SUPERVISED LEARNING METHODS. Stanley Liang, PhD Candidate, Lassonde School of Engineering, York University Helix Science Engagement Programs 2018

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1 SUPERVISED LEARNING METHODS Stanley Liang, PhD Candidate, Lassonde School of Engineering, York University Helix Science Engagement Programs 2018

2 2 CHOICE OF ML You cannot know which algorithm will work best on your dataset at the beginning Spot-checking: use trial and error to discover a shortlist of algorithms that do well on your problem, and to double down on and tune later On your list: Try a mixture of algorithm representations (instances and trees) Try a mixture of learning algorithms (different algorithms for learning the same type of representation) Try a mixture of modeling types (linear and nonlinear functions or parametric, and nonparametric) Stanley Liang, Lassonde School of Engineering, York University, 2018

3 3 CLASSIFICATION Six classification algorithms from Python with Scikit-learn Linear machine learning algorithms(two) Logistic regression Linear Discriminant Analysis nonlinear machine learning algorithms (Four) k-nearest Neighbors (knn) Naive Bayes Classification and Regression Trees Support Vector Machines Stanley Liang, Lassonde School of Engineering, York University

4 Logistic regression assumes a Gaussian distribution for the numeric input variables To model binary classification problems Linear Discriminant Analysis for binary and multiclass classification assumes a Gaussian distribution for the numerical input variables LINEAR MACHINE LEARNING 4 Stanley Liang, Lassonde School of Engineering, York University

5 NONLINEAR MACHINE LEARNING k-nearest Neighbors algorithm (KNN) uses a distance metric to find the k most similar instances in the training data for a new instance takes the mean outcome of the neighbors as the prediction Naive Bayes calculates the probability of each class and the conditional probability of each class given each input value assumes that all features are independent Assumes Gaussian distribution 5 Stanley Liang, Lassonde School of Engineering, York Univerrsity

6 NONLINEAR MACHINE LEARNING Classification and Regression Trees (CART or DT for decision trees) construct a binary tree from the training data Choose split points by a greedy algorithm by evaluating each attribute and each value of each attribute in the training data to minimize the cost function Support Vector Machines (SVM) seek a line that best separates two classes data instances that are closest to the line that best separates the classes are called support vectors influencing where the line is placed SVM is extended to multi-class Can use of different kernel functions via the kernel parameters SVM is sensitive to attribute distribution, data preprocessing usually helps to improve SVM Stanley Liang, Lassonde School of Engineering, York Univerrsity 6

7 7 REGRESSION Six regression algorithms from Python with Scikit-learn Linear machine learning algorithms(four) Linear regression Ridge regression LASSO linear regression Elastic net regression nonlinear machine learning algorithms (Three) k-nearest Neighbors (knn) Classification and Regression Trees Support Vector Machines Stanley Liang, Lassonde School of Engineering, York University

8 Linear Regression Assumes that the input variables have a Gaussian distribution Assumes that input variables are relevant to the output variable Assumes that the attributes are not highly correlated with each other (collinearity) Ridge Regression an extension of linear regression the loss function is modified to minimize the complexity of the model by L2-norm LINEAR MACHINE LEARNING 8 Stanley Liang, Lassonde School of Engineering, York University

9 9 LASSO linear regression Least Absolute Shrinkage and Selection Operator (LASSO) is a modification of linear regression like ridge regression LASSO minimizes the complexity of the model measured as the sum absolute value of the coefficient values or L1-norm Elastic Net Regression A form of regularization regression that combines the properties of both Ridge and LASSO minimize the complexity of the regression model (magnitude and # of regression coefficients) by penalizing both L2-norm and L1-norm LINEAR MACHINE LEARNING Stanley Liang, Lassonde School of Engineering, York Univerrsity

10 10 NONLINEAR MACHINE LEARNING k-nearest Neighbors algorithm (KNN) KNN locates the k most similar instances in the training dataset for a new data instance From the k neighbors, a mean or median output variable is taken as the prediction The Minkowski distance is used by default Stanley Liang, Lassonde School of Engineering, York Univerrsity

11 NONLINEAR MACHINE LEARNING Classification and Regression Trees (Decision Tree, DT) construct a binary tree from the training data Choose split points with a greedy algorithm by evaluating each attribute and each value of each attribute in the training data to minimize a cost function The default cost metric for regression decision trees is the mean squared error 11 Stanley Liang, Lassonde School of Engineering, York Univerrsity

12 NONLINEAR MACHINE LEARNING Support Vector Machines (SVM) SVM for regression is called Support Vector Regression (SVR) the use of different kernel functions via the kernel parameter RBF (radial basis function) kernel Polynomial function Sigmoid function SVM is sensitive to attribute distribution 12 Stanley Liang, Lassonde School of Engineering, York Univerrsity

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