Showing entries tagged [machine]

K Nearest Neighbors

Fred Rogers

K Nearest Neighbors (KNN) is a supervised machine learning method that "memorizes" (stores) an entire dataset, then relies on the concepts of proximity and similarity to make predictions about new data. The basic idea is that if a new data point is in some sense "close" to existing data points, its value is likely to be similar to the values of its neighbors. In the Earth Systems Sciences, such techniques can be useful for small- to moderate-scale classification and regression problems.

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Quick Tips for ESS Machine Learning Projects

Your idea of what's entailed in setting up a supervised Machine Learning (ML) project as an Earth Systems scientist is probably not as fanciful as what an image generation algorithm came up with. But there are many little decisions ML practitioners make along the way when starting an Earth Systems Science (ESS) ML project. This article provides some tips and ideas to consider as you're getting started. These tips are not in any particular order, and like all things related to ML projects they depend on the specific types of data and project goals.

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R2: Downsides and Potential Pitfalls for ESS ML Prediction

Datasaurus plot
Always plot your data!

Regression analysis is a fundamental concept in the field of machine learning (ML), in that it helps establish relationships among the variables by estimating how one variable affects the other.

The coefficient of determination, R2 (pronounced “R squared”), is a measure that provides information about how well the regression line suggested by a numerical model approximates the actual data (often referred to as “goodness of fit”).

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Self Organizing Maps for Earth Systems Science

Representation of Self Organizing Map
Representation of nodes in a Self Organizing Map.

A self-organizing map (SOM), sometimes known as a Kohonen map after its originator the Finnish professor Teuvo Kohonen, is an unsupervised machine learning technique used to produce a low-dimensional representation of a higher dimensional data set. SOMs are a specific type of artificial neural network, but use a different training strategy compared to more traditional artificial neural networks (ANNs). SOMs can be used for clustering, dimensionality reduction, feature extraction, and classification — all of which suggest that they can be important tools for understanding large Earth Systems Science (ESS) datasets.

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Why are GPUs Exciting for Machine Learning Research?

NVIDIA A100 graphics card

Machine Learning systems are often configured around Graphics Processing Units (GPUs) rather than Central Processing Units (CPUs). Why should this be the case, in an era when CPUs are powerful and (relatively) inexpensive? This article provides some insights into what GPUs are and why they provide advantages for certain types of computations, including some commonly used for machine learning and modeling.

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News@Unidata
News and information from the Unidata Program Center
News@Unidata
News and information from the Unidata Program Center

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