QMB3302 Final Questions and Answers 100% Verified

QMB3302 Final .head() returns - the first five sets of the dataframe 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 advantages of linear regression models - easy to implement, interpret, and train can reduce overfitting with cross validation extrapolation beyond particular data set are naive bayes suitable for high or low dimension databases? - high bagging - makes use of an ensemble (a grab bag, perhaps) of parallel estimators, each of which over -fits the data, and averages the results to find a better classification behavior of validation curves depend on two things - model complexity and number of training points best source of info on parameters that can be used in models (eg random forest) - the scikit learning documentation centroids - points dropped around the numbers in order to measure a distance. points are assigned to the cluster, and the mean of those points create a new centroid classification in machine learning - a type of supervised learning where the goal is to assign input data points to predefined categories or classes classification problems - approximates a mapping function from input variables to identify discrete output variables, which can be labels or categories decision tree random forest K means neural networks cluster centers are - number of clusters chosen randomly to begin clustering cluster centers are updated - iteratively to minimize the within cluster sum of squares cluster regression - improves accuracy of linear regression by splitting training space into subspaces (K) Clustering Algorithms - to segment customers into different clusters to be able to target specific promotions to them clustering algos do what - measure distance bw observations does not necessarily mean two values or dimensions coefficient of determination - r^2 common uses for random forest algorithms - classification and regression