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Updated: Jun 25, 2026
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1. In a regression model, the mean squared error (MSE) loss function measures:
Response:
A) The proportion of variance explained by the model
B) The total sum of squared residuals
C) The average of the squares of the differences between actual and predicted values
D) The average of absolute errors
2. What is the Fourier Series used for in data science?
Response:
A) To calculate the mean of the dataset
B) To generate random numbers for a model
C) To break down periodic functions into sums of sine and cosine terms
D) To determine the probability of an event
3. What is the primary use of 'k-fold cross-validation' in machine learning?
Response:
A) To select the most important features of the dataset
B) To increase the size of the training dataset
C) To optimize the hyperparameters of the model
D) To assess the model's performance on different subsets of the data
4. In machine learning, what does 'model generalization' mean?
Response:
A) Combining multiple models to improve performance
B) The model's ability to perform well on new, unseen data
C) Increasing the number of features in the model
D) Simplifying the model to make it more interpretable
5. What is the main advantage of using a 'bagging' approach in ensemble learning?
Response:
A) To optimize individual model parameters
B) To increase the bias in the model
C) To enhance the speed of training models
D) To reduce the variance of the model
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: C | Question # 3 Answer: D | Question # 4 Answer: B | Question # 5 Answer: D |
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