![]() ![]() Or, to get more technical, different versions of numerical libraries Good to know how things fit together, and what the limitations of dependency packages may be.įor example, knowing that Keras in R also uses Python we may want to consider documenting Apart from knowing which packages were used, it is also Understand the dependencies #įind and document dependencies. Keeping our work maintainable as it grows, but also producing cleaner and more consistent code.Īlso, removing things we tried out and then didn't use is a good habit to get into. Picking the minimal set of tools (and getting good at using it) goes a long way to not only That library that wraps one-hot encoding in a really neat way? For example, do we really needīoth caret and MLR, packages for fitting exotic models that cover special cases? How about data.tableĪnd tidyverse? Plotly, seaborn and bokeh? Both tensorflow and pytorch? Projects reproducible and maintainable over time. Here are a few recommendations to keep complex Use of versioning, setting seeds, and not hard-coding stuff. ![]() Leads to improved technical reproducibility of our work, all in addition to other good practices such as Technical reproducibility #īeing able to understand and reproduce the complexity of our compute environment ), or doĭeep learning via our favourite modelling tools. Normally, people start with the basics - numpy,Īlready those come with a substantial set of dependencies and versions, and they are only the start - we may want When installing each of these from scratch, hundreds of packages may make their way onto ourįor Python, the situation is no different. Is an entire ecosystem of tools for manipulating data, mlr and dependencies capture For example in R, the tidyverse set of packages ![]() The correct answer to the questions above will vary, but for complex scripts and projects, it willįew users rely on plain R or Python alone. How about a Jupyter/Rmarkdown notebook? What about one that combines R and Python via.Can we share it with someone else to run also? A fundamental component of data science is sharing of results, outputs and findings along with theĬode that produced them. ![]()
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