Ryan Holbrook

@ryanpholbrook

Math, stats, programming. R, Python, Haskell. Interested in employment opportunities.

Vrijeme pridruživanja: prosinac 2010.

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  1. Prikvačeni tweet
    18. sij

    A thread of classifiers learning a decision rule. Dashed line is optimal boundary. Animations with by and . Logistic regression {stats::glm} with each class having normally distributed features. (1/n)

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  2. 26. sij

    Consider sending a few dollars their way. I think the devs are needing help with server costs. XGBoost is awesome!

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  3. 21. sij

    Boosted p-splines {mboost::mboost} on mixture of normals. (15/n)

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  4. 21. sij

    Naive Bayes {naivebayes::naive_bayes} on normal features. (14/n)

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  5. 21. sij

    Mixture discriminant analysis {mda::mda} on mixture of normals. (13/n)

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  6. 21. sij

    Extreme learning machine {elmNNRcpp::elm_train} on mixture of normals. (12/n)

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  7. 21. sij

    Gaussian process {kernlab::gausspr} on mixture of normals. (11/n)

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  8. 21. sij

    Here's a better GAM animation (more frames!).

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  9. 19. sij

    Here is a repo with the gif files. I added an open license on the page, but basically you can use them however you like with attribution.

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  10. 18. sij

    Also, a blog post on optimal decision boundaries: It has most of the plotting code. I'm planning on writing another on classifiers with the animation code. In the meantime, here's a gist:

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  11. 18. sij

    Neural network {nnet::nnet} on mixture of normals. (10/n)

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  12. 18. sij

    SVM {kernlab::ksvm} with RBF kernel on mixture of normals. (9/n)

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  13. 18. sij

    GAM {mgcv::gam} with spline smoother on mixture of normals. (8/n)

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  14. 18. sij

    XGBoost {xgboost::xgboost} on mixture of normals. (7/n)

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  15. 18. sij

    Random forest {ranger::ranger} on mixture of normals. (6/n)

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  16. 18. sij

    Decision tree {rpart::rpart} with features distributed as a mixture of normal distributions. (5/n)

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  17. 18. sij

    Nearest Neighbors {class::knn} on normal features. (4/n)

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  18. 18. sij

    MARS {earth::earth} on normal features. (3/n)

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  19. 18. sij

    Quadratic discriminant analysis {MASS::qda} with normal features. The QDA model is the same as the data model in this case, and so it fits the optimal boundary very closely. (2/n)

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  20. proslijedio/la je Tweet
    28. svi 2019.

    Today, we open sourced our port of CQL, the Categorical Query Language (formerly AQL). It harnesses applied category theory to take data migration to the next level, and was developed in close collaboration with Ryan Wisnesky of .

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