?

One example is held out for testing, and the remaining examples are used for training. Signup and view all the answers

How is the predictive accuracy of the classification algorithm calculated in Leave-one-out Cross Validation?

It is the mean predictive accuracy. Signup and view all the answers

What type of procedure is Leave-one-out Cross Validation?

Deterministic. Signup and view all the answers

What is the purpose of performing stratified tenfold cross-validation for classification algorithms?

To obtain the predictive accuracy of each classification algorithm by averaging the accuracy over 10 rounds. Signup and view all the answers

What is the benefit of using stratified tenfold cross-validation over a single training/test set partition?

It provides a statistically more robust accuracy estimate. Signup and view all the answers

Why is it important to re-train the selected algorithm on all the data?

To maximize the amount of data used to produce the final classification model and increase its predictive performance. Signup and view all the answers

What is the consequence of uneven representation of examples in training and test sets?

It can reduce the predictive accuracy of the classification model. Signup and view all the answers

What is the computational cost of using stratified tenfold cross-validation?

It is computationally expensive, as each classification algorithm is trained 10 times, with 90% of the data used for training each time. Signup and view all the answers

What is the purpose of selecting the classification algorithm with the highest predictive accuracy?

To produce the final classification model with the highest predictive performance. Signup and view all the answers

Study Notes