Search results for "Bayesian Modeling"

A Gentle Introduction to Bayesian Belief Networks

A Gentle Introduction to Bayesian Belief Networks

Probabilistic models can define relationships between variables and be used to calculate probabilities. For example, fully conditional models may require an enormous amount of data to cover all possible cases, and probabilities may be intractable to calculate in practice. Simplifying assumptions such as the conditional independence of all random variables can be effective, such as […]

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Example bayesian network

Introduction to Bayesian Networks with Jhonatan de Souza Oliveira

This post is a spotlight interview with Jhonatan de Souza Oliveira on the topic of Bayesian Networks. Could you please introduce yourself? My name is Jhonatan Oliveira and I am an undergraduate student in Electrical Engineering at the Federal University of Vicosa, Brazil. I have been interested in Artificial Intelligence since the beginning of college, when had […]

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Scatter Plot of Dataset With Linear Model and Prediction Interval

Prediction Intervals for Machine Learning

A prediction from a machine learning perspective is a single point that hides the uncertainty of that prediction. Prediction intervals provide a way to quantify and communicate the uncertainty in a prediction. They are different from confidence intervals that instead seek to quantify the uncertainty in a population parameter such as a mean or standard […]

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Automated Machine Learning (AutoML) Libraries for Python

Automated Machine Learning (AutoML) Libraries for Python

AutoML provides tools to automatically discover good machine learning model pipelines for a dataset with very little user intervention. It is ideal for domain experts new to machine learning or machine learning practitioners looking to get good results quickly for a predictive modeling task. Open-source libraries are available for using AutoML methods with popular machine […]

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Combined Algorithm Selection and Hyperparameter Optimization (CASH Optimization)

Combined Algorithm Selection and Hyperparameter Optimization (CASH Optimization)

Machine learning model selection and configuration may be the biggest challenge in applied machine learning. Controlled experiments must be performed in order to discover what works best for a given classification or regression predictive modeling task. This can feel overwhelming given the large number of data preparation schemes, learning algorithms, and model hyperparameters that could […]

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HyperOpt for Automated Machine Learning With Scikit-Learn

HyperOpt for Automated Machine Learning With Scikit-Learn

Automated Machine Learning (AutoML) refers to techniques for automatically discovering well-performing models for predictive modeling tasks with very little user involvement. HyperOpt is an open-source library for large scale AutoML and HyperOpt-Sklearn is a wrapper for HyperOpt that supports AutoML with HyperOpt for the popular Scikit-Learn machine learning library, including the suite of data preparation […]

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Auto-Sklearn for Automated Machine Learning in Python

Auto-Sklearn for Automated Machine Learning in Python

Automated Machine Learning (AutoML) refers to techniques for automatically discovering well-performing models for predictive modeling tasks with very little user involvement. Auto-Sklearn is an open-source library for performing AutoML in Python. It makes use of the popular Scikit-Learn machine learning library for data transforms and machine learning algorithms and uses a Bayesian Optimization search procedure […]

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How to Spot-Check Imbalanced Machine Learning Algorithms

Step-By-Step Framework for Imbalanced Classification Projects

Classification predictive modeling problems involve predicting a class label for a given set of inputs. It is a challenging problem in general, especially if little is known about the dataset, as there are tens, if not hundreds, of machine learning algorithms to choose from. The problem is made significantly more difficult if the distribution of […]

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