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Data problems

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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:

Model error quantification

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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

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Pandas introduced pipe() starting from version 0.16.2. pipe() enables user-defined methods in method chains.

(TIL) Sklearn: Tree diagram

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The plot_tree() function allows you to create a diagram of steps present in a decision tree model:

(TIL) Sklearn: Pipeline diagram

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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 ...