Label Leakage: The Bug That Makes Churn Models Look Brilliant - Dataconomy
The model scores 0.94 AUC on held-out data. The notebook is clean, the cross-validation is stratified, nobody touched the test
feature-engineering
Feature Stores from Scratch: A Minimal Working Implementation
Build the five components every feature store needs, then see where AI changes the design.
How To Measure The Impact Of Features — Smashing Magazine
Meet TARS — a simple, repeatable, and meaningful UX metric designed specifically to track the performance of product features. Upcoming part of the Measure UX & Design Impact (use the code 🎟 IMPACT to save 20% off today).
Shapley Values Explained: Seeing Which Features Drive Your Predictions
Learn what Shapley values are and how SHAP tools help explain machine learning predictions.
How to Analyze Features Using Yellowbrick
In this article, we'll walk through the process of aggregating claims data to create meaningful provider features, visualize patterns using Yellowbrick's Parallel Coordinates, and explore other visualization tools available for feature analysis.
What is Dataset Distillation Learning? A Comprehensive Overview
Dataset distillation is an innovative approach that addresses the challenges posed by the ever-growing size of datasets in machine learning. This technique focuses on creating a compact, synthetic dataset that encapsulates the essential information of a larger dataset, enabling…
18 Data Profiling Tools Every Developer Must Know
Analytics, management, and business intelligence (BI) procedures, such as data cleansing, transformation, and decision-making, rely on data profiling. Content and quality reviews are becoming more important as data sets grow in size and variety of sources. In addition…
Basis Functions: Simple Definition - Statistics How To
Types of Functions > Basis functions (called derived features in machine learning) are building blocks for creating more complex functions. In other
Permutation Feature Importance from Scratch
Understanding the importance of permutations in the field of explainable AI
A Practical Guide to Data Normalization in R
Introduction Data normalization is a crucial preprocessing step in data analysis and machine learning workflows. It helps in standardizing the scale of numeric features, ensuring fair treatment to all variables regardless of their magnitude. In ...
Planning poker - Wikipedia
Planning poker, also called Scrum poker, is a consensus-based, gamified technique for estimating, mostly used for timeboxing in Agile principles. In planning poker, members of the group make estimates by playing numbered cards face-down to the table, instead of speaking them…
Why is Feature Scaling Important in Machine Learning? Discussing 6 Feature
Standardization, Normalization, Robust Scaling, Mean Normalization, Maximum Absolute Scaling and Vector Unit Length Scaling
https://www.uber.com/blog/research/maximum-relevance-and-minimum-redundancy-feature-selection-methods-for-a-marketing-machine-learning-platf…
A practical introduction to sequential feature selection
A gentle dive into this unusual feature selection technique
PacktPublishing/Python-Feature-Engineering-Cookbook-Second-Edition: Python
Python Feature Engineering Cookbook Second Edition, published by Packt - PacktPublishing/Python-Feature-Engineering-Cookbook-Second-Edition
python-data-science-handbook/scikit/SciKit-Feature-Engineering.ipynb at master · bjpcjp/python-data-science-handbook
Sourced from O'Reilly ebook of the same name.
scikit-learn/63_preprocessing.ipynb at master · bjpcjp/scikit-learn
Updates in progress. Jupyter workbooks will be added as time allows. - bjpcjp/scikit-learn
The 5 Feature Selection Algorithms every Data Scientist should know
Bonus: What makes a good footballer great?
Why you should always use feature embeddings with structured datasets
A simple technique for boosting accuracy on ANY model you use
[1605.09782v6] Adversarial Feature Learning
The ability of the Generative Adversarial Networks (GANs) framework to learn generative models mapping from simple latent distributions to arbitrarily complex data distributions has been...
Recursive Feature Elimination (RFE) for Feature Selection in Python
Recursive Feature Elimination, or RFE for short, is a popular feature selection algorithm. RFE is popular because it is easy to configure and use and because it is effective at selecting those features (columns) in a training dataset that are more or most relevant in predicting…
Feature Engineering: Data scientist's Secret Sauce ! - DataScienceCentral.com
originally posted by the author on Linkedin : Link It is very tempting for data science practitioners to opt for the best known algorithms for a given problem.However It’s not the algorithm alone , which can provide the best solution ; Model built on carefully engineered and…
Know Your Data: Part 1 - KDnuggets
This article will introduce the different type of data sets, data object and attributes.
Model-agnostic feature importance through ablation - Samuel Taylor
How to find Feature importances for BlackBox Models?
Permutation Importance as a feature selection method
Labeling, transforming, and structuring training data sets for machine lear
The O’Reilly Data Show Podcast: Alex Ratner on how to build and manage training data with Snorkel.
The 5 Feature Selection Algorithms every Data Scientist should know
This post is about some of the most common feature selection techniques one can use while working with data.
The Hitchhiker’s Guide to Feature Extraction
168 votes, 13 comments. 2.2M subscribers in the datascience community. A space for data science professionals to engage in discussions and debates on…
A Feature Selection Tool for Machine Learning in Python
Using the FeatureSelector for efficient machine learning workflows
Feature Engineering with Tidyverse
Stay up-to-date on the latest data science and AI news in the worlds of artificial intelligence, machine learning, deep learning, implementation, and more.
Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists: 9781491953242: Computer Science Books @ Amazon.com
Best Practices for Feature Engineering
Unsure how to perform feature engineering? Here are 20 best practices and heuristics that will help you engineer great features for machine learning.
Understanding Feature Engineering (Part 3) — Traditional Methods for Text D
Traditional strategies for taming unstructured, textual data
Understanding Feature Engineering (Part 2) — Categorical Data
Strategies for working with discrete, categorical data
Understanding Feature Engineering (Part 1) — Continuous Numeric Data
Strategies for working with continuous, numerical data
A Beginner’s Guide to Data Engineering — Part I – Robert Chang – Medium
Data Engineering: The Close Cousin of Data Science
[D] A Comparison of Six Methods for Missing Data Imputation
2.9M subscribers in the MachineLearning community. Beginners -> /r/mlquestions , AGI -> /r/singularity, career advices -> /r/cscareerquestions…