# How to Implement Logistic Regression Algorithm, with Orange Data Science Tool

A Classification problem, we implemented the Logistic Regression solution with the Orange Data Science tool

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1662832520271/L2kp0G-bh.jpeg)

Logistic regression is another technique borrowed by machine learning from the field of statistics.

It is the go-to method for binary classification problems (problems with two class values). In this post you will discover the logistic regression algorithm for machine learning.

After reading this post you will know:

*   The many names and terms used when describing logistic regression (like log odds and logit).
*   The representation used for a logistic regression model.
*   Techniques used to learn the coefficients of a logistic regression model from data.
*   How to actually make predictions using a learned logistic regression model.
*   Where to go for more information if you want to dig a little deeper.

This post was written for developers interested in applied machine learning, specifically predictive modeling. You do not need to have a background in linear algebra or statistics.

**Concepts of Regression Logistic**

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1662832521771/hmsrURZ5k.png)

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1662832523263/KvizGlttW.png)

### Now Implement Logistic Regression with Orange Tools

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