MACH

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

Which of the following is a popular evaluation metric for regression problems?

• F1 Score

• Root Mean Squared Error (RMSE)

• Precision

• Recall

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

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

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

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

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

How is the performance of a model estimated using cross-validation for classification problems?

• By calculating the mean squared error of the model on each fold

• By calculating the root mean squared error of the model on each fold

• By calculating the mean absolute error of the model on each fold

• By calculating the mean accuracy of the model on each fold

What is the purpose of a residual plot in linear regression?

• None of these

• To visualize the distribution of the residuals.

• To determine the optimal degree of the polynomial regression model.

• To determine the strength of the relationship between the independent and dependent variables.

In Support Vector Machines (SVM), what is a hyperplane?

• classes

• targets

• data points

• features

• decision boundaries