QMB3302 Final Exam Questions And Answers With Verified Solutions Graded A+
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QMB3302
According to the documentation, a silhouette scores of 1 is _____, and -1 is _____. - the best
score; the worst score
All the nodes prior to the output nodes essentially 'guess' at the correct weights. The algorithm checks to
see if the initial guess is correct (usually not). When it is wrong......
QMB3302 Final Exam According to the documentation, a silhouette scores of 1 is _____, and -1 is _____. - the best score; the worst score All the nodes prior to the output nodes essentially 'guess' at the correct weights. The algorithm checks to see if the initial guess is correct (usually not). When it is wrong... - ... it tries again (runs another epoch) An example this week was done in Jupiter like environment called Google Collab. What was the language that was demonstrated in the videos? - TensorFlow Choosing a class of models Your data set consists of details about customer traits, such as "number of items in the basket at checkout". Your task is to group customers that are like each other together. You don't already have labeled customer types. What kind of model are you build ing? - unsupervised model (such as K means) - reminder: if you have a bunch of Xs, but no Ys the problem is unsupervised; When you are building a supervised model, you have an X and a Y . The hints there is "you don't already have labeled customer types " without these labels, the Y , you can't have a ny supervision Decision trees have a few problems. The problem we talked about the most is: - overfitting Each of the connections between noes as a connection, each of those connections has a _____. - Activation function Imagine X in the below is a missing value. If I were to run a median imputer on this set of data, what would the return value be? 50, 60, 70, 80, 100, 60, 5000, X - 70 Imagine you have a dataset with 2 columns, both filled with continuous numbers. You believe the first column is a predictor of the second column. Which of the model approaches below could work when building a model? - Regression - Decision Tree - Running .describe and .info on the data - Graphing - Random Forest - - Regression - Decision Tree - Random Forest In class we walked through 5 steps to building a machine learning model. The textbook goes over in some depth the 5 steps. What are they? - First Step: choosing a class of model Second Step: choosing hyperparameters Third Step: arrange data Fourth Step: fit the model Fifth Step: predict In kmeans - the algorithm has multiple iterations. If we have a simple 2d problem, and a k = 2, it begins by assigning the first centroids to _____, and then _____ of each point or record to the centroid. - a random initial starting point; measuring the distance In y = ax + b A is commonly known as _____ and B is commonly known as _____. - slope; intercept Models, such as the random forest model we ran, often have a number of parameters that the analyst can choose or set. What is a the best source of up to date information about the different parameters that can be set? - The scikit learn documentation
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