ISYE 6501 Midterm 2 Questions with All Correct Answers

ISYE 6501 Midterm 2 Questions with All Correct Answers Rows - ANSWER Data points are values in data tables Columns - ANSWER The 'answer' for each data point (response/outcome) Structured Data - ANSWER Quantitative, Categorical, Binary, Unrelated, Time Series Unstructured Data - ANSWER Text Support Vector Model - ANSWER Supervised machine learning algorithm used for both classification and regression challenges. Mostly used in classification problems by plotting each data item as a point in n-dimensional space (n is the number of features you have) with the value of each feature being the value of a particular coordinate. Then you classify by finding a hyperplane that differentiates the 2 classes very well. Support vectors are simply the coordinates of individual observation -- it best segregates the two classes (hyperplane / line). What do you want to find with a SVM model? - ANSWER Find values of a0, a1,...,up to am that classifies the points correctly and has the maximum gap or margin between the parallel lines. What should the sum of the green points in a SVM model be? - ANSWER The sum of green points should be greater than or equal to 1 What should the sum of the red points in a SVM model be? - ANSWER The sum of red points should be less than or equal to -1 What should the total sum of green and red points be? - ANSWER The total sum of all green and red points should be equal to or greater than 1 because yj is 1 for green and -1 for red. First principal component - ANSWER PCA -- a linear combination of original predictor variables which captures the maximum variance in the data set. It determines the direction of highest variability in the data. Larger the variability captured in first component, larger the information captured by component. No other component can have variability higher than first principal component. it minimizes the sum of squared distance between a data point and the line. Second principal component - ANSWER PCA -- also a linear combination of original predictors which captures the remaining variance in the data set and is uncorrelated with Z¹. In other words, the correlation between first and second component should is zero. What if it's not possible to separate green and red points in a SVM model? - ANSWER Utilize a soft classifier -- In a soft classification context, we might add an extra multiplier for each type of error with a larger penalty, the less we want to accept mis-classifying that type of point. Soft Classifier - ANSWER Account for errors in SVM classification. Trading off minimizing errors we make and maximizing the margin. To trade off between them, we pick a lambda value and minimize a combination of error and margin. As lambda gets large, this term gets large. The importance of a large margin outweighs avoiding mistakes and classifying known data points. Should you scale your data in a SVM model? - ANSWER Yes, so the orders of magnitude are approximately the same. Data must be in bounded range. Common scaling: data between 0 and 1 a. Scale factor by factor b. Linearly How should you find which coefficients to hold value in a SVM model? - ANSWER If there is a coefficient who's value is very close to 0, means the corresponding attribute is probably not relevant for classification. Does SVM work the same for multiple dimensions? - ANSWER Yes Does a SVM classifier need to be a straight line? - ANSWER No, SVM can be generalized using kernel methods that allow for nonlinear classifiers. Software has a kernel SVM function that you can use to solve for both linear and nonlinear classifiers. Can classification questions be answered as probabilities in SVM? - ANSWER Yes. K Nearest Neighbor Algorithm - ANSWER Find the class of the new point, Pick the k closest points to the new one, the new points class is the most common amongst the k neighbors. What should you do about varying level of importance across attributes with K Nearest Neighbors? - ANSWER Some attributes might be more important than others to the classification --- can deal with this by weighting each dimension's distance differently. Unimportant attributes may be removed as they are not very important for the classification.