AI & ML - Unit3.pdf

TE7704 AI & ML UNIT 3 Supervised Learning 1 Multiple Linear Regression 2 3 Multiple Linear Regression Problem Statement :- https://github.com/ketanp23/multilinearreg Data of 50 companies. R&D Spends/Administration/Marketing Spend/State Pr...

TE7704 AI & ML UNIT 3 Supervised Learning 1 Multiple Linear Regression 2 3 Multiple Linear Regression Problem Statement :- https://github.com/ketanp23/multilinearreg Data of 50 companies. R&D Spends/Administration/Marketing Spend/State Profit (Independent Variables) (Dependent Variable) A VC Fund is interested in investing in these companies. But has questions like :- Where companies perform better? Are these companies are those who spend more money on R&D Spend or on Marketing Spend ? Help VC Fund to build a model 4 Dummy Variable/Categorical Variable/One hot encoding 5 Dummy Variable Trap Not Truly Independent Variables Multicollinearity = High correlation between 2 or more independent variables 6 Dummy Variable Trap 7 P Value = How likely it is to get a particular result when the null hypothesis is assumed to be true. 8  Logistic Regression Unlike regression where you predict a continuous number, you use classification to predict a category. There is a wide variety of classification applications from medicine to marketing 9  65% 35-65% 35% 10 11 12 https://github.com/ketanp23/logisticreg Support Vector Machine 14  Support Vectors Support vectors are the data points nearest to the hyperplane, the points of a data set that, if removed, would alter the position of the dividing hyperplane. Because of this, they can be considered the critical elements of a data set. What is a hyperplane? As a simple example, for a classification task with only two features (like the image above), you can think of a hyperplane as a line that linearly separates and classifies a set of data. Intuitively, the further from the hyperplane our data points lie, the more confident we are that they have been correctly classified. We therefore want our data points to be as far away from the hyperplane as possible, while still being on the correct side of it. So when new testing data is added, whatever side of the hyperplane it lands will decide the class that we assign to it. Decision Trees 18 Decision Tree Analysis is a general, predictive modelling tool that has applications spanning a number of different areas. In general, decision trees are constructed via an algorithmic approach that identifies ways to split a data set based on different conditions. It is one of the most widely used and practical methods for supervised learning. Decision Trees are a supervised learning method used for both classification and regression tasks. The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features. The decision rules are generally in form of if-then-else statements. The deeper the tree, the more complex the rules and fitter the model.  Artificial Neural Networks (Perceptrons, Multilayer networks, back-propagation) 22 MultiLayer Networks & Backpropagation 26 Activation Functions 31 32 33 34 35 Predicting Price of Property 37 38 39 40 41 42 43