Publications by T. Moudiki
mlsauce’s `v0.17.0`: boosting with Elastic Net, polynomials and heterogeneity in explanatory variables
Last week in #135, I talked about mlsauce’s v0.13.0, and LSBoost in particular. When using LSBoost, it’s now possible to:Obtain prediction intervals for regression, notably by employing Split Conformal Prediction.Take into account an a priori heterogeneity in explanatory variables through clustering.In v0.17.0, I added a 2 new features to LSBoo...
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mlsauce’s `v0.13.0`: taking into account inputs heterogeneity through clustering
Last week in #134, I talked about mlsauce’s v0.12.0, and LSBoost in particular. As shown in the post, it’s now possible to obtain prediction intervals for the regression model, notably by employing Split Conformal Prediction.Right now (looking for ways to fix it), the best way to install the package, is to use the development version:pip instal...
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mlsauce’s `v0.12.0`: prediction intervals for LSBoostRegressor
Many of you (> 2600 reads so far) are reading this document on LSBoost, a gradient boosting algorithm for penalized nonlinear least squares. This never ceases to amaze me, because this document is quite… empty 🙂mlsauce’s v0.12.0 includes prediction intervals for the LSBoostRegressor in particular. These prediction intervals are obtained thro...
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Conformalized predictive simulations for univariate time series on more than 250 data sets
Predictive simulation of time series data is useful for many applications such as risk management and stress-testing in finance or insurance, climate modeling, and electricity load forecasting. This (preprint) paper proposes a new approach to uncertainty quantification for univariate time series forecasting. This approach adapts split conformal pr...
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learningmachine v1.1.2: for Python
I talked about learningmachine – a package for machine learning with uncertainty quantification and interpretatbility – last week in #131. Here comes the Python version!Keep in mind that learningmachine is still experimental, probably with some quirks (because achieving this level of abstraction required some effort), with no beautiful document...
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Bayesian inference and conformal prediction (prediction intervals) in nnetsauce v0.18.1
Version 0.18.1 of nnetsauce (Python version) is available on PyPI and for conda. New developments include Bayesian inference and conformal prediction. Bayesian inference is available for scikit-learn models that possess a posterior distribution (BayesianRidge, ARDRegressor, and GaussianProcessRegressor). Conformal prediction is available for every ...
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rtopy (v0.1.1): calling R functions in Python
rtopy is a Python package that allows you to call R functions in Python. There are other packages doing something similar, but in addition to have a different interface, under the hood, rtopy explicitly uses R at the command line, plus text mining and caching tools. It’s a work in progress, and you can find some examples of use below.1 – Instal...
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ahead forecasting (v0.10.0): fast time series model calibration and Python plots
ahead is an R, Python and Julia package for univariate and multivariate time series forecasting with uncertainty quantification (including predictive simulation). Matlab is next. The aim is always to make these implementations of ahead as similar as possible, but that’s a looot of work as you might guess. Do not hesitate to submit a pull request...
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Subsampling continuous and discrete response variables
New in nnetsauce v0.16.3:add robust scaler to type_scalingrelatively faster scaling in preprocessingRegression-based classifiers (see https://www.researchgate.net/publication/377227280_Regression-based_machine_learning_classifiers)DeepMTS (multivariate time series forecasting with deep quasi-random layers): see https://thierrymoudiki.github.io/blog...
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Tuning Machine Learning models with GPopt’s new version
A new version of Python package GPopt is available on PyPI. GPopt is a package for stochastic optimization based on Gaussian process regressors (for now, the name GP* is ‘unfortunate’). This type of optimization is particularly useful for tuning machine learning models’ hyperparameters.The main change in GPopt’s v0.3.0 is: the user can now ...
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