Introduction to Machine Learning

  Multiclass logistic regression Instead of  y = 0 , 1  we will expand our definition so that  y = 0 , 1... n . Basically we re-run binary classification multiple times, once for each class. Procedure 1.     Divide the problem into n+1 binary classification problems (+1 because the index starts at 0?). 2.      For each class… 3.      Predict the probability the observations are in that single class. 4.      prediction = <math>max(probability of the classes) For each sub-problem, we select one class (YES) and lump all the others into a second class (NO). Then we take the class with the highest predicted value. Softmax activation The softmax function (softargmax or normalized exponential function) is a function that takes as input a vector of K real numbers, and normalizes it into a probability distribution consisting of K probabilities proportional to the exponentials of...

  What is Logistic Regression? Logistic regression is Regression as well as Classification machine learning algorithm that comes under Supervised Learning techniques. Logistic regression is used to predict the categorical dependent variable with the help of independent variables. Logistic regression converts its output using the logistic sigmoid function to return a probability value which can then be mapped to two or more discrete classes. Comparison to linear regression Logistic Regression Linear Regression Solves the classification problems Solves the regression problems mostly Predict the categorical dependent variables using set of independent variables Predict the continuous dependent variables using set of independent variables To estimate the accuracy, Maximum likelihood estimation method is used To estimate the accuracy, Least square estimation method is used We get Ca...