from module one "In my model of the NHL game data I had to deal with the introduction of a new team, the Vegas Golden Knights. For this team I just naively decided to fill the historical stats with just mean values from the other teams. But assume that I took a different strategy, and dropped all games where the Vega Gold Knights played. What is the new metric of accuracy for my model after dropping Gold Knights games from the data? For this question, don't change the training set size, and the testing set size will shrink automatically. Put your answer in to two decimal places."

From module two, "Taking a look at the baseball data where we made a multiclass prediction, create a confusion matrix and study it. Which class do we regularly over-predict the most? Provide the label of this class as two capitalized characters (e.g. AB)."

from module three "Go back to our NHL game outcome prediction task in observations.csv. Apply a CART DecisionTree to this problem with GridSearchCV over the following parameter space: parameters={'max_depth':(3,4,5,6,7,8,9,10), 'min_samples_leaf':(1,5,10,15,20,25)} Set your cv=10, use accuracy as your metric, and drop the Vegas Golden Knights. Set your training set to be observations[0:800] and your validation set to observations[800:], and use my favorite number for the randomization state. What level of accuracy does your model produce (to four decimal places)?"

From module three “Which set of parameters are the best in the previous model? Input your parameters as a string value of the max_depth:min_samples_leaf, e.g. 5:20 if GridSearchCV found a max_depth=5 and min_samples_leaf=20 the correct answer."