Logistic Regression from Scratch

Logistic regression is a machine learning algorithm for classification. In this algorithm, the probabilities describing the possible outcomes of a single trial are modeled using a logistic function. Logistic regression makes a binary classification prediction based on the sigmoid function with n input features. The β[i] are coefficients that can be determined from a stochastic gradient descent or other optimization on the training dataset. The desired classification is either a 0 or 1, so the probability calculated with the sigmoid function is rounded to the nearest value. 0:00 Logistic Function 1:48 Crescent Example 3:40 Number Classification 5:17 LR from Scratch 7:15 Train test split 8:41 Predict Function 11:11 Update Coefficients 18:52 Plot Loss Function 19:47 Test Evaluation 22:40 Any Number of Inputs 32:45 Easy or Hard Classification 33:55 Cluster Standard Deviation 35:15 ML for Engineers Course ML for Engineers: https://apmonitor.com/pds Logistic Regression: https://apmonitor.com/pds/index.php/Main/LogisticRegression