Data Science MCQs – Page 5 – EasyExamNotes.com

#1. What is the purpose of regularization techniques like L1 and L2 regularization in machine learning?

1. To increase the number of features in the dataset

2. To reduce the complexity of the model

3. To add noise to the data and increase variability

4. To decrease the learning rate of the model

5. None of the above

#2. Which algorithm is commonly used for natural language processing tasks such as text generation and language translation?

1. Decision Tree

2. K-Means Clustering

3. Recurrent Neural Network (RNN)

4. Support Vector Machine (SVM)

5. None of the above

#3. What does the term “batch gradient descent” refer to in the context of machine learning optimization?

1. Updating model parameters after each training example

2. Updating model parameters using the entire training dataset

3. Updating model parameters using a random subset of the training data

4. Updating model parameters using the validation data

5. None of the above

#4. What is the primary purpose of the term “bias-variance tradeoff” in machine learning?

1. The balance between underfitting and overfitting

2. The tradeoff between training time and accuracy

3. The tradeoff between model complexity and prediction accuracy

4. The balance between recall and precision

5. None of the above

#5. What is the purpose of the “SVM” (Support Vector Machine) algorithm in machine learning?

1. To perform clustering of data points

2. To predict continuous values

3. To classify data points into two categories

4. To generate random numbers for simulations

5. None of the above

#6. What is the primary function of “gradient boosting” algorithms in machine learning?

1. To decrease the training time of the model

2. To decrease the complexity of the model

3. To combine multiple weak learners into a strong learner

4. To increase the learning rate of the model

5. None of the above

#7. What does the term “bagging” refer to in ensemble learning techniques?

1. Combining multiple weak learners into a strong learner

2. Training multiple models independently and combining their predictions

3. Assigning weights to features based on their importance

4. Adjusting hyperparameters of the model

5. None of the above

#8. What is the primary purpose of the “dropout” technique in neural networks?

1. To reduce the learning rate

2. To add noise to the input data to increase variability

3. To randomly deactivate some neurons during training

4. To increase the number of layers in the network

5. None of the above

#9. Which technique is commonly used for feature scaling in machine learning?

1. Min-Max Scaling

2. Feature Engineering

3. One-Hot Encoding

4. Decision Trees

5. None of the above

#10. What is the primary purpose of A/B testing in data science?

1. To perform feature selection for machine learning models

2. To compare two versions of a webpage or app and determine which one performs better

3. To clean and preprocess data

4. To transform categorical variables into numerical values

5. None of the above

#11. What is the primary objective of the K-Nearest Neighbors (KNN) algorithm in machine learning?

1. To classify data points into predefined classes

2. To optimize decisions over time using trial and error

3. To predict continuous values

4. To find patterns and relationships in data points

5. None of the above

#12. In the context of machine learning, what is the role of the activation function in a neural network?

1. To preprocess input data before feeding it to the network

2. To control the learning rate of the network

3. To transform the weighted sum of inputs into an output

4. To calculate the distance between data points

5. None of the above

#13. What is the purpose of the term “confusion matrix” in the evaluation of classification models?

1. To measure the prediction accuracy of a model

2. To calculate the area under the ROC curve

3. To visualize the performance of a model

4. To calculate true positive, true negative, false positive, and false negative values

5. None of the above

#14. What does the term “bagging” refer to in ensemble learning techniques?

1. Combining multiple weak learners into a strong learner

2. Training multiple models independently and combining their predictions

3. Assigning weights to features based on their importance

4. Adjusting hyperparameters of the model

5. None of the above

#15. What is the purpose of the “dropout” technique in neural networks?

1. To reduce the learning rate

2. To add noise to the input data to increase variability

3. To randomly deactivate some neurons during training

4. To increase the number of layers in the network

5. None of the above

#16. Which technique is commonly used for feature scaling in machine learning?

1. Min-Max Scaling

2. Feature Engineering

3. One-Hot Encoding

4. Decision Trees

5. None of the above

#17. What is the primary purpose of A/B testing in data science?

1. To perform feature selection for machine learning models

2. To compare two versions of a webpage or app and determine which one performs better

3. To clean and preprocess data

4. To transform categorical variables into numerical values

5. None of the above

#18. What is the purpose of k-fold cross-validation in machine learning?

1. To split the dataset into training and testing sets

2. To evaluate a model's performance on an independent dataset

3. To create multiple train-test splits and average the performance

4. To transform categorical variables into numerical values

5. None of the above

#19. What does the term “bagging” refer to in ensemble learning techniques?

1. Combining multiple weak learners into a strong learner

2. Training multiple models independently and combining their predictions

3. Assigning weights to features based on their importance

4. Adjusting hyperparameters of the model

5. None of the above

#20. What is the primary purpose of the “dropout” technique in neural networks?

1. To reduce the learning rate

2. To add noise to the input data to increase variability

3. To randomly deactivate some neurons during training

4. To increase the number of layers in the network

5. None of the above

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