Ans :- Logistic Regression often referred as logit model is a technique to predict the binary outcome from a linear combination of predictor variables. For example, if you want to predict whether a particular political leader will win the election or not. In this case, the outcome of prediction is binary i.e. 0 or 1 (Win/Lose). The predictor variables here would be the amount of money spent for election campaigning of a particular candidate, the amount of time spent in campaigning, etc.
Ans :- Classification Models are employed when the observations have to be classified in categories and not predicted. Examples being Cancerous and Non-cancerous tumor (2 categories), Bus, Rail, Car, Carpool (>2 categories)
Ans:- a. 1, b. 2, c. 30, d. None . Correct Answer is b which is 2. Output of Linear Regression is in the form of equation of straight line which requires atleast 2 observations.
Ans:- First of all, the sign (+ve,-ve) indicates the impact of the input variable on the output mode. In this case, if there is a unit increase in the input variable i.e., cost.car, the Log(Carpool/Car) decreases by 0.65123
Ans :-L2 regularization tends to spread error among all the terms, while L1 is more binary/sparse, with many variables either being assigned a 1 or 0 in weighting. L1 corresponds to setting a Laplacean prior on the terms, while L2 corresponds to a Gaussian prior.
