ML Part10: Supervised Machine Learning: Classification

Classification refers to a type of supervised learning where the goal is to predict the category or class label of new observations based on past observations with known class labels. It involves training a model on a labelled dataset and then using this model to classify new data points.

Algorithms: Common algorithms used for classification include:

• Logistic Regression
• Decision Trees
• Random Forests
• Support Vector Machines (SVM)
• Neural Networks
• k-Nearest Neighbors (k-NN)
• Naive Bayes

Logistic regression

Logistic regression is a statistical method used for binary classification problems, where the outcome or dependent variable is dichotomous, meaning it can take on one of two possible values, often labelled as 0 and 1. Despite its name, logistic regression is used for classification rather than regression.

Linear regression vs Logistic regression

Linear regression vs Logistic regression

Key Concepts of Logistic Regression

1. Binary Classification: Logistic regression is used to classify data points into one of two classes. For…