Logistic Regression(Machine Learning)

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Logistic Regression is a Supervised Learning algorithm, used for classification. It is used to predict probability of Target Variable. It produces results in binary format. The outcome is discrete/categorical such as:

• Yes/No.
• 0/1.
• True/False.
• High/Low.

etc…

What it does?

It uses “Sigmoid Function” to give the outcomes. Just like the sigmoid curve, the outcomes can range from 0 to 1. Categorization is done on the basis of threshold value.

Let us suppose, the threshold value is 0.5 . Then, the values above it will be in ‘1’ category & values below it will be in ‘0’ category.

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Applications

• Heart Attack Prediction.
• Credit Scoring
• Email Spam Non-Spam

and many more…

Using Logistic Regression

Way 1- Directly importing from scikit learn library in Python.

Input- from sklearn.linear_model import LogisticRegression

This imports the Logistic Model.

Way 2- Implementing Logistic Model from Scratch.

This was all about Logistic Regression algorithm.

Happy Learning Folks!

You can also visit my previous blogs by clicking on their name below-

• Google Colab
• Pandas
• NumPy
• Matplotlib
• OpenCV
• Data Preprocessing
• Linear Regression
• Decision Tree & Random Forest
• K-Nearest Neighbors

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