Problem 6 Create a function called best model that takes as argument a list called x...

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Problem 6 Create a function called best model that takes as argument a list called x that contains numbers, a list called y that also contains numbers, and a list called all models that contains lists. Each list in all models contains two numbers that represent a [mb] combination. Each (m.b] combination represents a set of parameters that define a different linear model. For example, if all_models=[[4.4,5],[1.5,3]], then each list represents a set of parameters that define the linear models: Model 1: ; = 4.4 x ; +5 Model 2: = 1.5 x ; +3 The function best model must compute the predictions and the SSE of each model based on the model parameters and the values in the x and y lists. Finally, the function must return the model parameters that result in the minimum SSE (Le, the best model). Note that you can use the functions defined in Problems 4 and 5 within best model. [ ] Hint: Run this code and think what it means: all models [[3,-1], [2,0.25], [2.5, 0.1]] print (all_models[0]) print(all_models[1]) print(all models[2]) [] #create best model function here: def best _model(): [] #TEST CASE FOR EX 6 ### Run the following lines. # If you get "Correct" your function is correct. x = [1,3,5] y = [2.5, 6.5, 10.5] all_models = [[3, -1], [2,0.25], [2.5, 0.1]] result = []# use your function here; delete the empty list if result == [2, 0.25]: print("Correct!") print("Incorrect :(") else: Problem 6 Create a function called best model that takes as argument a list called x that contains numbers, a list called y that also contains numbers, and a list called all models that contains lists. Each list in all models contains two numbers that represent a [mb] combination. Each (m.b] combination represents a set of parameters that define a different linear model. For example, if all_models=[[4.4,5],[1.5,3]], then each list represents a set of parameters that define the linear models: Model 1: ; = 4.4 x ; +5 Model 2: = 1.5 x ; +3 The function best model must compute the predictions and the SSE of each model based on the model parameters and the values in the x and y lists. Finally, the function must return the model parameters that result in the minimum SSE (Le, the best model). Note that you can use the functions defined in Problems 4 and 5 within best model. [ ] Hint: Run this code and think what it means: all models [[3,-1], [2,0.25], [2.5, 0.1]] print (all_models[0]) print(all_models[1]) print(all models[2]) [] #create best model function here: def best _model(): [] #TEST CASE FOR EX 6 ### Run the following lines. # If you get "Correct" your function is correct. x = [1,3,5] y = [2.5, 6.5, 10.5] all_models = [[3, -1], [2,0.25], [2.5, 0.1]] result = []# use your function here; delete the empty list if result == [2, 0.25]: print("Correct!") print("Incorrect :(") else:

Answer rating: 100% (QA)

Code to copy def bestmodelxyallmodels get the sum of squared e... View the full answer