Overfitting, Underfitting, Bias-Variance Quiz (Easy)

1. Question

What is Overfitting in machine learning?

Training a model on a large dataset

Correct Incorrect Correct answer

Training a model that performs well on both training and test data

Correct Incorrect Correct answer

Training a model that is too complex and performs well on training data but poorly on new, unseen data

Correct Incorrect Correct answer

Training a model that is too simple

Correct Incorrect Correct answer

2. Question

What is a common sign of overfitting in a trained model?

High bias

Correct Incorrect Correct answer

High variance

Correct Incorrect Correct answer

Low variance

Correct Incorrect Correct answer

Low complexity

Correct Incorrect Correct answer

3. Question

How can overfitting be prevented in a machine learning model?

By training the model for more iterations

Correct Incorrect Correct answer

By using a larger and more complex model

Correct Incorrect Correct answer

By reducing the complexity of the model and using techniques like regularization

Correct Incorrect Correct answer

By using all available data for training and none for testing

Correct Incorrect Correct answer

4. Question

What is Underfitting in machine learning?

Training a model that is too complex

Correct Incorrect Correct answer

Training a model that performs poorly on both training and test data

Correct Incorrect Correct answer

Training a model on a small dataset

Correct Incorrect Correct answer

Training a model that performs exceptionally well on test data

Correct Incorrect Correct answer

5. Question

A common cause of underfitting is:

Too many training epochs

Correct Incorrect Correct answer

An overly complex model

Correct Incorrect Correct answer

Too few features in the model

Correct Incorrect Correct answer

Excessive regularization

Correct Incorrect Correct answer

6. Question

Which is a possible solution to underfitting?

Increasing model complexity

Correct Incorrect Correct answer

Adding more noise to the data

Correct Incorrect Correct answer

Reducing the number of features

Correct Incorrect Correct answer

Applying more regularization

Correct Incorrect Correct answer

7. Question

Bias in a machine learning model refers to:

The variability of model predictions

Correct Incorrect Correct answer

The error due to overly simplistic assumptions in the model

Correct Incorrect Correct answer

The error introduced by noisy data

Correct Incorrect Correct answer

Overfitting of the model

Correct Incorrect Correct answer

8. Question

Which of the following is true about a high bias, low variance model?

It generally underfits the data

Correct Incorrect Correct answer

It is too complex

Correct Incorrect Correct answer

It typically has a low training and test error

Correct Incorrect Correct answer

It is ideal for unknown datasets

Correct Incorrect Correct answer

9. Question

In the context of bias-variance trade-off, a good model:

Always has zero bias

Correct Incorrect Correct answer

Balances bias and variance to minimize total error

Correct Incorrect Correct answer

Focuses only on reducing variance

Correct Incorrect Correct answer

Has high bias to avoid overfitting

Correct Incorrect Correct answer

10. Question

The phenomenon where a model learns the random noise in the data rather than the actual pattern is known as:

High bias / Underfitting

Correct Incorrect Correct answer

Feature Engineering

Correct Incorrect Correct answer

High variance / Overfitting

Correct Incorrect Correct answer

Regularization

Correct Incorrect Correct answer

11. Question

A model with high variance and low bias is likely to:

Be overly simple

Correct Incorrect Correct answer

Underfit the data

Correct Incorrect Correct answer

Overfit the data

Correct Incorrect Correct answer

Be robust to new data

Correct Incorrect Correct answer

Pos.	Name	Entered on	Points	Result
Table is loading
No data available

Overfitting, Underfitting, Bias-Variance Quiz (Easy)

Quiz Summary

Information

Results

Results

Categories

1. Question

2. Question

3. Question

4. Question

5. Question

6. Question

7. Question

8. Question

9. Question

10. Question

11. Question