ML(17ISDE651) UNIT 2: REGRESSION AND CLASSIFICATION METHODS SHRUTHISHREE S.H |ASST.PROF|DEPT.OF ISE | JAIN UNIVERSITY Page 1 Regression and classification methods UNIT – II Regression and classification methods : 2.1 Support vector machine. 2.2 Discriminate analysis. 2.3 Naïve Bayas. 2.4 K-nearest neighbor. 2.5 Euclidean Distance and Manhattan Distances. 2.6 Neural network. 2.7 Intelligent learning algorithm. 2.7.1 Linear Regression. 2.7.2 Non-liner Regression. 2.8 Ensemble methods. 2.9 Decision tree.
ML(17ISDE651) UNIT 2: REGRESSION AND CLASSIFICATION METHODS SHRUTHISHREE S.H |ASST.PROF|DEPT.OF ISE | JAIN UNIVERSITY Page 2 INTRODUCTION Regression and classification methods : Techniques of Supervised Machine Learning algorithms include linear and logistic regression , multi-class classification , Decision Trees and support vector machines . Supervised learning requires that the data used to train the algorithm is already labeled with correct answers. For example, a classification algorithm will learn to identify animals after being trained on a dataset of images that are properly labeled with the species of the animal and some identifying characteristics. Supervised learning problems can be further grouped into Regression and Classification problems. Both problems have as goal the construction of a succinct model that can predict the value of the dependent attribute from the attribute variables. The difference between the two tasks is the fact that the dependent attribute is numerical for regression and categorical for classification.
ML(17ISDE651) UNIT 2: REGRESSION AND CLASSIFICATION METHODS SHRUTHISHREE S.H |ASST.PROF|DEPT.OF ISE | JAIN UNIVERSITY Page 3 2.1 Support vector machine. Introduction to SVMs: In machine learning, support vector machines (SVMs, also support vector networks) are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis. A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data (supervised learning), the algorithm outputs an optimal hyperplane which categorizes new examples. SVM can be of two types: o Linear SVM: Linear SVM is used for linearly separable data, which means if a dataset can be classified into two classes by using a single straight line, then such data is termed as linearly separable data, and classifier is used called as Linear SVM classifier. o Non-linear SVM: Non-Linear SVM is used for non-linearly separated data, which means if a dataset cannot be classified by using a straight line, then such data is termed as non-linear data and classifier used is called as Non- linear SVM classifier.
ML(17ISDE651) UNIT 2: REGRESSION AND CLASSIFICATION METHODS SHRUTHISHREE S.H |ASST.PROF|DEPT.OF ISE | JAIN UNIVERSITY Page 4 Hyperplane and Support Vectors in the SVM algorithm: Hyperplane: There can be multiple lines/decision boundaries to segregate the classes in n-dimensional space, but we need to find out the best decision boundary that helps to classify the data points. This best boundary is known as the