Tag: Logistic

Logistic regression is a widely used statistical and machine learning algorithm primarily used for binary classification tasks. Unlike linear regression, which is designed for predicting continuous numerical values, logistic regression is specifically tailored to handle categorical outcomes with two...

Do you love logistic regression, but hate interpreting anything with any form of logarithmic transformation? Well, I can’t say you are in good company, but I can say that you do have me as company! In this article, I’m going to talk all about interpreting logistic regression...

Welcome to another exciting chapter of the Data Science 2-Minutes Series! In this chapter, we’re going to dive into the world of Logistic Regression using the classic “Iris” dataset. Get ready for a fun and informative read on classification! Step 1: Data Wrangling We start b...

Do you love logistic regression, but hate interpreting anything with any form of logarithmic transformation? Well, I can’t say you are in good company, but I can say that you do have me as company! In this article, I’m going to talk all about interpreting logistic regression...

Do you love logistic regression, but hate interpreting anything with any form of logarithmic transformation? Well, I can’t say you are in good company, but I can say that you do have me as company! In this article, I’m going to talk all about interpreting logistic regression...

Logistic regression is one of the most common machine learning algorithms. It can be used to predict the probability of an event occurring, such as whether an incoming email is spam or not, or whether a tumor is malignant or not, based on a given labeled data set. Due to its simplicity, logistic ...

Logistic regression is a supervised machine learning algorithm that is used to predict the probability of an event occurring. It is a classification algorithm, which means that it can be used to classify data into two or more categories. Understanding Logistic Regression At its core, Logistic ...

Imagine this — You are a data-scientist working with one of the largest convenience delivery firms in the world. For the past few days, you have been working on a new algorithm that would speed up deliveries and cut costs! This algorithm is a new way of deciding which delivery-partner should b...

In this article we will explore why we need Logistic, how we derived Logistic from Linear and a few more important facts in mathematics. Let’s Recall the nature of Linear Regression: In Linear Regression, we try to estimate the continuous variable Y depending on any type of variable X wh...

You are a logistic performance manager in an international clothing group that has stores all around the world. The company produces garments, bags and accessories in factories located in Asia. Stores are delivered from local warehouses that are dir...

When it comes to improving warehouse productivity, there’s no one-size-fits-all solution. But with Lean Six Sigma methodology and the power of Logistic Regression in Python, you can create a custom incentive policy that works for your business. In this article, we’ll walk you throu...

Here’s an example of an end-to-end logistic regression model implemented in Python using the scikit-learn library: import numpy as np import pandas as pd from sklearn.linear_model import LogisticRegression from sklearn.metrics import accuracy_score from sklearn.model_select...

It is so cool to predict the future! We can do the same if we have the right set of past data, which is also known as the ground truth. Suppose you have data on temperature, humidity, wind, and cloud for a location, and you want to predict if it will rain in the upcoming few days. These parameters o...

At its heart, the difference between linear regression and logistic regression lies in the nature of the dependent variable they are each designed to predict. Linear regression is used to forecast a continuous dependent variable using a linear relationship with one or more independent variables. Con...

If you are only interested in learning how to interpret coefficients, you can skip to the section below on Coefficients Interpretation. Introduction First, let’s briefly introduce Logistic Regression and its two variants. Logistic Regression is a type of Generalized Linear Regressio...

Then we rotate the line a little bit and do the same. The line with the smallest sum of squared residuals is the line chosen to fit best. Why can’t we make use of least-squares to find the best fitting line in logistic regression? Well, to answer this we need to recall logistic regressio...

I’ve always been fascinated by Logistic Regression. It’s a fairly simple yet powerful Machine Learning model that can be applied to various use cases. It’s been widely explained and applied, and yet, I haven’t seen many correct and simple interpretations of the model its...

Logistic regression is a widely used classification algorithm that uses a linear model to predict the probability of a binary outcome. It is a simple and effective way to model binary data, but it can sometimes suffer from overfitting and poor generalization to new data. Regularization is a tec...