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What is/are true about ridge regression?
1. When lambda is 0, model works like linear regression model
2. When lambda is 0, model doesn't work like linear regression model
3. When lambda goes to infinity, we get very, very small coefficients approaching
04. When lambda goes to infinity, we get very, very large coefficients approaching infinity.
In a linear regression problem, we are using "R-squared" to measure goodness-of-fit. We add a feature in linear regression model and retrain the same model. Which of the following option is true?
The Weberian model of bureaucracy has been criticised from a variety of stand points.
Consider the following points in this regard:
1. The Weberian model appears to be a 'machine theory' and a closed system model taking little account of organisational interactions with the environment.
2. The structural features of bureaucracy as laid down by Weber might be suitable for routine and repetitive tasks but these would produce dysfunctional consequences in terms of human behaviour if the jobs would involve innovation and creativity.
3. Weberian model has not been found to respond adequately to the tasks of socioeconomic transformation in the developing countries. These tasks require quick changes whereas the capacity of the Weberian model for adaptation to change is rather slow.
4. Weber's model is confined to the study of formal bureaucratic structure and fails to take notice of the informal structure of bureauracy such as informal relations, informal norms and values, an informal power hierarchy and informal power struggles.
Which of the above points make a valid criticism of the Weberian model? Select the correct answer from below:
The least squares estimate for the coefficient of a multivariate regression model is exactly regression through the origin with the linear relationships.
The plum pudding ( first model )of the atom was proposed by JJ Thomson. Which of the following is/are the limitations of the Thomson model (i) The model wasn't able to explain the alpha scattering (ii) The model failed to explain the position of nuclei  (iii) The model failed to explain how the positive charges hold the negative charges (iv)The model failed to explain the existence of electrons in an atom 
User level threads are threads that are visible to the programmer and are unknown to the kernel. The operating system kernel supports and manages kernel level threads. Three different types of models relate user and kernel level threads. Which of the following statements is/are true ? (a) (i) The Many - to - one model maps many user threads to one kernel thread (ii) The one - to - one model maps one user thread to one kernel thread (iii) The many - to - many model maps many user threads to smaller or equal kernel threads (b) (i) Many - to - one model maps many kernel threads to one user thread (ii) One - to - one model maps one kernel thread to one user thread (iii) Many - to- many model maps many kernel threads to smaller or equal user threads
Which of the following statements is false?  1. When value of correlation coefficient is one, the two regression lines coincide 2. The regression coefficients are independent of the change of origin and of scale 3. The sign of the regression coefficients are always the same 4. The square of the coefficient of correlation is called coefficient of determination 
Assertion (A): If regression coefficient of X on Y is greater than one, regression coefficient of Y on X must be less than one.
Reason (R): The geometric mean between two regression coefficients is the coefficient of correlation.
Assertion (A): If regression coefficient of X on Y is greater than one, regression coefficient of Y on X must be less than one.
Reason (R): The geometric mean between two regression coefficients is the co-efficient of correlation.
On the basis of the above, choose the appropriate answer :
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?