DL_Unit-1

In the context of linear classifiers, which of the following statements best describes the relationship between loss functions and optimization algorithms in machine learning?

• Loss functions and optimization algorithms are independent of each other and serve distinct purposes in the training process.

• The choice of loss function directly determines the optimization algorithm to be used, with different loss functions requiring specific optimization techniques for convergence.

• Optimization algorithms and loss functions are interdependent, with the optimization algorithm guiding the update of model parameters based on the gradients computed from the loss function.

• Loss functions and optimization algorithms are interchangeable terms used to measure the model's performance and are not directly related to the training process.

Which of the following is a widely used and effective machine learning algorithm based on the idea of bagging?

• Decision Tree

• Regression

• Classification

• Random Forest

When performing regression or classification, which of the following is the correct way to preprocess the data?

• Normalize the data -> PCA -> normalize PCA output -> training

• Normalize the data -> PCA -> training

• None of the above

• PCA -> normalize PCA output -> training

In which of the following cases will K-means clustering fail to give good results?

1) Data points with outliers
2) Data points with different densities
3) Data points with nonconvex shapes

• 
  2 and 3
  

• 1 and 2

• 1 and 3

• All of the above

p → 0q is not a?

• system clause

• horn clause

• hack clause

• structural clause

When you find many noises in data, which of the following options would you consider in kNN?

• • Increase the value of k

• • Decrease the value of k

• • k = 0

• • Noise does not depend on k

A machine learning problem involves four attributes plus a class. The attributes have 3, 2, 2, and 2 possible values each. The class has 3 possible values. How many maximum possible different examples are there?

• 1. 12

• 1. 24

• 1. 48

• 1. 72

Which of the following is a reasonable way to select the number of principal components “k”?

• Choose k to be the smallest value so that at least 99% of the varinace is retained

• Use the elbow method

• Choose k to be 99% of m (k = 0.99*m, rounded to the nearest integer)

• Choose k to be the largest value so that 99% of the variance is retained

A feature F1 can take certain value: A, B, C, D, E, F and represents grade of students from a college. Which of the following statements is true in the following case?

• A. Feature F1 is an example of a nominal variable.

• B. Feature F1 is an example of ordinal variables.

• C. It doesn’t belong to any of the above categories.

•  D. Both of these

Which of the following criteria is typically used for optimizing in linear regression.

• Maximize the number of points it touches.

• Minimize the number of points it touches.

• Minimize the squared distance from the points.

• Minimize the maximum distance of a point from a line.