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MCQs

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What is the Cargo package manager?

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Who was the first chairman of Indian space agency ISRO?

Indian Space Research Organization (ISRO)

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What is the value of Avogadro’s number?

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What are Photoelectrons?

The photoelectric effect is the emission of electrons when electromagnetic radiation, such as light, hits a material. Electrons emitted in this manner are called photoelectrons.

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When was Einstein born?

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Who proposed the concept of Wave-Particle Duality?

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Who proposed the concept of Bose-Einstein Condensate?

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Who proposed the theory of General Theory of Relativity?

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Who proposed the theory of Photoelectric Effect?

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Who invented the concept of Avogadro’s Number?

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Who proposed the Special Theory of Relativity?

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Who invented the concept of Brownian Movement?

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In which year Einstein was awarded the Nobel prize?

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What is regression? (Machine Learning)

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What is Classification in Machine Learning?

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Gaussian Naïve Bayes Classifier is ___________distribution

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We usually 1use 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 variables 3. Feature normalization always helps when we use Gaussian kernel in SVM

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Suppose you have trained an SVM with linear decision boundary after training SVM, you correctly infer that your SVM model is under fitting.Which of the following option would you more likely to consider iterating SVM next time?

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Computers & Internet

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 variables 2. I will start introducing polynomial degree variables 3. I will remove some variables

Computers & Internet

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 of training data?

Computers & Internet

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 mean training error?

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