# Machine Learning Set 22

This set of Machine Learning (ML) Multiple Choice Questions & Answers (MCQs) focuses on Machine Learning Set 22

Q1 | We have been given a dataset with n records in which we have input attribute as x and output attribute as y. Suppose we use a linear regression method to model this data. To test our linear regressor, we split the data in training set and test set randomly. Now we increase the training set size gradually. As the training set size increases, what do you expect will happen with the meantraining error?
Q2 | We have been given a dataset with n records in which we have input attribute as x and output attribute as y. Suppose we use a linear regression method to model this data. To test our linear regressor, we split the data in training set and test set randomly. What do you expect will happen with bias and variance as you increase the size oftraining data?
Q3 | Suppose, you got a situation where you find that your linear regression model is under fitting the data. In such situation which of the following options would you consider?1. I will add more variables2. I will start introducing polynomial degree variables3. I will remove some variables
Q4 | Problem: Players will play if weather is sunny. Isthis statement is correct?
Q5 | Multinomial Naïve Bayes Classifier is    _                distribution
Q6 | For the given weather data, Calculate probabilityof not playing
Q7 | The minimum time complexity for training an SVM is O(n2). According to this fact, what sizesof datasets are not best suited for SVM’s?
Q8 | The effectiveness of an SVM depends upon:
Q9 | What do you mean by generalization error in terms of the SVM?
Q10 | We usually use feature normalization before using the Gaussian kernel in SVM. What is true about feature normalization? 1. We do feature normalization so that new feature will dominate other 2. Some times, feature normalization is not feasible in case of categorical variables3. Feature normalization always helps when we useGaussian kernel in SVM
Q11 | Support vectors are the data points that lieclosest to the decision surface.
Q12 | Which of the following is not supervisedlearning?
Q13 | Gaussian Naïve Bayes Classifier is    _                distribution
Q14 | If I am using all features of my dataset and I achieve 100% accuracy on my training set, but~70% on validation set, what should I look outfor?
Q15 | What is the purpose of performing cross- validation?
Q16 | Suppose you are using a Linear SVM classifier with 2 class classification problem. Now you have been given the following data in which some points are circled red that are representing support vectors.If you remove the following any one red points from the data. Does the decisionboundary will change?
Q17 | Linear SVMs have no hyperparameters that needto be set by cross-validation
Q18 | For the given weather data, what is theprobability that players will play if weather is sunny
Q19 | 100 people are at party. Given data gives information about how many wear pink or not, and if a man or not. Imagine a pink wearing guest leaves, what is the probability of being aman
Q20 | Problem: Players will play if weather is sunny. Is t
Q21 | For the given weather data, Calculate probability
Q22 | For the given weather data, Calculate probability
Q23 | For the given weather data, what is the probabilit
Q24 | 100 people are at party. Given data gives informa
Q25 | 100 people are at party. Given data gives informa