Describe Lasso Regularization which can be used for variable selection in Linear Regression.

Analysts at a start-up company are analyzing 35 months of sales data. They partition the data (the first 20 observations are assigned to the training set; the most recent 15 months are in the test set). The only independent variable is T (month number, ranging from 1 to 35). Five models (polynomials of order 1 - 5) are fit to the data. The first order is just the linear model; the 2nd order polynomial is the quadratic model; order 3 is the cubic model, etc. In each case the model is fit on the training data, and scored on both the training and test data sets. The results are below. Based on this output, which is the best predictive model? Metrics AE RMSE MAE SSE Metrics AE RMSE MAE SSE Model 2 Model 3 Model 5 Model 4 Model 1 1 <0.000001 0.955978 0.792802 18.277907 1 -1.034550 1.424155 1.208991 30.423248 Training Data Scoring Models (Polynomial of order 1-5) 4 2 <0.000001 0.928791 0.759583 17.253086 3 <0.000001 <0.000001 0.928295 0.855568 0.761951 0.652212 17.234646 14.639962 Test Data…

A Ridge Linear Regression adds the sum of the squared values of the coefficients to the loss function to penalize large coefficients.   Group of answer choices True False

In the simple linear regression equation ŷ = bo + b₁x, how is b₁ interpreted? it is the change in that occurs with a one-unit change in y O It is the estimated value of ŷ when x = 0 O It is the change in ŷ that occurs when bo increases O it is the change in ŷ that occurs with a one-unit change in

How do you construct a good and bad match table with Boyer Moore?   What do the tables mean and can you please provide an example.

QUESTION 10 Which of the following are true about locally weighted regression? Hint: See https://en.wikipedia.org/wiki/Local_regression A low level polynomial is used for local fitting Generally, it provides lower error that global model Closer points are given more emphasis It is an example of weighted regression where weight is the distance between points.

We estimate the following LRM for the relationship between COURSE_EVAL and AGE of the instructor: E(COURSE_EVAL | AGE) = 4.774 -0.2154 * log(AGE) where the AGE variable  is transformed with the logarithmic function. Provide an interpretation of the coefficient estimate -0.2154.

Q.1 Explain the different regularization techniques used with Linear regression to handle multivariate data.

We want to create an extra feature for the following data with only one feature (x) to use in a multiple linear regression model. What feature should we add? 6.0 V 5.5 50 40 35 30 -1.00 Linear Cubic Quadratic Logarithmic -0.50 -0.25 0:00 0.25 0.50 0.75 1.00