QMB3302 UF Fall '22 Final 2025-2026 most recent version Comprehensive 75 questions and verified answers accurate solutions Already graded A+ Get it 100% correct
5 steps to building a machine learning model - CORRECT ANSWER: 1. choosing a
class of model
- choose hyperparameters
- arrange data
- fit the model
- predict
a silhouette score of 1 is the [best/worst] and -1 is the [best/worst] score - CORRECT
ANSWER: best, worst
basic idea of regression - CORRECT ANSWER: we have some X values called features and some Y value, the variable we are trying to predict
Difference between unsupervised and supervised learning - CORRECT ANSWER:
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?
- random forest
- running .describe and .info on the data
- regression 1 / 2
4. decision trees - CORRECT ANSWER: 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. - CORRECT ANSWER: 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? - CORRECT
ANSWER: the scikit learn documentation
most talked about problem with decision trees - CORRECT ANSWER: overfitting
NLP - CORRECT ANSWER: natural language processing
one of the problems with decision trees is that they are prone to _____ if you are not careful or do not set the _____ appropriately - CORRECT ANSWER: overfitting, max depth
random forests are [more/less/just as] interpretable than decision trees - CORRECT
ANSWER: less
the first variable in a decision tree (before any of the branches) - CORRECT ANSWER: root
The LinearRegression estimator is only capable of simple straight line fits: true or false?
- CORRECT ANSWER: false
the terminal node - CORRECT ANSWER: the last node (sometimes called a leaf), the tree doesn't split after this
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