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Home - Thomas Huijskens. Bayesian optimization with scikit-learn. Choosing the right parameters for a machine learning model is almost more of an art than a science. Kaggle. Competitors spend considerable time on tuning their model in the hopes of winning competitions, and proper model selection plays a huge part in that. It is remarkable then, that the industry standard algorithm for selecting hyperparameters, is something as simple as random search. There is actually a whole field dedicated to this pro...

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Thomas Huijskens · | thuijskens.github.io Reviews

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Home - Thomas Huijskens. Bayesian optimization with scikit-learn. Choosing the right parameters for a machine learning model is almost more of an art than a science. Kaggle. Competitors spend considerable time on tuning their model in the hopes of winning competitions, and proper model selection plays a huge part in that. It is remarkable then, that the industry standard algorithm for selecting hyperparameters, is something as simple as random search. There is actually a whole field dedicated to this pro...

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Thomas Huijskens · | thuijskens.github.io Reviews

https://thuijskens.github.io

Home - Thomas Huijskens. Bayesian optimization with scikit-learn. Choosing the right parameters for a machine learning model is almost more of an art than a science. Kaggle. Competitors spend considerable time on tuning their model in the hopes of winning competitions, and proper model selection plays a huge part in that. It is remarkable then, that the industry standard algorithm for selecting hyperparameters, is something as simple as random search. There is actually a whole field dedicated to this pro...

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Archives · Thomas Huijskens

https://thuijskens.github.io/archive

Archives - Thomas Huijskens. 29 Dec 2016 Bayesian optimization with scikit-learn. 25 Aug 2016 The fundamental problem of causal analysis. 03 Aug 2016 Long-term forecasting with machine learning models.

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About me · Thomas Huijskens

https://thuijskens.github.io/about

About me - Thomas Huijskens. I am Thomas Huijskens, born in the US but brought up in The Netherlands. I am a Data Scientist at QuantumBlack. And I work and live in London. I’m using this site to occasionally share some of my personal insights and opinions about the world of data science. I have also published a paper in these fields, and if you are really interested in that you can read the pre-print here. And find the final published version at the Journal of Computational and Applied Mathematics.

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The fundamental problem of causal analysis · Thomas Huijskens

https://thuijskens.github.io/2016/08/25/causal-modelling

The fundamental problem of causal analysis - Thomas Huijskens. The fundamental problem of causal analysis. Correlation does not imply causation. Is one of those principles every person that works with data should know. It is one of the first concepts taught in any introduction to statistics class. There is a good reason for this, as most of the work of a data scientist, or a statistician, does actually revolve around questions of causation:. Use these principles to correct for hidden bias in their A/B te...

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Bayesian optimization with scikit-learn · Thomas Huijskens

https://thuijskens.github.io/2016/12/29/bayesian-optimisation

Bayesian optimization with scikit-learn - Thomas Huijskens. Bayesian optimization with scikit-learn. Choosing the right parameters for a machine learning model is almost more of an art than a science. Kaggle. Competitors spend considerable time on tuning their model in the hopes of winning competitions, and proper model selection plays a huge part in that. It is remarkable then, that the industry standard algorithm for selecting hyperparameters, is something as simple as random search. There is actually ...

5

Long-term forecasting with machine learning models · Thomas Huijskens

https://thuijskens.github.io/2016/08/03/time-series-forecasting

Long-term forecasting with machine learning models - Thomas Huijskens. Long-term forecasting with machine learning models. A common problem in time series analysis is to make a forecast for the time series at hand. An extensive theory around on the different types of models you can use for calculating a forecast of your time series is already available in the literature. Seasonal ARIMA. Models and state-space models. Forecasts (which in itself is an ambiguous term, as it depends on the context) for a.

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Thomas Huijskens ·

Home - Thomas Huijskens. Bayesian optimization with scikit-learn. Choosing the right parameters for a machine learning model is almost more of an art than a science. Kaggle. Competitors spend considerable time on tuning their model in the hopes of winning competitions, and proper model selection plays a huge part in that. It is remarkable then, that the industry standard algorithm for selecting hyperparameters, is something as simple as random search. There is actually a whole field dedicated to this pro...

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