Question 1 : Why SVMâ€™s are more accurate than logistic regression?

- SVM gives more weightage to wrongly classified data points.
- SVM gives more weightages to data points which are correctively classified .
- SVM uses all the data points assuming a probabilistic model.
- SVM uses concept of large margin seperator and for non linearity it uses kernel functions

Question 2 : What is true about the discount factor in reinforcement learning?

- discount factor should be greater than 1
- discount factor should always be negative
- discount factor should be in range of 0 and 1
- discount factor can be any real number

Question 3 : Which algorithm is used for performing probabilistic reasoning on temporal data?

- Hill-climbing search
- Hidden markov model
- NaÃ¯ve Method
- Support Vector Machine

Question 4 : Choose correct applications of reinforcement learning?

- Aircraft Control
- Sentimental analysis
- House price prediction
- Spam Email Filtering

Question 5 : Support Vector Machine(SVM) can be used for both classification or regression challenges.Which kind of learning technique SVM uses?

- supervised
- unsupervised
- reinforced
- clustered

Question 6 : What are two steps of pruning in decision tree ?

- Pessimistic pruning and Optimistic pruning
- Postpruning and Prepruning
- Cost complexity pruning and time complexity pruning
- High pruning and low pruning

Question 7 : Various ____ methods and techniques are used for calculation of the outliers.

- distance calculation
- prediction
- optimization
- integration

Question 8 : Which of the following is a clustering algorithm in machine learning?

- Expectation Maximization
- CART
- Gaussian NaÃ¯ve Bayes
- Apriori

Question 9 : What is the approach of basic algorithm for decision tree induction?

- Greedy
- Top Down
- Procedural
- Step by Step

Question 10 : Support Vector Machine(SVM) performs well in _____ dimension spaces.

- high
- low
- wide
- single

Question 11 : You ran gardient descent for 20 iterations with learning rate=0.2 and compute cost for each iteration.You observe that cost decreases after each iteration .Based on this which conclusion is more suitable.

- Try smaller values of learning rate like 0.01
- 0.2 is effective choice of learning rate.
- Try larger values of learning rate like 1.
- Try any negetive value for learning rate

Question 12 : What are support vectors?

- These are the datapoints which help the SVM to generate optimal hyperplane.
- It is an intermediate vector generated during calculation of optimal hyperplane
- In SVM all the data points are called support vectors.
- This are predefined vectors used in calculating hyperplane

Question 13 : Which of the following problems can be solved by supervised learning too? Assume appropriate dataset is available.

- From a large collection of spam emails, discover if there are sub types of spam emails.
- Given data on how 1000 medical patients respond to an experimental medicine , discover whether there are different categories of patients in terms of how they respond to , and if so what are these categories
- Given a large dataset of medical records from patients suffering from heart disease, try to learn whether there might be different groups of patients for which customised treatment is required
- Given genetic (DNA) data from a person, predict the odds of the person developing diabetes over the next 10 years

Question 14 : Principal component analysis(PCA) is used for___.

- Dimensionality Enhancement
- LU Decomposition
- QR Decomposition
- Dimensionality Reduction

Question 15 : ___________ phenomenon refers that a model is neither trained on training data nor generalized properly on new data.

- good fitting
- overfitting
- moderate fitting
- underfitting

Question 16 : In Logistic regression predicted value of Y lies within _____ range.

- 0 to 1
- 0 to -âˆž
- -âˆž to +âˆž
- -1 to 1

Question 17 : Choose the correct tree based learner.

- Rule based
- Hidden markov model
- Bayesian classifier
- CART

Question 18 : In principal component analysis ,if eigenvalues are equal.What does it mean?

- PCA will perform outstandingly
- PCA will perform badly
- No effect
- Model will be unstable

Question 19 : For a trained logistic classifer given a sample x,it gives prediction as 0.8.This means that___.

- P(Y=0|x)=0.8
- P(Y=1|x)=0.8
- P(Y=0|x)=0.2
- P(Y=1|x)=0.2

Question 20 : K-fold cross-validation is____.

- linear in K
- quadratic in K
- cubic in K
- exponential in K

Question 21 : Consider a point that is correctly classified and distant from the decision boundary. Which of the following methods will be unaffected by this point?

- Nearest neighbor
- Support Vector Machine
- Logistic regression
- Linear regression

Question 22 : You are training an RBF SVM with the following parameters: C (slack penalty) and Î³ = 1/2Ïƒ 2 (where Ïƒ 2 is the variance of the RBF kernel). How should you tweak the parameters to reduce overfitting?

- Increase C and/or reduce Î³
- Reduce C and/or increase Î³
- Reduce C and/or reduce Î³
- Reduce C only (Î³ has no predictable effect on overfitting)

Question 23 : Machine Learning comes under which of the following domain?

- Artificial Intelligence
- Network Security
- Engineeering sciences
- System programming

Question 24 : Choose the reason for pruning a Decision Tree?

- To save computing time during testing
- To avoid overfitting the training set
- To save space for storing the Decision Tree
- To make the training set error smaller

Question 25 : While comparing reinforcement learning and supervised learning, which of the following statement is true?

- Both in reinforcement and supervised learning decisions are taken sequentially
- Supervised learning is best suited where human interaction is prevalant wheareas reinforcement learning is best suited for sofware systems.
- Reinforcement learning works by interacting with environment wheareas supervised learning works on sample data
- Both in reinforcement and supervised learning decisions taken at one time step is independent with respect to previous timestep.

Question 26 : Neural networks:

- Optimize a convex objective function
- Can use a mix of different activation functions
- are not suitable for learning.
- Can only be trained with stochastic gradient descent

Question 27 : Which of the following is not a clustering algorithm?

- EM-Algorithm
- K-means clustering
- Radial Basis Function
- Decision Tree

Question 28 : Principal component analysis is a technique for performing

- Dimensionality reduction
- Pruning
- Aggregation
- Sampling

Question 29 : The process of obtaining best result under given constraints is called as

- Optimization
- Generalization
- Summation
- Regularization

Question 30 : Below are the 8 actual values of the target variable in the train file.[0,0,0,1,1,1,1,1]What is the entropy of the target variable?

- -(5/8 log(5/8) + 3/8 log(3/8))
- 5/8 log(5/8) + 3/8 log(3/8)
- 3/8 log(5/8) + 5/8 log(3/8)
- 5/8 log(3/8) â€“ 3/8 log(5/8)