Machine learning questions with answers
371 multiple-choice questions. Open any question for its answer and explanation.
- Which statement is true about the K-Means algorithm?
- Which of the following is true about weighted majority votes? 1. We want to give higher weights to better performing models 2. Inferior models can overrule the best model if collective weighted votes for inferior models is higher than best model 3. Voting is special case of weighted voting
- What is PCA, KPCA and ICA used for?
- scikit-learn also provides a class for per-sample normalization, Normalizer. It can apply . . . . . . . . to each element of a dataset
- Problem:Players will play if weather is sunny. Is this statement is correct?
- The . . . . . . . . of the hyperplane depends upon the number of features.
- The difference between the actual Y value and the predicted Y value found using a regression equation is called the
- Suppose that we have N independent variables (X1, X2, Xn) and dependent variable is Y. Now Imagine that you are applying linear regression by fitting the best fit line using least square error on this data. You found that correlation coefficient for one of its variable(Say X1) with Y is -0.95. Which of the following is true for X1?
- Which of the following sentences are true?
- Solving a non linear separation problem with a hard margin Kernelized SVM (Gaussian RBF Kernel) might lead to overfitting
- How does the bias-variance decomposition of a ridge regression estimator compare with that of ordinary least squares regression?
- The objective of the support vector machine algorithm is to find a hyperplane in an N-dimensional space(N the number of features) that distinctly classifies the data points.
- Impact of high variance on the training set ?
- Wrapper methods are hyper-parameter selection methods that
- Even if there are no actual supervisors . . . . . . . . learning is also based on feedback provided by the environment
- . . . . . . . . produce sparse matrices of real numbers that can be fed into any machine learning model.
- A student Grade is a variable F1 which takes a value from A,B,C and D. Which of the following is True in the following case?
- To control the size of the tree, we need to control the number of regions. One approach to do this would be to split tree nodes only if the resultant decrease in the sum of squares error exceeds some threshold. For the described method, which among the following are true? a. It would, in general, help restrict the size of the trees b. It has the potential to affect the performance of the resultant regression/classification model c. It is computationally infeasible
- Bayes Theorem is given by where 1. P(H) is the probability of hypothesis H being true. 2. P(E) is the probability of the evidence(regardless of the hypothesis). 3. P(E|H) is the probability of the evidence given that hypothesis is true. 4. P(H|E) is the probability of the hypothesis given that the evidence is there.
- Machine learning techniques differ from statistical techniques in that machine learning methods
- The most popularly used dimensionality reduction algorithm is Principal Component Analysis (PCA). Which of the following is/are true about PCA? 1. PCA is an unsupervised method 2. It searches for the directions that data have the largest variance 3. Maximum number of principal components
- A database has 5 transactions. Of these, 4 transactions include milk and bread. Further, of the given 4 transactions, 2 transactions include cheese. Find the support percentage for the following association rule "if milk and bread are purchased, then cheese is also purchased".
- Any linear combination of the components of a multivariate Gaussian is a univariate Gaussian.
- Which of the following quantities are minimized directly or indirectly during parameter estimation in Gaussian distribution Model?
- What is the purpose of performing cross-validation?