Logistic Regression - Machine Learning with Python - IBM AI Engineering certificate program on Coursera

 

Intro to Logistic Regression

https://www.coursera.org/learn/machine-learning-with-python/lecture/eySE4/intro-to-logistic-regression

• Logistic Regression is a statistical and Machine Learning algorithm to classify data in the dataset.
• it is similar to linear regression, but it takes categorical (discrete) target fields instead the numeric continuous values.
• The classification can b binary (i.e. yes, no), or multi-class.
• Logistic Regression gives the probability of a given class
• The independent variables (features, x) should be continuous (i.e 0.0 .. 1.0)
• The dependent variable (the label, y) should be categorical (i.e. TRUE, FALSE, MAYBE)

What are the applications of Logistic Regression?

• a chance of mortality
• a likelihood of propensity to purchase a product
• a probability for the failure of a process or a product
• a likelihood of default on a loan 

When should I use Logistic Regression?

• If the data is binary (i.e. 0/1, Yes/No, True, False)
• If you need probabilistic results
• When you need a decision boundary (linear, polynomial, or more complex hyperplane)
• if you need to understand the impact of the features