Model Evaluation and Validation MCQs With Answers

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Model Evaluation and Validation Online Quiz

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• Test Name: Model Evaluation and Validation MCQ Quiz Practice
• Type: Quiz Test
• Total Questions: 40
• Total Marks: 40
• Time: 40 minutes

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Model Evaluation and Validation

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1 / 40

What is AUC-ROC in model evaluation?

The Area Under the Receiver Operating Characteristic curve, which measures the ability of the model to distinguish between classes

A measure of model complexity

A measure of accuracy

2 / 40

What is model persistence in model evaluation?

Preprocessing data

Saving the trained model to disk for future use

Increasing model complexity

3 / 40

What is recall in model evaluation?

A measure of preprocessing

A measure of precision

The ratio of correctly predicted positive observations to all observations in the actual class

4 / 40

What is cross-validation used for in model evaluation?

To increase model complexity

To assess how the model generalizes to new data by splitting the dataset into multiple subsets

To preprocess data

5 / 40

What is the purpose of stratified sampling in cross-validation?

To preprocess data

To increase model complexity

To ensure class proportions are maintained in each fold

6 / 40

What is the purpose of confusion matrix in model evaluation?

To preprocess data

To summarize the performance of a classification model

To increase model complexity

7 / 40

What is the purpose of regularization techniques like Lasso and Ridge in model evaluation?

Increasing model complexity

Preprocessing data

To prevent overfitting by adding a penalty to the model

8 / 40

What is randomized search in hyperparameter tuning?

Preprocessing data

Sampling hyperparameters randomly from specified distributions

Increasing model complexity

9 / 40

What is the purpose of learning curves in model evaluation?

To preprocess data

To visualize how model performance changes with training set size

To increase model complexity

10 / 40

What are false positives in a confusion matrix?

Incorrectly predicted negative instances

Incorrectly predicted positive instances

Correctly predicted positive instances

11 / 40

What is k-fold cross-validation?

A technique to preprocess data

A technique to handle missing values

A technique where the dataset is divided into k subsets and the model is trained and evaluated k times

12 / 40

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

To evaluate the final performance of the model on unseen data

To preprocess data

To validate the model

13 / 40

What are true negatives in a confusion matrix?

Correctly predicted negative instances

Incorrectly predicted negative instances

Correctly predicted positive instances

14 / 40

What is ensemble learning in model evaluation?

Increasing model complexity

Using multiple models to improve prediction performance

Preprocessing data

15 / 40

What is the purpose of the validation set in model evaluation?

To preprocess data

To tune hyperparameters and assess model performance during training

To train the model

16 / 40

What is accuracy in model evaluation?

A measure of preprocessing

The ratio of correctly predicted observations to the total observations

A measure of model complexity

17 / 40

What are true positives in a confusion matrix?

Correctly predicted positive instances

Correctly predicted negative instances

Incorrectly predicted positive instances

18 / 40

What is early stopping in model training?

Preprocessing data early

Stopping the training process when the performance on a validation set starts to degrade

Increasing model complexity

19 / 40

What are false negatives in a confusion matrix?

Incorrectly predicted negative instances

Correctly predicted negative instances

Incorrectly predicted positive instances

20 / 40

What is precision-recall curve used for in model evaluation?

To increase model complexity

To preprocess data

To visualize the trade-off between precision and recall for different thresholds

21 / 40

What is model evaluation in machine learning?

Assessing the performance and effectiveness of a trained model

Training the model

Preprocessing data

22 / 40

What is the meaning of generalization error in model evaluation?

Model training error

Preprocessing data

The error rate on new, unseen data

23 / 40

What is the purpose of the training set in model evaluation?

To train the model on known data

To validate the model

To preprocess data

24 / 40

What is RMSE (Root Mean Squared Error) used for in model evaluation?

A measure of preprocessing

A measure of the difference between predicted and actual values

A measure of model complexity

25 / 40

What is F1-score in model evaluation?

A measure of model complexity

The harmonic mean of precision and recall

A measure of accuracy

26 / 40

What is bagging (Bootstrap Aggregating) in model evaluation?

Preprocessing data

A technique to reduce variance by averaging multiple models trained on different subsets of the data

Increasing model complexity

27 / 40

What is hyperparameter tuning in model evaluation?

Preprocessing data

Increasing model complexity

Optimizing the parameters that are not directly learned by the model

28 / 40

What is multiclass classification used for in model evaluation?

Preprocessing data

Classifying instances into more than two classes

Increasing model complexity

29 / 40

What is the purpose of receiver operating characteristic (ROC) curve in model evaluation?

To visualize the performance of a binary classifier

To preprocess data

To increase model complexity

30 / 40

What is the purpose of model validation?

To increase model complexity

To ensure the model generalizes well to unseen data

To preprocess data

31 / 40

What is bias-variance trade-off in model evaluation?

A measure of preprocessing

A measure of model complexity

The balance between underfitting (high bias) and overfitting (high variance)

32 / 40

What is overfitting in machine learning?

When a model is too simple

When a model underfits the training data

When a model learns the training data too well, including noise and outliers

33 / 40

What is underfitting in machine learning?

When a model overfits the training data

When a model is too complex

When a model is too simple to capture the underlying pattern of the data

34 / 40

What is precision in model evaluation?

The ratio of correctly predicted positive observations to the total predicted positive observations

A measure of recall

A measure of preprocessing

35 / 40

What is R-squared (Coefficient of Determination) used for in model evaluation?

A measure of model complexity

A measure of preprocessing

A measure of how well the model fits the data

36 / 40

What is boosting in model evaluation?

A technique to sequentially train models, giving more weight to instances that were previously misclassified

Increasing model complexity

Preprocessing data

37 / 40

What is MAE (Mean Absolute Error) used for in model evaluation?

A measure of preprocessing

A measure of model complexity

A measure of the average magnitude of errors in a set of predictions

38 / 40

What is the advantage of using cross-validation over a single train-test split?

It provides a more reliable estimate of model performance

Increasing model complexity

Preprocessing data

39 / 40

What is grid search in hyperparameter tuning?

Exhaustively searching through a manually specified subset of hyperparameters

Preprocessing data

Increasing model complexity

40 / 40

What is the purpose of ROC-AUC in model evaluation?

It measures the area under the ROC curve, indicating model discrimination ability

To preprocess data

To increase model complexity

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