QMB3302 Final UF Updated 2024/2025 Actual Questions and answers with complete solutions
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Course
QMB3302
Institution
QMB3302
According to the documentation, a silhouette score of -1 is - The worst score
According to the documentation, a silhouette scores of 1 ia - The best score
All the the nodes prior to the output nodes essentially 'guess' at the correct weights. Then the algorithm
checks to see if the initial guess...
QMB3302 Final UF According to the documentation, a silhouette score of -1 is - The worst score According to the documentation, a silhouette scores of 1 ia - The best score All the the nodes prior to the output nodes essentially 'guess' at the correct weights. Then 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 a Jupiter like environment called Google Collab. What was the language that was demonstrated in the videos? (One cool thing about this is that is looks just like any other package! Installing this on your own is tricky) - TensorFlow Decision tree's are nice because they are fairly simple and straightforward to interpret (True of False) - True Decision trees have a few problems, you should probably review those for the final exam! The problem we talked about the most is: - Overfitting Deep Neural Networks have only 1 hidden layer and multiple input layers. (True or False) - False Each of the connections between nodes as a connection, each of those connections has a ________ - Activation function from our possibly overly simplistic explanation) In the attempt to fit values from the input layer to the output layer, the hidden layer applies some weights to the input values. (True or False) - True Hierarchical clustering is more powerful than Kmeans, as it allows the researcher to determine the exact number of clusters to use in the analysis. (True or False) - False If you are not careful or do not see the __________________ appropriately, leads to decision trees overfitting - Max depth 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? - Random forests, regression, decision trees (Maybe not the BEST solution, Decision Trees have some problems like overfitting that we discussed. ) In class we walked through 5 steps to building a machine learning model. The textbook also goes over in some depth the 5 steps. What is step 1? - Choosing a class of model In class we walked through 5 steps to building a machine learning model. The textbook also goes over in some depth the 5 steps. What is step 2? - Choose hyperparameters In class we walked through 5 steps to building a machine learning model. The textbook also goes over in some depth the 5 steps. What is step 3? - Aarrange data In class we walked through 5 steps to building a machine learning model. The textbook also goes over in some depth the 5 steps. What is step 4? - Fit the model In class we walked through 5 steps to building a machine learning model. The textbook also goes over in some depth the 5 steps. What is step 5? - Predict In K Means clustering, the analyst does not need to determine the number of clusters (K), these are always derived analytically using the kmeans algorithm. (True or False) - False 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 - A random initial starting point
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