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We have developed a framework for spatial and spatiotemporal predictions based on ranger package in which also geographic locations of points are used in modeling. We call this framework "RFsp". We were amazed to see that RFsp can be used to generate comparable maps as those generated using geostatistics i.e. various types of kriging... the difference was that, with RFsp, there was no need to fit variograms or consider any stationarity conditions or transformations at all! Please let us know if this framework works also with your data (and if it does not work, why not?). https://github.com/thengl/GeoMLA

Hi everybody!

I am looking for authors (articles) who have worked on the prediction of soil classes (multi-class classification) by comparing several complex models of machine learning algorithms and using Logarithmic Loss and kappa to evaluate the performance of models.

Thank you

I am looking for authors (articles) who have worked on the prediction of soil classes (multi-class classification) by comparing several complex models of machine learning algorithms and using Logarithmic Loss and kappa to evaluate the performance of models.

Thank you

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Presenting Machine Learning series.

#SchoolofMachineLearning

#Theory #Code #learningisfun

Have you heard people talking about machine learning but only have some random thoughts about what that means? Are you a student who wants to cope up with this technology?

Are you intimidated by the fact that there are lots of machine learning resources online?

Don’t worry we present you Machine Learning Zero To One, A series will make you from Beginner to Expert Level. Let’s get on Board!

https://medium.com/meta-design-ideas/math-stats-and-nlp-for-machine-learning-as-fast-as-possible-915ef47ced5f

#SchoolofMachineLearning

#Theory #Code #learningisfun

Have you heard people talking about machine learning but only have some random thoughts about what that means? Are you a student who wants to cope up with this technology?

Are you intimidated by the fact that there are lots of machine learning resources online?

Don’t worry we present you Machine Learning Zero To One, A series will make you from Beginner to Expert Level. Let’s get on Board!

https://medium.com/meta-design-ideas/math-stats-and-nlp-for-machine-learning-as-fast-as-possible-915ef47ced5f

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#Worth_Article_On_Decision_Tree.

Machine Learning Basics.

The goal of the articles.

#1 What is Decision Tree? How does it work?

#2 Theoretical Introduction to Decision Tree.

#3 Mathematical concepts behind Decision Tree.

#4 Popular Decision Tree Algorithms.

#5 Coding example using

#6 Use Cases of Decision Tree.

Get on Board. Happy Learning.

https://medium.com/meta-design-ideas/decision-tree-a-light-intro-to-theory-math-code-10dbb3472ec4

Machine Learning Basics.

The goal of the articles.

#1 What is Decision Tree? How does it work?

#2 Theoretical Introduction to Decision Tree.

#3 Mathematical concepts behind Decision Tree.

#4 Popular Decision Tree Algorithms.

#5 Coding example using

#6 Use Cases of Decision Tree.

Get on Board. Happy Learning.

https://medium.com/meta-design-ideas/decision-tree-a-light-intro-to-theory-math-code-10dbb3472ec4

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Excellent explanation of the difference between ML AI and DS.

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Hanna made this function (see github) that builds on top of caret and helps with building models using clustered data st data.

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