Data Science

Using Random Forests for Feature Selection with Categorical Features

Python helper functions for adding feature importance, and displaying them as a single variable.

Notebook here

Helper functions here

One of the best features of Random Forests is that it has built-in Feature Selection. Explicability is one of the things we often lose when we go from traditional statistics to Machine Learning, but Random

Code Snippet Corner

Matthew Alhonte

matt Sep 24

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Tuning Random Forests Hyperparameters with Binary Search Part III: min_samples_leaf

Tune the min_samples_leaf parameter in for a Random Forests classifier in scikit-learn in Python

Part 1 (n_estimators) here
Part 2 (max_depth) here
Notebook here

Another parameter, another set of quirks!

min_samples_leaf is sort of similar to max_depth. It helps us avoid overfitting. It's also non-obvious what you should use

Code Snippet Corner

Matthew Alhonte

matt Sep 17

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Tuning Random Forests Hyperparameters with Binary Search Part II: max_depth

Code snippet corner is back! Tune the max_depth parameter in for a Random Forests classifier in scikit-learn in Python

Continued from here

Notebook for this post is here

Binary search code itself is here

max_depth is an interesting parameter. While n_estimators has a tradeoff between speed & score, max_depth has the possibility of improving both. By

Code Snippet Corner

Matthew Alhonte

matt Sep 10

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Code Snippet Corner: Tuning Machine Learning Hyperparameters with Binary Search

Tune the n_estimators parameter in for a Random Forests classifier in scikit-learn in Python

Ah, hyperparameter tuning. Time & compute-intensive. Frequently containing weird non-linearities in how changing a parameter changes the score and/or the time it takes to train the model.

RandomizedSearchCV goes noticeably faster than a full GridSearchCV but it still takes

Code Snippet Corner

Matthew Alhonte

matt Sep 03

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Automagically Turn JSON into Pandas DataFrames

Let pandas do the heavy lifting for you when turning JSON into a DataFrame.

In his post about extracting data from APIs, Todd demonstrated a nice way to massage JSON into a pandas DataFrame. This method works great when our JSON response is flat, because dict.keys() only gets the keys on the first

Python

Graham Beckley

graham Jul 28

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Trash Pandas: Messy, Convenient DB Operations via Pandas

(And a way to clean it up with SQLAlchemy)

Let's say you were continuing our task from last week: Taking a bunch of inconsistent Excel files and CSVs, and putting them into a database.

Let's say you've been given a new CSV that conflicts with some rows you've already

Pandas

Matthew Alhonte

matt Jul 23

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Data Could Save Humanity if it Weren't for Humanity

A compelling case for robot overlords.

A decade has passed since I stumbled into technical product development. Looking back, I've spent that time almost exclusively in the niche of data-driven products and engineering. While it seems obvious now, I realized in the 2000s that you could

Data