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- FILL IN THE BLANKS: Q. Regression testing is primarily related toHow can you recognize whether a model satisfies proportionality andadditivity?Model evaluation Create a predictions variable using your fitted model and the test dataset; call it y_pred. Then get the accuracy score of your predictions and save it in a variable called accuracy. Finally get the confusion matrix for your predictions and save it in a variable called confusion_mat. Code: y_pred = Noneaccuracy = Noneconfusion_mat = None
- If you have a training set with millions of features, which Linear Regression training procedure should you use?Explain how Logistic Regression works. Note: Please do it with your own words. ThankyouWhat is the type of method to create regression models in which the coefficients are penalized for being too large than what they should be if multicollinearity was not there? Answer Choices: a) Elastic Net b) Lasso c) Ridge d) Regularization
- Python Regression Model 1: train MSE = 0.423, test MSE = 0.978 Model 2: train MSE = 0.572, test MSE = 0.644 Model 3: train MSE = 0.218, test MSE = 1.103 Based on this information, which of these models generalises the best to unseen data?Theoretical Overview Suppose we have a set of data consisting of ordered pairs and we suspect the x and y coordinates are related. It is natural to try to find the best line that fits the data points. If we can find this line, then we can use it to make all sorts of other predictions. In this project, we're going to use several functions to find this line using a technique called least squares regression. The result will be what we call the least squares regression line (or LSRL for short). In order to do this, you'll need to program a statistical computation called the correlation coefficient, denoted by r in statistical symbols: NOTE: Equation is written assuming you start at the value 1. Lists start at index 0. Once you have the correlation coefficient, you use it along with the sample means and sample standard deviations of the x and y-coordinates to compute the slope and y-intercept of your regression line via these formulas: Tasks: In this project, you must read…Write the objective function that can be used to determine the regression model parameters. How is this objective function will be used to find model parameters?
- The only way to tell if a model is right or wrong is to see how well it works on test data. tell me more; give me more information? Explain?Python: Which of the following is true about the Regression Line? It provides the estimation of the value of a dependent variable for a given value of the independent variable(s) It provides the actual value of a dependent variable for a given value of the independent variable(s) It's just a straight line drawn through the data points between X and Y axis. It indicates a linear relationship between dependent variable and independent variable(s) It's an estimation of a linear relationship between dependent and independent variables by assuming the minimum error termPython regression b) When adding more variables to a linear model, what is true about the R-squared value? c) Adding more variables always leads to a lower test MSE.FalseTrue