Data problems
Another nice Medium post from Benjamin Obi Tayo has a good summary of the types of issues you should always be mindful of when you get a new data set:
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Model error quantification
When we train a machine-learning model, we almost always report some performance metric, such as accuracy, recall, or F1-score.
(TIL) Pandas: Pipe function
Pandas introduced pipe() starting from version 0.16.2. pipe() enables user-defined methods in method chains.
(TIL) Sklearn: Tree diagram
The plot_tree() function allows you to create a diagram of steps present in a decision tree model:
(TIL) Sklearn: Pipeline diagram
Estimators can be displayed with a HTML representation when shown in a jupyter notebook. This can be useful to diagnose or visualize a Pipeline with many ...