Machine Learning

Overview

Rewards

FAQs

Overview

Machine Learning (ML) is a branch of Artificial Intelligence (AI) that enables computers to learn from data and improve their performance on tasks without being explicitly programmed. It focuses on developing algorithms that can identify patterns, make predictions, and optimize decision-making processes based on experience.

This questionnaire covers key aspects of Machine Learning, including model training, optimization, and algorithms. It explores various techniques and processes used to develop models that generalize well to unseen data.

Take this assessment to check how good you are with ML.

Click the Rewards tab for eligibility requirements.

Read the FAQs tab carefully for Instructions before beginning the assessment.

Rewards

NYXPoints are used to generate the Leaderboard (coming soon). They are awarded for achieving a certain score.

200 nyxpoints for a passing score of 80% or more
300 nyxpoints for a perfect score of 100%
Didn’t pass? You still get 30 nyxpoints for attempting the assesment

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300 NyxCoins*

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Question 1 of 30

1. Question
Which of the following is an example of a non-parametric machine learning model?
- Logistic Regression
- Decision Tree
- Naive Bayes
- Linear Regression
Correct

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Question 2 of 30

2. Question
Which method is typically used to prevent overfitting in a machine learning model?
- Increasing the dataset size
- Decreasing the learning rate
- Cross-validation
- Increasing the number of epochs
Correct

Incorrect
Question 3 of 30

3. Question
In Gradient Descent, which factor determines how large a step should be taken in the direction of the gradient?
- Loss function
- Batch size
- Learning rate
- Regularization
Correct

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Question 4 of 30

4. Question
Which of the following algorithms is NOT typically used for classification tasks?
- k-Nearest Neighbors
- Support Vector Machine
- Principal Component Analysis
- Decision Trees
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Question 5 of 30

5. Question
What is the purpose of regularization in machine learning models?
- Improve accuracy on training data
- Reduce model complexity to avoid overfitting
- Increase model complexity
- Decrease bias and variance
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Question 6 of 30

6. Question
Which of the following statements about Random Forests is true?
- They are prone to overfitting
- They are ensembles of multiple Decision Trees
- They only work for regression tasks
- Each tree in the forest is trained on the entire dataset
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Question 7 of 30

7. Question
In Support Vector Machines, what is the role of the kernel trick?
- To reduce training time
- To enable the algorithm to handle non-linearly separable data
- To optimize the loss function
- To prevent overfitting
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Question 8 of 30

8. Question
Which of the following techniques is commonly used in neural networks to address vanishing gradients?
- Dropout
- Batch Normalization
- Max Pooling
- Cross-entropy
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Question 9 of 30

9. Question
The ROC curve plots which of the following?
- Precision vs. Recall
- True Positive Rate vs. False Positive Rate
- Accuracy vs. F1 Score
- Precision vs. False Negative Rate
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Question 10 of 30

10. Question
In the context of unsupervised learning, which method is most appropriate for clustering?
- Logistic Regression
- Decision Trees
- K-Means
- Gradient Boosting
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Question 11 of 30

11. Question
What does L2 regularization penalize in a linear model?
- The sum of squared residuals
- The sum of absolute values of weights
- The sum of squared weights
- The difference between predicted and actual values
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Question 12 of 30

12. Question
In reinforcement learning, what is the agent’s goal?
- Minimize the loss function
- Maximize the reward over time
- Minimize variance
- Maximize classification accuracy
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Question 13 of 30

13. Question
Which of the following best describes a high-bias model?
- It performs well on training data but poorly on test data
- It captures noise in the dataset, leading to high test error
- It makes overly simplistic assumptions, leading to underfitting
- It has extremely complex decision boundaries
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Question 14 of 30

14. Question
What is the purpose of early stopping in neural networks?
- To increase the model’s capacity
- To prevent the model from overfitting
- To decrease the learning rate
- To handle class imbalance
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Question 15 of 30

15. Question
What does the term ‘dropout’ refer to in the context of neural networks?
- Removing some neurons randomly during training
- Reducing the learning rate over time
- Halting the training process early
- Reducing the number of layers in the network
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Question 16 of 30

16. Question
Which of the following optimization algorithms uses momentum to speed up training?
- Stochastic Gradient Descent
- Adam
- RMSProp
- Adagrad
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Question 17 of 30

17. Question
How does increasing the value of k in k-NN impact the model?
- It increases variance but decreases bias
- It reduces overfitting but may lose finer details
- It has no effect on performance
- It makes the decision boundary more complex
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Question 18 of 30

18. Question
What is the goal of Principal Component Analysis (PCA)?
- Maximize the classification accuracy
- Maximize variance in the dataset
- Reduce the dimensionality of the dataset
- Minimize the loss function
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Question 19 of 30

19. Question
Which of the following loss functions is most commonly used for regression tasks?
- Cross-entropy
- Hinge loss
- Mean Squared Error (MSE)
- Binary cross-entropy
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Question 20 of 30

20. Question
What is a confusion matrix primarily used for?
- Analyzing errors in classification models
- Performing cross-validation
- Estimating feature importance
- Calculating precision and recall for regression models
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Question 21 of 30

21. Question
What is the purpose of a softmax function in classification models?
- To reduce overfitting
- To normalize input data
- To convert logits to class probabilities
- To apply regularization
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Question 22 of 30

22. Question
Which of the following is a measure of a model’s sensitivity to small changes in the training data?
- Precision
- Variance
- Bias
- Recall
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Question 23 of 30

23. Question
Which of the following neural network architectures is most suitable for time series data?
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs)
- Autoencoders
- GANs
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Question 24 of 30

24. Question
In unsupervised learning, which metric is typically used to evaluate the performance of clustering algorithms like K-means?
- Mean Absolute Error
- Confusion Matrix
- Silhouette Score
- Precision
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Question 25 of 30

25. Question
In XGBoost, what is the main purpose of the “learning rate” hyperparameter?
- To control the depth of individual trees
- To control the regularization strength
- To reduce the contribution of each individual tree
- To increase model interpretability
Correct

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Question 26 of 30

26. Question
Which of the following statements about Bagging and Boosting is true?
- Bagging focuses on reducing bias, while Boosting focuses on reducing variance
- Boosting focuses on reducing bias, while Bagging focuses on reducing variance
- Both Bagging and Boosting reduce bias and variance equally
- Bagging increases the model's complexity, while Boosting reduces it
Correct

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Question 27 of 30

27. Question
What is the primary difference between stochastic gradient descent (SGD) and mini-batch gradient descent?
- The use of regularization
- The number of samples used to compute the gradient
- The model architecture used
- The learning rate adjustment technique
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Question 28 of 30

28. Question
Which of the following can be used as a regularization technique in machine learning models?
- Hyperparameter tuning
- L1 and L2 regularization
- K-Fold cross-validation
- Confusion matrix
Correct

Incorrect
Question 29 of 30

29. Question
What does the term “ensemble learning” refer to in machine learning?
- Combining multiple models to improve prediction performance
- Training a single deep neural network
- Applying a sequence of transformations to the data
- Using reinforcement learning to optimize a single model
Correct

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Question 30 of 30

30. Question
In which of the following cases is label smoothing used?
- To handle imbalanced datasets
- To prevent overfitting in neural networks
- To adjust overly confident predictions in classification
- To reduce the dimensionality of the output layer
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