Model Evaluation and Validation MCQs With Answers

Welcome to the Model Evaluation and Validation MCQs with Answers. In this post, we have shared Model Evaluation and Validation Online Test for different competitive exams. Find practice Model Evaluation and Validation Practice Questions with answers in Computer Tests exams here. Each question offers a chance to enhance your knowledge regarding Model Evaluation and Validation.

Model Evaluation and Validation Online Quiz

By presenting 3 options to choose from, Model Evaluation and Validation Quiz which cover a wide range of topics and levels of difficulty, making them adaptable to various learning objectives and preferences. You will have to read all the given answers of Model Evaluation and Validation Questions and Answers and click over the correct answer.

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

Note: Answer of the questions will change randomly each time you start the test. Practice each quiz test at least 3 times if you want to secure High Marks. Once you are finished, click the View Results button. If any answer looks wrong to you in Quizzes. simply click on question and comment below that question. so that we can update the answer in the quiz section.

0

---

Model Evaluation and Validation

Please fill out the form before starting Quiz.

1 / 40

What is precision in model evaluation?

A measure of preprocessing

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

A measure of recall

2 / 40

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

A measure of preprocessing

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

A measure of model complexity

3 / 40

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

To validate the model

To preprocess data

To evaluate the final performance of the model on unseen data

4 / 40

What is the purpose of confusion matrix in model evaluation?

To increase model complexity

To preprocess data

To summarize the performance of a classification model

5 / 40

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

Preprocessing data

To prevent overfitting by adding a penalty to the model

Increasing model complexity

6 / 40

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

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

A measure of preprocessing

A measure of model complexity

7 / 40

What is bagging (Bootstrap Aggregating) in model evaluation?

Preprocessing data

Increasing model complexity

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

8 / 40

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

A measure of model complexity

A measure of the difference between predicted and actual values

A measure of preprocessing

9 / 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

10 / 40

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

Preprocessing data

It provides a more reliable estimate of model performance

Increasing model complexity

11 / 40

What are false positives in a confusion matrix?

Correctly predicted positive instances

Incorrectly predicted negative instances

Incorrectly predicted positive instances

12 / 40

What is the purpose of model validation?

To ensure the model generalizes well to unseen data

To preprocess data

To increase model complexity

13 / 40

What is hyperparameter tuning in model evaluation?

Preprocessing data

Increasing model complexity

Optimizing the parameters that are not directly learned by the model

14 / 40

What is cross-validation used for in model evaluation?

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

To preprocess data

To increase model complexity

15 / 40

What is the meaning of generalization error in model evaluation?

The error rate on new, unseen data

Model training error

Preprocessing data

16 / 40

What is the purpose of learning curves in model evaluation?

To increase model complexity

To visualize how model performance changes with training set size

To preprocess data

17 / 40

What is accuracy in model evaluation?

A measure of model complexity

The ratio of correctly predicted observations to the total observations

A measure of preprocessing

18 / 40

What are false negatives in a confusion matrix?

Incorrectly predicted negative instances

Incorrectly predicted positive instances

Correctly predicted negative instances

19 / 40

What is F1-score in model evaluation?

A measure of accuracy

The harmonic mean of precision and recall

A measure of model complexity

20 / 40

What is randomized search in hyperparameter tuning?

Preprocessing data

Increasing model complexity

Sampling hyperparameters randomly from specified distributions

21 / 40

What is underfitting in machine learning?

When a model is too complex

When a model overfits the training data

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

22 / 40

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

To preprocess data

To train the model

To tune hyperparameters and assess model performance during training

23 / 40

What is early stopping in model training?

Increasing model complexity

Preprocessing data early

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

24 / 40

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

To preprocess data

To visualize the performance of a binary classifier

To increase model complexity

25 / 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

26 / 40

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

A measure of how well the model fits the data

A measure of model complexity

A measure of preprocessing

27 / 40

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

To validate the model

To preprocess data

To train the model on known data

28 / 40

What are true negatives in a confusion matrix?

Incorrectly predicted negative instances

Correctly predicted positive instances

Correctly predicted negative instances

29 / 40

What is multiclass classification used for in model evaluation?

Preprocessing data

Increasing model complexity

Classifying instances into more than two classes

30 / 40

What is model persistence in model evaluation?

Increasing model complexity

Saving the trained model to disk for future use

Preprocessing data

31 / 40

What is AUC-ROC in model evaluation?

A measure of accuracy

A measure of model complexity

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

32 / 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

33 / 40

What is model evaluation in machine learning?

Preprocessing data

Assessing the performance and effectiveness of a trained model

Training the model

34 / 40

What is ensemble learning in model evaluation?

Preprocessing data

Using multiple models to improve prediction performance

Increasing model complexity

35 / 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

36 / 40

What is recall in model evaluation?

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

A measure of precision

A measure of preprocessing

37 / 40

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

To ensure class proportions are maintained in each fold

To preprocess data

To increase model complexity

38 / 40

What is boosting in model evaluation?

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

Preprocessing data

Increasing model complexity

39 / 40

What are true positives in a confusion matrix?

Incorrectly predicted positive instances

Correctly predicted positive instances

Correctly predicted negative instances

40 / 40

What is grid search in hyperparameter tuning?

Increasing model complexity

Preprocessing data

Exhaustively searching through a manually specified subset of hyperparameters

0%

Restart quiz

Download Certificate of Quiz Model Evaluation and Validation

On the end of Quiz, you can download the certificate of the quiz if you got more than 70% marks. Add a certificate to your job application or social profile (like LinkedIn) and get more job offers.

Download Model Evaluation and Validation MCQs with Answers Free PDF

You can also download 100 Model Evaluation and Validation Questions with Answers free PDF from the link provided below. To Download file in PDF click on the arrow sign at the top right corner.

If you are interested to enhance your knowledge regarding  English, Physics, Chemistry, and Biology please click on the link of each category, you will be redirected to dedicated website for each category.