Search results for "Bayesian Modeling"

A Gentle Introduction to Model Selection for Machine Learning

A Gentle Introduction to Model Selection for Machine Learning

Given easy-to-use machine learning libraries like scikit-learn and Keras, it is straightforward to fit many different machine learning models on a given predictive modeling dataset. The challenge of applied machine learning, therefore, becomes how to choose among a range of different models that you can use for your problem. Naively, you might believe that model […]

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A Gentle Introduction to Maximum a Posteriori (MAP) for Machine Learning

A Gentle Introduction to Maximum a Posteriori (MAP) for Machine Learning

Density estimation is the problem of estimating the probability distribution for a sample of observations from a problem domain. Typically, estimating the entire distribution is intractable, and instead, we are happy to have the expected value of the distribution, such as the mean or mode. Maximum a Posteriori or MAP for short is a Bayesian-based […]

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Histogram of Dataset Constructed From Two Different Gaussian Processes

A Gentle Introduction to Expectation-Maximization (EM Algorithm)

Maximum likelihood estimation is an approach to density estimation for a dataset by searching across probability distributions and their parameters. It is a general and effective approach that underlies many machine learning algorithms, although it requires that the training dataset is complete, e.g. all relevant interacting random variables are present. Maximum likelihood becomes intractable if […]

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Probabilistic Model Selection Measures AIC, BIC, and MDL

Probabilistic Model Selection with AIC, BIC, and MDL

Model selection is the problem of choosing one from among a set of candidate models. It is common to choose a model that performs the best on a hold-out test dataset or to estimate model performance using a resampling technique, such as k-fold cross-validation. An alternative approach to model selection involves using probabilistic statistical measures […]

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A Gentle Introduction to Maximum Likelihood Estimation for Machine Learning

A Gentle Introduction to Maximum Likelihood Estimation for Machine Learning

Density estimation is the problem of estimating the probability distribution for a sample of observations from a problem domain. There are many techniques for solving density estimation, although a common framework used throughout the field of machine learning is maximum likelihood estimation. Maximum likelihood estimation involves defining a likelihood function for calculating the conditional probability […]

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Probability for Machine Learning

Probability for Machine Learning

Probability for Machine Learning Discover How To Harness Uncertainty With Python Machine Learning DOES NOT MAKE SENSE Without Probability What is Probability?…it’s about handling uncertainty Uncertainty involves making decisions with incomplete information, and this is the way we generally operate in the world. Handling uncertainty is typically described using everyday words like chance, luck, and […]

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A Gentle Introduction to Uncertainty in Machine Learning

A Gentle Introduction to Uncertainty in Machine Learning

Applied machine learning requires managing uncertainty. There are many sources of uncertainty in a machine learning project, including variance in the specific data values, the sample of data collected from the domain, and in the imperfect nature of any models developed from such data. Managing the uncertainty that is inherent in machine learning for predictive […]

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