Introduction to Logistic Regression Algorithm
Greetings.
I am a machine learning engineer based in India, possessing a sustained interest in machine learning since my undergraduate studies. I have completed Stanford University's machine learning course (Andrew Ng) via Coursera, and IBM's machine learning and deep learning curriculum. My current focus is on machine learning and data science projects, aiming to leverage my expertise for impactful, real-world problem-solving.
In Logistic Regression, does not mean that it is a regression algorithm.
Logistic Regression extends linear regression with a logistic function to make it suitable for classification.
It is arguably the simplest machine learning algorithm for classification tasks.
It is one of the most powerful machine learning algorithms that can be used for binary classification problems such as classifying emails or SMS as spam or not spam.
Assumption of Logistic Regression
- Below are the assumptions of the logistic regression algorithm that you should know:
It assumes that there is an appropriate structure of the output label.
All observations are independent of each other.
There is little or no multicollinearity in the dataset.
It also assumes that the dataset consists of a very large sample of the population.
