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30 questions
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1.
MULTIPLE CHOICE QUESTION
1 min ⢠1 pt
How does SVM handle non-linearly separable data?
By using decision trees to split the data into linearly separable regions
By transforming the data into a higher dimensional space where it becomes linearly separable
By fitting a polynomial function to the data
By using a kernel function to map the data into a higher dimensional space
2.
MULTIPLE CHOICE QUESTION
1 min ⢠1 pt
Which of the following is a popular evaluation metric for regression problems?
F1 Score
Root Mean Squared Error (RMSE)
Precision
Recall
3.
MULTIPLE CHOICE QUESTION
1 min ⢠1 pt
Which of the following is not a disadvantage of the K-means algorithm when applied to high-dimensional data?
a, b and
The "curse of dimensionality" can make it difficult to identify clusters that are well-separated
It can struggle to identify clusters that have different densities or sizes
It can be computationally expensive to compute pairwise distances between all data points
None of the above
4.
MULTIPLE CHOICE QUESTION
1 min ⢠1 pt
How does SVM handle imbalanced data?
By removing some instances from the majority class
By undersampling the majority class
By adjusting the weights of the classes in the cost function
By oversampling the minority class
5.
MULTIPLE CHOICE QUESTION
1 min ⢠1 pt
What is bootstrapping in statistics?
A method to fit a regression model to a dataset
A process of reducing the size of a dataset
A technique to generate new datasets by randomly sampling with replacement from a given data set
A method of estimating the variance of a population
6.
MULTIPLE CHOICE QUESTION
1 min ⢠1 pt
What is the purpose of a confusion matrix in logistic regression?
To evaluate the accuracy of the model
To evaluate the stability of the model
To evaluate the complexity of the model
To evaluate the speed of the model
7.
MULTIPLE CHOICE QUESTION
1 min ⢠1 pt
What is the main advantage of Kernel PCA over standard PCA?
It is more computationally efficient
It can find a higher-dimensional representation of the data
It can handle non-linear relationships in the data
None of the above
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