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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?
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 :
Generally, which of the following method(s) is used for predicting continuous dependent variable?
1. Linear Regression
2. Logistic Regression
The least squares estimate for the coefficient of a multivariate regression model is exactly regression through the origin with the linear relationships.
The cost function for logistic regression and linear regression are the same.
Consider a discrete-time system which satisfies the additivity property, i.e., if the output for u1[n] is y1[n] and that for u2[n] is y2[n], then output for u1[n] + u2[n] is y1[n] + y2[n]. Such a system is : 1. Linear 2. Non-linear 3. Sometimes Linear and Sometimes non-linear
The Coordinator of an International Virtual Conference has planned six guest lectures on different areas-Cryptography, Fluid Dynamics, Big Data Analysis, Wavelet Theory, Differential Equations and Linear Algebra-to be organised only one on each day from Monday to Sunday.  Wavelet Theory should be organised immediately after Differential Equations.  Big Data Analysis should be organised on Wednesday and should not be followed by Linear Algebra.  Fluid Dynamics should be organised on Friday and there should be a gap of two days between Cryptography and Linear Algebra.  One day there will be no lecture (Saturday is not that day), and just before that day Linear Algebra will be organised. How many lectures are organised between Big Data Analysis and Wavelet Theory?