We present a versatile GPU-based parallel version of Logistic Regression (LR), aiming to address the increasing demand for faster algorithms in binary classification due to large data sets. Our implementation is a direct translation of the parallel Gradient Descent Logistic Regression algorithm proposed by X. Zou et al. [12]. Our experiments demonstrate that our GPU-based LR outperforms existing CPU-based implementations in terms of execution time while maintaining comparable f1 score. The significant acceleration of processing large datasets makes our method particularly advantageous for real-time prediction applications like image recognition, spam detection, and fraud detection. Our algorithm is implemented in a ready-to-use Python library available at : https://github.com/NechbaMohammed/SwiftLogisticReg

The logistic regression algorithm is a probabilistic machine learning algorithm used for classification tasks. This is usually the first classification algorithm you'll try a classification task on. Unlike many machine learning algorithms that seem to be a black box, the logisitc regression algorithm is easily understood. In this tutorial, you'll learn everything you need to know about the logistic regression algorithm. You'll start by creating a custom logistic regresssion algorithm. This will help you understand everything happening under the hood and how to debug problems with your logisitic regression models. Next, you'll learn how to train and optimize Scikit-Learn implementation of the logistic regression algorithm. Finally, you'll learn how to handle multiclass classification tasks with this algorithm. This tutorial covers L1 and L2 regularization, hyperparameter tuning using grid search, automating machine learning workflow with pipeline, one vs rest classifier, object-oriented programming, modular programming, and documenting Python modules with docstring.

Did you know in 2.17mins you can discover your present heart condition? I think you'll agree with me when I say: Artificial Intelligence is the future of computing. The study of AI has improved over the years with multiple universities teaching the fundamentals and transcending verbal courses to real life sectors with the health sector being one of the foremost benefactor. Artificial intelligence in the health sector is gradually taking the world by surprise. From a history of the earliest notable work in AI by Alan Mathison Turing in the mid-20th century, now, we have over 400% increase in student study on AI according to Will Hazell an education correspondent in 2021.

Logistic regression is a method to create a model by using binary data (0 and 1). The goal is to predict something independent variable based on a dependent variable. In a real application, logistic regression is applied to predict the number of customers who buy a product or who did not base on their previous transaction, to predict the number of fraud transactions in credit cards, and so on. In logistic regression, Y-axis lies from 0 – 1. Logistic regression cannot be solved by using a linear equation like linear regression. That is because if the Y-axis of the logistic function is transformed into a linear function, the boundary of the Y-axis lies from -infinity to infinity. Then, when we calculate the misfit error between actual data and predicted data, it will not get a good misfit error.

Linear machine learning algorithms assume a linear relationship between the features and the target variable. In this article, we'll discuss several linear algorithms and their concepts. Here's a glimpse into what you can expect to learn: You can use linear algorithms for classification and regression problems. Let's start by looking at different algorithms and what problems they solve. Linear regression is arguably one of the oldest and most popular algorithms.

In this blog post, I would like to continue my series on "building from scratch." I will discuss a linear classifier called Logistic Regression. After the discussion of the theoretical concepts we will dive into the code. So, without a further adieu let's start the discussion with the basics of a classifier. A classifier is an estimator that assigns a class label to the input data point.

Regression analysis is a technique used to determine the relationship between the dependent and the independent variable (s) for prediction purposes. It is a good tool for data modelling and analysis. There are different regression techniques. Our focus will be on Logistic Regression. Logistic Regression is suitable when there are more than one independent variables in a dataset.

Bob has started his own mobile company. He wants to give a tough fight to big companies like Apple, Samsung etc. He does not know how to estimate the price of mobiles his company creates. In this competitive mobile phone market, you cannot simply assume things. To solve this problem he collects sales data of mobile phones of various companies.

The model and the preprocessing are individual for each project. Hyperparameters are tuned according to the dataset and using the same hyperparameters for each project compromises the accuracy of the results. For example, there are different hyperparameters such as'solver', 'C', 'penalty' in the Logistic Regression algorithm, and different combinations of these give different results. Similarly, there are adjustable parameters for Support Vector Machine such as gamma value, C value, and combination of them also gives different results. These hyperparameters of the algorithms are available on the sklearn website.

This article will talk about Logistic Regression, a method for classifying the data in Machine Learning. Logistic regression is generally used where we have to classify the data into two or more classes. One is binary and the other is multi-class logistic regression. As the name suggests, the binary class has 2 classes that are Yes/No, True/False, 0/1, etc. In multi-class classification, there are more than 2 classes for classifying data. " Logistic Regression is a classification algorithm for categorical variables like Yes/No, True/False, 0/1, etc."