Using Linear Regression, all predictions >= 0.5 can be considered as 1 and rest all < 0.5 can be considered as 0. But then the question arises why classification can’t be performed using it?
Suppose we are classifying a mail as spam or not spam and our output is y, it can be 0(spam) or 1(not spam). In case of Linear Regression, hθ(x) can be > 1 or < 0. Although our prediction should be in between 0 and 1, the model will predict value out of the range i.e. maybe > 1 or < 0.
So, that’s why for a Classification task, Logistic/Sigmoid Regression plays its role.
Here, we plug θTx into logistic function where θ are the weights/parameters and x is the input and hθ(x) is the hypothesis function. g() is the sigmoid function.
It means that y = 1 probability when x is parameterized to θ
To get the discrete values 0 or 1 for classification, discrete boundaries are defined. The hypothesis function cab be translated as
Decision Boundary is the line that distinguishes the area where y=0 and where y=1. These decision boundaries result from the hypothesis function under consideration.
Understanding Decision Boundary with an example –
Let our hypothesis function be
Then the decision boundary looks like
Let out weights or parameters be –
So, it predicts y = 1 if
And that is the equation of a circle with radius = 1 and origin as the center. This is the Decision Boundary for our defined hypothesis.
- ML | Logistic Regression v/s Decision Tree Classification
- ML | Logistic Regression using Python
- Understanding Logistic Regression
- ML | Logistic Regression using Tensorflow
- ML | Cost function in Logistic Regression
- Identifying handwritten digits using Logistic Regression in PyTorch
- ML | Kaggle Breast Cancer Wisconsin Diagnosis using Logistic Regression
- ML | Classification vs Regression
- Regression and Classification | Supervised Machine Learning
- Getting started with Classification
- ML | Classification vs Clustering
- ML | Using SVM to perform classification on a non-linear dataset
- Python | Image Classification using keras
- Multiclass classification using scikit-learn
- ML | Cancer cell classification using Scikit-learn
If you like GeeksforGeeks and would like to contribute, you can also write an article using contribute.geeksforgeeks.org or mail your article to email@example.com. See your article appearing on the GeeksforGeeks main page and help other Geeks.
Please Improve this article if you find anything incorrect by clicking on the "Improve Article" button below.