Your boss at a software company gives you a binary classifier (i. e., a classifier with only two possible output values) that predicts, for any basketball game, whether the home team will win or not. this classifier has a 28% accuracy, and your boss assigns you the task of improving that classifier, so that you get an accuracy that is better than 60%.
How do you achieve that task? Can you guarantee achieving better than 60% accuracy?

Using least squares fitting, you are to fit the data sets to the following models and solve for the parameters ai , where i is the index of the parameter. the input/output data for the systems are linked in the bblearn site. for each of the systems use matlab to plot the supplied data vs. the model fit on one plot. include your code in the solutions. (a) linear fit "lineardata. mat" y=a1x^3 + a2x^2 + a3x + a4 (b) plant fit "plantdata. mat g(s) = a1/(s + a2)

Im doing this last minute and literally none of my neighbors or people that my dad works with use excel so if anyone could me make up an example

The place where the extended axis of the earth would touch the celestial sphere is called the celestial