QMB3302 UF Fall Final Exam Updated 2024/2025 Actual Questions and answers with complete solutions
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Course
QMB3302 UF
Institution
QMB3302 UF
5 steps to building a machine learning model - 1. choosing a class of model
2. choose hyperparameters
3. arrange data
4. fit the model
5. predict
a silhouette score of 1 is the [best/worst] and -1 is the [best/worst] score - best, worst
basic idea of regression - we have some X values called ...
QMB3302 UF Fall Final Exam 5 steps to building a machine learning model - 1. choosing a class of model 2. choose hyperparameters 3. arrange data 4. fit the model 5. predict a silhouette score of 1 is the [best/worst] and -1 is the [best/worst] score - best, worst basic idea of regression - we have some X values called features and some Y value, the variable we are trying to predict Difference between unsupervised and supervised learning - unsupervised: you have an X but no Y supervised: you have an X and a Y 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? 1. random forest 2. running .describe and .info on the data 3. regression 4. decision trees - regression (obvious choice), random forest, decision trees (not the best) 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 measuring the ____ of each point or record to the centroid. - a random initial starting point; distance models often have a number of parameters that the analyst can choose or set, what is the best source of up to date info about the different ones that can be set? - the scikit learn documentation
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