Bayesian

How to solve binary classification problems using Bayesian methods in Python.Bayesian Thinking — OpenAI DALL-E Generated Image by AuthorIntroductionIn this article, I will build a simple Bayesian logistic regression model using Pyro, a Python probabilistic programming package. This article will cover EDA, feature engineering, model build and evaluation. The focus is to provide a simple framework for Bayesian logistic regression. Therefore, the depth of the first two sections will be limited. The code used in this article…

Bayesian Inference Applied to Real-World ExamplesContinue reading on Towards Data Science » Bayesian Inference Applied to Real-World ExamplesContinue reading on Towards Data Science » FOLLOW US ON GOOGLE NEWS Read original article here Denial of responsibility! Techno Blender is an automatic aggregator of the all world’s media. In each content, the hyperlink to the primary source is specified. All trademarks belong to their rightful owners, all materials to their authors. If you are the owner of the content…

Do we or can we think as a Bayesian?Continue reading on Towards Data Science » Do we or can we think as a Bayesian?Continue reading on Towards Data Science » FOLLOW US ON GOOGLE NEWS Read original article here Denial of responsibility! Techno Blender is an automatic aggregator of the all world’s media. In each content, the hyperlink to the primary source is specified. All trademarks belong to their rightful owners, all materials to their authors. If you are the owner of the content and do not want us to…

A primer in Bayesian thinking and AB testing using PyroCredit: Free-Photos on PixabayThis article is an introduction to AB testing using the Python probability programming language (PPL) Pyro, an alternative to PyMC. The motivation for writing this article was to further my understanding of Bayesian statistical inference using the Pyro framework and to help others in the process. As such, feedback is welcomed and encouraged.IntroductionMy previous experience with Bayesian modelling in Python was with PyMC. However, I have…

The ability to ask questions to your data set has always been an intriguing prospect.Continue reading on Towards Data Science » The ability to ask questions to your data set has always been an intriguing prospect.Continue reading on Towards Data Science » FOLLOW US ON GOOGLE NEWS Read original article here Denial of responsibility! Techno Blender is an automatic aggregator of the all world’s media. In each content, the hyperlink to the primary source is specified. All trademarks belong to their rightful…

Exploring PyMC’s Insights with SHAP Framework via an Engaging Toy ExampleSHAP values (SHapley Additive exPlanations) are a game-theory-based method used to increase the transparency and interpretability of machine learning models. However, this method, along with other machine learning explainability frameworks, has rarely been applied to Bayesian models, which provide a posterior distribution capturing uncertainty in parameter estimates instead of point estimates used by classical machine learning models.While Bayesian…

A new and shiny library from the PyMC team worth trying outPhoto by Nathan Fertig on UnsplashYou can tell the importance of a topic by how many big companies are releasing software packages on it. In the field of marketing mix modeling, you can see thatEven better than marketing mix modeling is Bayesian marketing mix modeling, which Google’s and PyMC Labs’ libraries provide. While LMMM is certainly interesting as well, today we will focus on PyMC Marketing.In this article, you will learn how easy it is to build a…

A step-by-step tutorial to build Bayesian optimization from the grounds upPhoto by Brett Jordan on UnsplashHyperparameter optimization has become a necessary step in most machine learning pipelines and probably the most well-known “learning” approach towards hyperparameter optimization is Bayesian Optimization. The task intended to help choose a set of optimal parameters for the cost (or objective) function in a learning algorithm is called hyperparameter optimization. These parameters can be data-driven (e.g. various…

Most common hyperparameter optimization methodologies to boost machine learning outcomesPhoto by Jonas Jaeken on UnsplashUsually the first solution that comes to mind when trying to improve a machine learning model is to just add more data to it. Additional data usually helps (barring certain situations) but generating high-quality data can be quite expensive. Hyperparameter optimization can save us time and resources by getting the best model performance using the existing data.Hyperparameter optimization, as the name…

Introducing maximum likelihood estimation, maximum a posteriori estimation and Bayesian InferencePhoto by fabio on UnsplashIn this post we will introduce the concepts MLE (maximum likelihood estimation), MAP (maximum a posteriori estimation) and Bayesian inference — which are fundamental to statistics, data science and machine learning, to name just a few fields. We will explain each method using the same example of an unfair coin toss, derive results analytically and numerically (for Bayesian inference) and show…