List: Feature Engineering | Curated by Nathan Hanks

Oct 26, 2024
28 stories
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Feature Engineering
In
TDS Archive
by
Benjamin Bodner
Deep Learning vs Data Science: Who Will Win?What is more important, your data or your model?
Oct 22, 2024
38
Oct 22, 2024
38
In
TDS Archive
by
W Brett Kennedy
FormulaFeatures: A Tool to Generate Highly Predictive Features for Interpretable ModelsCreate more interpretable models by using concise, highly predictive features, automatically engineered based on arithmetic combinations of…
Oct 6, 2024
2
Oct 6, 2024
2
In
TDS Archive
by
Haden
Cyclical Encoding: An Alternative to One-Hot Encoding for Time Series FeaturesCyclical encoding provides your model with the same information using significantly fewer features
May 3, 2024
3
May 3, 2024
3
In
The Techlife
by
Fabiana Clemente
The 1 Python Package to Profile your Spark dataframesThe great debut of ydata-profiling into the big data landscape
Feb 17, 2023
1
Feb 17, 2023
1
In
Python in Plain English
by
Nick Hemenway
My Favorite Way to Smooth Noisy Data With PythonNearly all real-world data is noisy. What do I mean by noisy? Consider the following simple example: I’ve got a mass attached to a spring —…
Dec 4, 2022
6
Dec 4, 2022
6
In
TDS Archive
by
Rob Taylor, PhD
Multicollinearity: Problem, or Not?A brief guide on multicollinearity and how it affects multiple regression models
Jun 24, 2022
5
Jun 24, 2022
5
In
Towards AI
by
Mohneesh S
Savitzky-Golay Filter for data SmoothingMost underrated technique while Data Preprocessing
Jan 4, 2022
2
Jan 4, 2022
2
Raphael Schoenenberger
Encoding Temporal Features (Part 1)How to teach public holidays to Deep Neural Networks (DNN)
Mar 23, 2022
Mar 23, 2022
In
TDS Archive
by
Mark Derdzinski
Five reasons feature engineering is key to maximizing data science impactA framework for evaluating data products and building high-impact teams
Jul 28, 2022
Jul 28, 2022
In
TDS Archive
by
Eryk Lewinson
Three Approaches to Feature Engineering for Time SeriesUsing dummy variables, cyclical encoding, and radial basis functions
Jul 29, 2022
5
Jul 29, 2022
5
Gianluca Malato
Why You Shouldn’t Use PCA in a Supervised Machine Learning ProjectSome flaws of Principal Component Analysis that affect supervised machine learning projects
Jul 10, 2022
8
Jul 10, 2022
8
In
TDS Archive
by
Indraneel Dutta Baruah
Deep-dive on ML techniques for feature selection in Python — Part 2The second part of a series on ML-based feature selection where we discuss popular embedded and wrapper methods like Lasso regression…
Jul 10, 2022
Jul 10, 2022
Armand Sauzay
SHAP values: Machine Learning interpretability and feature selection made easy.Machine learning interpretability with hands on code with SHAP.
Jun 26, 2022
2
Jun 26, 2022
2
Shagun Kala
Intuitive trick to find important features in the data
May 2, 2022
2
May 2, 2022
2
Brenda Loznik
Pump it up — How to build a high-ranking modelAfter the hyperparameter tuning step, I combined the best performing models in an ensemble. For the ensemble I used a regular voting…
Dec 27, 2021
Dec 27, 2021
Brenda Loznik
Pump it up — Which features should you include in your model?During EDA, I identified features that appeared to have great overlap with other features. Multicollinearity occurs when two or more…
Dec 27, 2021
1
Dec 27, 2021
1
In
TDS Archive
by
Cristiana de Azevedo von Stosch
Why Graph-modeling Frameworks are the Future of Unsupervised LearningCo-authored by Abhishek Singh, Machine Learning Engineer at Bayer Pharmaceuticals, former Microsoft, JPMorgan Chase & Co, HSBC, and by…
Apr 25, 2022
7
Apr 25, 2022
7
In
TDS Archive
by
Aashish Nair
Standardization vs NormalizationDistinguishing between two common feature scaling methods
Mar 21, 2022
1
Mar 21, 2022
1
In
TDS Archive
by
Andrew Engel
Normalizing Features Within GroupsAn alternative approach to standardization
Mar 22, 2022
1
Mar 22, 2022
1
In
TDS Archive
by
Wing Poon
Feature Engineering for Machine Learning (2/3)Part 2: Feature Generation
Mar 14, 2022
1
Mar 14, 2022
1