alteryx

featuretools

Python2017–2023

alteryx / featuretools

featuretools

An open source python library for automated feature engineering

Explore on GitHub Read Hacker News discussions

7Distinct people

38Mentions

1Explicit recommenders

7.7KGitHub stars

REPOSITORY AT A GLANCE

What to know before exploring

What it is

An open source python library for automated feature engineering

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

To get started, I'd pick the most important business problem you have and then solve it using the simplest machine learning approach. You mentioned using Pytorch. Instead, I…

kmax12 used a recommendation phrase. 2 indexed comments match. This is a phrase match, not a verdict.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

alteryx/compose · linked in 2 of the same discussions

Mentioned in the same Hacker News threads. That is not proof they are substitutes.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 11:00:58 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by year38 dated mentions

Observed mentions: 2017-11-06 14:51:21 UTC to 2023-09-20 21:40:24 UTC.

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges

UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
Period	Start (UTC)	End (exclusive, UTC)	Mentions	Submissions	Comments
2017	2017-01-01 00:00:00 UTC	2018-01-01 00:00:00 UTC	7	1	6
2018	2018-01-01 00:00:00 UTC	2019-01-01 00:00:00 UTC	19	0	19
2019	2019-01-01 00:00:00 UTC	2020-01-01 00:00:00 UTC	7	0	7
2020	2020-01-01 00:00:00 UTC	2021-01-01 00:00:00 UTC	4	0	4
2021	2021-01-01 00:00:00 UTC	2022-01-01 00:00:00 UTC	0	0	0
2022	2022-01-01 00:00:00 UTC	2023-01-01 00:00:00 UTC	0	0	0
2023	2023-01-01 00:00:00 UTC	2024-01-01 00:00:00 UTC	1	1	0

What Hacker News says

7 sources

kmax12·Oct 1, 2019

Feature Labs, Inc. | Software Engineer | Boston, MA | Full-time | On-site | https://www.featurelabs.com At Feature Labs, we’re building automation technology to make machine learning easier to use. We work on many unique and important machine learning challenges with a focus on making sure machine learning isn’t just a theoretical endeavor but has a real-world impact. Currently, our stack is mostly Python on the backend (pandas, django, sklearn), React for the front end, and AWS and docker for deployment. Above all, we prioritize the ability to choose the right approach to solve a problem. We love open source and hope you do too: https://www.featurelabs.com/open Here is one of our most popular libraries: https://github.com/featuretools/featuretools Apply at https://www.featurelabs.com/careers/

In Ask HN: Who is hiring? (October 2019)

kmax12·Sep 3, 2019

In Ask HN: Who is hiring? (September 2019)

kmax12·Aug 1, 2019

In Ask HN: Who is hiring? (August 2019)

kmax12·May 10, 2019

I think it’s a bit of an overstatement to call this an end-to-end solution. What they are starting with here is a single table of data with all the features already defined and an existing binary label column. Typically when this type of data is collected in the field it is much more fine grained (i.e many observations collected over time) and unlabeled (e.g how do we define a true example? How many false examples do we select?). The competition description even goes as far to say “We have chosen a dataset that you can get started with easily”. So, yes, this is a cool demonstration of Google's product, but the success in the competition might not extend to the problems real business face when trying to apply ML to a problem like this. That being said, I do think AutoML can help with these problems as it is extended to handle data that isn

Read the rest

’t in a single table already. For example, I’m a developer of a open source library called Featuretools (https://github.com/Featuretools/featuretools) that tries to automate feature engineering for temporal and relational datasets. Basically, it helps data scientists prepare real world data into the form this competition starts with.

In An End-to-End AutoML Solution for Tabular Data at KaggleDays

kmax12·Apr 21, 2019

I see this as a very timely question. As ML has proliferated, so has the number of ways to construct machine learning pipelines. This means that from one project to another the tools/libraries change, the codebases start looking very different, and each project gets its own unique deployment and evaluation process. In my experience, this causes several problems that hinder the expansion of ML within organizations 1. When a ML project inevitably changes hands, there is a strong desire from the new people working on it to tear things down and start over 2. It's hard to leverage and build upon success on previous projects e.g "my teammate built a model that worked really well for predict X, but I can use any of the that code to predict y" 3. Data science managers face challenges tracking progress and managing multiple data science projects

Read the rest

simultaneously. 4. People and teams new to machine learning have a hard time charting single a path to success. While coming up with a single way to build machine learning pipelines may never be possible, consolidation in the approaches would go a long way. We've already seen that happen in the modeling algorithms part of the pipeline with libraries like scikit-learn. It doesn't matter what machine learning algorithm you use, it the code will be fit/transform/predict. Personally, I've noticed this problem of multiple approaches and ad-hoc solutions to be most prevalent in the feature engineering step of the process. That is one of the reasons I work on an open source library called Featuretools (http://github.com/featuretools/featuretools/) that aims to use automation to create more reusable abstrations for feature engineering. If you look at our demos (https://www.featuretools.com/demos), we cover over a dozen different use cases, but the code you use to construct features stays the same. This means it is easier to reuse previous work and reproduce results in development and production environments. Ultimately, building successful machine learning pipelines is not about having an approach for the problem you are working on today, but something that generalizes across the all the problems you and your team will work on in the future.

In Ask HN: What does your ML pipeline look like?

asavinov·Feb 14, 2019

Deep feature extraction is important for not only image analysis but also in other areas where specialized tools might be useful such as listed below: o https://github.com/Featuretools/featuretools - Automated feature engineering with main focus on relational structures and deep feature synthesis o https://github.com/blue-yonder/tsfresh - Automatic extraction of relevant features from time series o https://github.com/machinalis/featureforge - creating and testing machine learning features, with a scikit-learn compatible API o https://github.com/asavinov/lambdo - Feature engineering and machine learning: together at last! The workflow engine allows for integrating feature training and data wrangling tasks with conventional ML o https://github.com/xiaoganghan/awesome-feature-engineering - other resource related to feature engineering (video

Read the rest

, audio, text)

In The Unreasonable Effectiveness of Deep Feature Extraction

westurner·Jan 21, 2019

To add to the three points in the article: Data quality https://en.wikipedia.org/wiki/Data_quality Imputation https://en.wikipedia.org/wiki/Imputation_(statistics) Feature selection https://en.wikipedia.org/wiki/Feature_selection datacleaner can drop NaNs, do imputation with "the mode (for categorical variables) or median (for continuous variables) on a column-by-column basis", and encode "non-numerical variables (e.g., categorical variables with strings) with numerical equivalents" with Pandas DataFrames and scikit-learn. https://github.com/rhiever/datacleaner sklearn-pandas "[maps] DataFrame columns to transformations, which are later recombined into features", and provides "A couple of special transformers that work well with pandas inputs: CategoricalImputer and FunctionTransformer" https://github.com/scikit-learn-contrib/sklearn-pand

Read the rest

as Featuretools https://github.com/Featuretools/featuretools > Featuretools is a python library for automated feature engineering. [using DFS: Deep Feature Synthesis] auto-sklearn does feature selection (with e.g. PCA) in a "preprocessing" step; as well as "One-Hot encoding of categorical features, imputation of missing values and the normalization of features or samples" https://automl.github.io/auto-sklearn/master/manual.html#tur... auto_ml uses "Deep Learning [with Keras and TensorFlow] to learn features for us, and Gradient Boosting [with XGBoost] to turn those features into accurate predictions" https://auto-ml.readthedocs.io/en/latest/deep_learning.html#...

In Steps to a clean dataset with Pandas