If you have a training set with millions of features, which Linear Regression training procedure should you use?
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If you have a training set with millions of features, which Linear Regression training procedure should you use?
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- Which Linear Regression training procedure should you employ if you have a training set with millions of features?Which Linear Regression training algorithm can you use if you have atraining set with millions of features?Why do we use z-scores to evaluate a classifier? Why do we use feature weighting?
- How do you analyse the performance of the predictions generated by regression models versus classification models?What are some creative ways to use binary variables in model formulation?The hyper-parameters of a model must NOT be tuned on the test data ( i.e, the data used to evaluate the performance of the final model after selecting the hyper-parameters) Group of answer choices True False
- What exactly is the point of separating the data into a training set and a testing set? It is not obvious what the goal of the training set is. What exactly is the validation set supposed to accomplish?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?
- Why is it useful to leave certain variables out of the model altogether?What sort of regression-testing strategy could you establish before undertaking the process of refactoring on the simulation? Is this something you could conveniently automate?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