LLMs in feature selection: classical tools win on accuracy, but the real story is deception—give the model shuffled feature names and it #LLMs #FeatureSelection #MachineLearning #DataScience
https://freegardner.com/synapse/the-feature-names-were-right-the-columns-were-not.html
https://freegardner.com/synapse/the-feature-names-were-right-the-columns-were-not.html
The Feature Names Were Right. The Columns Were Not.
The Feature Names Were Right. The Columns Were Not.
freegardner.com
October 22, 2026 at 12:22 AM
LLMs in feature selection: classical tools win on accuracy, but the real story is deception—give the model shuffled feature names and it #LLMs #FeatureSelection #MachineLearning #DataScience
https://freegardner.com/synapse/the-feature-names-were-right-the-columns-were-not.html
https://freegardner.com/synapse/the-feature-names-were-right-the-columns-were-not.html
Why you should start domain research before feature selection #data #dataanalytics #python #frauddetection #featureselection
October 19, 2026 at 8:01 AM
Why you should start domain research before feature selection #data #dataanalytics #python #frauddetection #featureselection
SelectArena is a new benchmark for feature selection on real-world tabular data, addressing limitations of outdated models and artificial datasets in prior evaluations.
#MachineLearning #FeatureSelection #TabularData #AIResearch
https://arxiv.org/abs/2610.13644
#MachineLearning #FeatureSelection #TabularData #AIResearch
https://arxiv.org/abs/2610.13644
October 12, 2026 at 2:01 PM
SelectArena is a new benchmark for feature selection on real-world tabular data, addressing limitations of outdated models and artificial datasets in prior evaluations.
#MachineLearning #FeatureSelection #TabularData #AIResearch
https://arxiv.org/abs/2610.13644
#MachineLearning #FeatureSelection #TabularData #AIResearch
https://arxiv.org/abs/2610.13644
Can we use statistical tests to select features? 🤔
Turns out, we can! 🎉
In the slides below, we’ll explore the most commonly used statistical tests for feature selection, along with their advantages and limitations. 👇
#machinelearning #datascience #featureselection
Turns out, we can! 🎉
In the slides below, we’ll explore the most commonly used statistical tests for feature selection, along with their advantages and limitations. 👇
#machinelearning #datascience #featureselection
August 19, 2025 at 4:02 PM
Can we use statistical tests to select features? 🤔
Turns out, we can! 🎉
In the slides below, we’ll explore the most commonly used statistical tests for feature selection, along with their advantages and limitations. 👇
#machinelearning #datascience #featureselection
Turns out, we can! 🎉
In the slides below, we’ll explore the most commonly used statistical tests for feature selection, along with their advantages and limitations. 👇
#machinelearning #datascience #featureselection
Feature selection is key to robust Machine Learning models.
Feature-engine helps you pick the best features, from removing duplicates to advanced methods like permutation.
Watch how to optimize your models here!
📹https://f.mtr.cool/ljhsellcdv
#MachineLearning #featureselection
Feature-engine helps you pick the best features, from removing duplicates to advanced methods like permutation.
Watch how to optimize your models here!
📹https://f.mtr.cool/ljhsellcdv
#MachineLearning #featureselection
Enjoy the videos and music that you love, upload original content and share it all with friends, family and the world on YouTube.
f.mtr.cool
July 23, 2025 at 4:02 PM
Feature selection is key to robust Machine Learning models.
Feature-engine helps you pick the best features, from removing duplicates to advanced methods like permutation.
Watch how to optimize your models here!
📹https://f.mtr.cool/ljhsellcdv
#MachineLearning #featureselection
Feature-engine helps you pick the best features, from removing duplicates to advanced methods like permutation.
Watch how to optimize your models here!
📹https://f.mtr.cool/ljhsellcdv
#MachineLearning #featureselection
📘 Feature Selection in Machine Learning – 2nd Edition
The only book fully dedicated to feature selection.
Learn how to choose the right features for simpler, faster, and more interpretable models.
👉 Our book: https://f.mtr.cool/ydrvbttjdh
#FeatureSelection #MachineLearning
The only book fully dedicated to feature selection.
Learn how to choose the right features for simpler, faster, and more interpretable models.
👉 Our book: https://f.mtr.cool/ydrvbttjdh
#FeatureSelection #MachineLearning
July 9, 2025 at 4:02 PM
📘 Feature Selection in Machine Learning – 2nd Edition
The only book fully dedicated to feature selection.
Learn how to choose the right features for simpler, faster, and more interpretable models.
👉 Our book: https://f.mtr.cool/ydrvbttjdh
#FeatureSelection #MachineLearning
The only book fully dedicated to feature selection.
Learn how to choose the right features for simpler, faster, and more interpretable models.
👉 Our book: https://f.mtr.cool/ydrvbttjdh
#FeatureSelection #MachineLearning
When training #machinelearning models, which features actually matter? 🧠
A quick way to find out is by training single-feature models.
🎥 Watch: https://tinyurl.com/yc7aje5r
📚 Learn more: https://www.trainindata.com/p/feature-selection-for-machine-learning
#featureselection
A quick way to find out is by training single-feature models.
🎥 Watch: https://tinyurl.com/yc7aje5r
📚 Learn more: https://www.trainindata.com/p/feature-selection-for-machine-learning
#featureselection
June 16, 2025 at 4:02 PM
When training #machinelearning models, which features actually matter? 🧠
A quick way to find out is by training single-feature models.
🎥 Watch: https://tinyurl.com/yc7aje5r
📚 Learn more: https://www.trainindata.com/p/feature-selection-for-machine-learning
#featureselection
A quick way to find out is by training single-feature models.
🎥 Watch: https://tinyurl.com/yc7aje5r
📚 Learn more: https://www.trainindata.com/p/feature-selection-for-machine-learning
#featureselection
Spending hours researching the best practices for feature selection? ⏲️
If so, watch this video 📹 to see how this course can help you build simpler, faster, and more robust machine learning models. 👇
📹https://tinyurl.com/n6fyd6ry
#machinelearning #featureselection #datascience
If so, watch this video 📹 to see how this course can help you build simpler, faster, and more robust machine learning models. 👇
📹https://tinyurl.com/n6fyd6ry
#machinelearning #featureselection #datascience
Enjoy the videos and music that you love, upload original content and share it all with friends, family and the world on YouTube.
tinyurl.com
June 11, 2025 at 4:02 PM
Spending hours researching the best practices for feature selection? ⏲️
If so, watch this video 📹 to see how this course can help you build simpler, faster, and more robust machine learning models. 👇
📹https://tinyurl.com/n6fyd6ry
#machinelearning #featureselection #datascience
If so, watch this video 📹 to see how this course can help you build simpler, faster, and more robust machine learning models. 👇
📹https://tinyurl.com/n6fyd6ry
#machinelearning #featureselection #datascience
Beyond filter, wrapper and embedded methods, there is a whole world of feature selection algorithms.
Good news is... most of them are available in Feature-engine.
You can find the link in the comment below 👇
https://tinyurl.com/288yhd7d
#featureselection #featureengine
Good news is... most of them are available in Feature-engine.
You can find the link in the comment below 👇
https://tinyurl.com/288yhd7d
#featureselection #featureengine
June 9, 2025 at 4:02 PM
Beyond filter, wrapper and embedded methods, there is a whole world of feature selection algorithms.
Good news is... most of them are available in Feature-engine.
You can find the link in the comment below 👇
https://tinyurl.com/288yhd7d
#featureselection #featureengine
Good news is... most of them are available in Feature-engine.
You can find the link in the comment below 👇
https://tinyurl.com/288yhd7d
#featureselection #featureengine
With Feature-engine’s SelectByTargetMeanPerformance, you can:
✅ Encode categorical variables
✅ Transform numerical features
✅ Select the most predictive ones
Watch the video and learn how to apply this technique to your ML models!👇
https://tinyurl.com/2w5ntwyz
#featureselection #machinelearning
✅ Encode categorical variables
✅ Transform numerical features
✅ Select the most predictive ones
Watch the video and learn how to apply this technique to your ML models!👇
https://tinyurl.com/2w5ntwyz
#featureselection #machinelearning
April 30, 2025 at 10:01 AM
With Feature-engine’s SelectByTargetMeanPerformance, you can:
✅ Encode categorical variables
✅ Transform numerical features
✅ Select the most predictive ones
Watch the video and learn how to apply this technique to your ML models!👇
https://tinyurl.com/2w5ntwyz
#featureselection #machinelearning
✅ Encode categorical variables
✅ Transform numerical features
✅ Select the most predictive ones
Watch the video and learn how to apply this technique to your ML models!👇
https://tinyurl.com/2w5ntwyz
#featureselection #machinelearning
A strong feature subset is highly correlated with the target but not with each other.
This thesis dives deep into correlation-based feature selection and likely inspired methods like MRMR.
Want to learn more? Check it out!👇
📄https://tinyurl.com/2nhjr3x2
#featureselection #machinelearning #ml
This thesis dives deep into correlation-based feature selection and likely inspired methods like MRMR.
Want to learn more? Check it out!👇
📄https://tinyurl.com/2nhjr3x2
#featureselection #machinelearning #ml
April 25, 2025 at 10:01 AM
A strong feature subset is highly correlated with the target but not with each other.
This thesis dives deep into correlation-based feature selection and likely inspired methods like MRMR.
Want to learn more? Check it out!👇
📄https://tinyurl.com/2nhjr3x2
#featureselection #machinelearning #ml
This thesis dives deep into correlation-based feature selection and likely inspired methods like MRMR.
Want to learn more? Check it out!👇
📄https://tinyurl.com/2nhjr3x2
#featureselection #machinelearning #ml
One way to rank variables is by testing a classifier or regression model on each one individually. The image shows how it works!
Want to dive deeper? Check out my book for a detailed breakdown with step-by-step procedures with illustrations. 📖✨
https://tinyurl.com/yc6sxvvu
#featureselection
Want to dive deeper? Check out my book for a detailed breakdown with step-by-step procedures with illustrations. 📖✨
https://tinyurl.com/yc6sxvvu
#featureselection
April 23, 2025 at 10:01 AM
One way to rank variables is by testing a classifier or regression model on each one individually. The image shows how it works!
Want to dive deeper? Check out my book for a detailed breakdown with step-by-step procedures with illustrations. 📖✨
https://tinyurl.com/yc6sxvvu
#featureselection
Want to dive deeper? Check out my book for a detailed breakdown with step-by-step procedures with illustrations. 📖✨
https://tinyurl.com/yc6sxvvu
#featureselection
Watch how Recursive Feature Addition with Feature-engine optimize feature selection! We build a classifier, derive feature importance & select the best features—1 at a time—using cross-validation for better accuracy. 📊✨
📹https://tinyurl.com/mrxskrr2
#MachineLearning #FeatureSelection #ml
📹https://tinyurl.com/mrxskrr2
#MachineLearning #FeatureSelection #ml
Enjoy the videos and music that you love, upload original content and share it all with friends, family and the world on YouTube.
tinyurl.com
April 17, 2025 at 10:02 AM
Watch how Recursive Feature Addition with Feature-engine optimize feature selection! We build a classifier, derive feature importance & select the best features—1 at a time—using cross-validation for better accuracy. 📊✨
📹https://tinyurl.com/mrxskrr2
#MachineLearning #FeatureSelection #ml
📹https://tinyurl.com/mrxskrr2
#MachineLearning #FeatureSelection #ml
Can we use statistical tests to select features? 🤔
Turns out, we can! 🎉
In the slides below, we’ll explore the most commonly used statistical tests for feature selection—along with their advantages and limitations. 👇
More resources - my book 📘 https://tinyurl.com/yc6sxvvu
#featureselection
Turns out, we can! 🎉
In the slides below, we’ll explore the most commonly used statistical tests for feature selection—along with their advantages and limitations. 👇
More resources - my book 📘 https://tinyurl.com/yc6sxvvu
#featureselection
April 14, 2025 at 10:01 AM
Can we use statistical tests to select features? 🤔
Turns out, we can! 🎉
In the slides below, we’ll explore the most commonly used statistical tests for feature selection—along with their advantages and limitations. 👇
More resources - my book 📘 https://tinyurl.com/yc6sxvvu
#featureselection
Turns out, we can! 🎉
In the slides below, we’ll explore the most commonly used statistical tests for feature selection—along with their advantages and limitations. 👇
More resources - my book 📘 https://tinyurl.com/yc6sxvvu
#featureselection
Picking the right features is key to building a strong, interpretable #ML model. #Featureselection helps cut through the noise, improving accuracy and resilience.
In this blog, you’ll learn filter-based selection methods, how they work, and when to use them.👇
https://tinyurl.com/2a2u7xa4
In this blog, you’ll learn filter-based selection methods, how they work, and when to use them.👇
https://tinyurl.com/2a2u7xa4
April 9, 2025 at 10:01 AM
Picking the right features is key to building a strong, interpretable #ML model. #Featureselection helps cut through the noise, improving accuracy and resilience.
In this blog, you’ll learn filter-based selection methods, how they work, and when to use them.👇
https://tinyurl.com/2a2u7xa4
In this blog, you’ll learn filter-based selection methods, how they work, and when to use them.👇
https://tinyurl.com/2a2u7xa4
Beyond filter, wrapper and embedded methods, there is a whole world of feature selection algorithms.
Good news is... most of them are available in Feature-engine. 👍
https://buff.ly/45uNPAW
#featureselection #featureengine #machinelearning #datascience #dataengineering #mlmodels #ml #ai #algorithms
Good news is... most of them are available in Feature-engine. 👍
https://buff.ly/45uNPAW
#featureselection #featureengine #machinelearning #datascience #dataengineering #mlmodels #ml #ai #algorithms
April 7, 2025 at 10:02 AM
Beyond filter, wrapper and embedded methods, there is a whole world of feature selection algorithms.
Good news is... most of them are available in Feature-engine. 👍
https://buff.ly/45uNPAW
#featureselection #featureengine #machinelearning #datascience #dataengineering #mlmodels #ml #ai #algorithms
Good news is... most of them are available in Feature-engine. 👍
https://buff.ly/45uNPAW
#featureselection #featureengine #machinelearning #datascience #dataengineering #mlmodels #ml #ai #algorithms
#Featureselection with Feature-engine made easy! 😊
I’ve put together a playlist so you can learn to:
✨ Drop constant & quasi-constant features
✨ Remove duplicates with Feature-engine & Scikit-learn
✨ Pick the most predictive features and more...
Watch now 👉📹 https://tinyurl.com/ywppy8j2
I’ve put together a playlist so you can learn to:
✨ Drop constant & quasi-constant features
✨ Remove duplicates with Feature-engine & Scikit-learn
✨ Pick the most predictive features and more...
Watch now 👉📹 https://tinyurl.com/ywppy8j2
April 2, 2025 at 10:01 AM
#Featureselection with Feature-engine made easy! 😊
I’ve put together a playlist so you can learn to:
✨ Drop constant & quasi-constant features
✨ Remove duplicates with Feature-engine & Scikit-learn
✨ Pick the most predictive features and more...
Watch now 👉📹 https://tinyurl.com/ywppy8j2
I’ve put together a playlist so you can learn to:
✨ Drop constant & quasi-constant features
✨ Remove duplicates with Feature-engine & Scikit-learn
✨ Pick the most predictive features and more...
Watch now 👉📹 https://tinyurl.com/ywppy8j2
You can now implement MRMR out of the box with Feature-engine.
More details about how the method works in Feature-engine's documentation.👇
https://bit.ly/3ULpao9
#featureselection #datascience #dataengineering #AI #machinelearning #ML
More details about how the method works in Feature-engine's documentation.👇
https://bit.ly/3ULpao9
#featureselection #datascience #dataengineering #AI #machinelearning #ML
March 31, 2025 at 10:01 AM
You can now implement MRMR out of the box with Feature-engine.
More details about how the method works in Feature-engine's documentation.👇
https://bit.ly/3ULpao9
#featureselection #datascience #dataengineering #AI #machinelearning #ML
More details about how the method works in Feature-engine's documentation.👇
https://bit.ly/3ULpao9
#featureselection #datascience #dataengineering #AI #machinelearning #ML
More features aren’t always better! While adding variables won’t necessarily hurt accuracy, using fewer, well-chosen features comes with big benefits. 🙂
Want to dive deeper? Check out the details here:👇
https://www.trainindata.com/p/feature-selection-in-machine-learning-book
#featureselection #ml
Want to dive deeper? Check out the details here:👇
https://www.trainindata.com/p/feature-selection-in-machine-learning-book
#featureselection #ml
March 26, 2025 at 11:15 AM
More features aren’t always better! While adding variables won’t necessarily hurt accuracy, using fewer, well-chosen features comes with big benefits. 🙂
Want to dive deeper? Check out the details here:👇
https://www.trainindata.com/p/feature-selection-in-machine-learning-book
#featureselection #ml
Want to dive deeper? Check out the details here:👇
https://www.trainindata.com/p/feature-selection-in-machine-learning-book
#featureselection #ml
This is the go to review to know more about feature selection for machine learning.
I am yet to find a better article than this one, even though it is from 2003.
If you are new to feature selection, this is your starting point. 👍
https://dl.acm.org/doi/pdf/10.5555/944919.944968
#featureselection
I am yet to find a better article than this one, even though it is from 2003.
If you are new to feature selection, this is your starting point. 👍
https://dl.acm.org/doi/pdf/10.5555/944919.944968
#featureselection
March 24, 2025 at 11:15 AM
This is the go to review to know more about feature selection for machine learning.
I am yet to find a better article than this one, even though it is from 2003.
If you are new to feature selection, this is your starting point. 👍
https://dl.acm.org/doi/pdf/10.5555/944919.944968
#featureselection
I am yet to find a better article than this one, even though it is from 2003.
If you are new to feature selection, this is your starting point. 👍
https://dl.acm.org/doi/pdf/10.5555/944919.944968
#featureselection
🚀#Featureselection made easy!
In this video, we show how to use Recursive Feature Addition with Feature-engine to find the best features for your model.
Watch as we build a classifier, assess feature importance, and improve performance—one feature at a time!👇
https://bit.ly/4kmYLIA
In this video, we show how to use Recursive Feature Addition with Feature-engine to find the best features for your model.
Watch as we build a classifier, assess feature importance, and improve performance—one feature at a time!👇
https://bit.ly/4kmYLIA
March 20, 2025 at 11:10 AM
🚀#Featureselection made easy!
In this video, we show how to use Recursive Feature Addition with Feature-engine to find the best features for your model.
Watch as we build a classifier, assess feature importance, and improve performance—one feature at a time!👇
https://bit.ly/4kmYLIA
In this video, we show how to use Recursive Feature Addition with Feature-engine to find the best features for your model.
Watch as we build a classifier, assess feature importance, and improve performance—one feature at a time!👇
https://bit.ly/4kmYLIA
New to feature selection? Want to learn what methods are out there?
Then check out one of the iconic reviews on feature selection for machine learning.
A great way to get an overview, for free. 👇
https://tinyurl.com/3hv8fwjd
#featureselection #machinelearning #datascience #dataengineering
Then check out one of the iconic reviews on feature selection for machine learning.
A great way to get an overview, for free. 👇
https://tinyurl.com/3hv8fwjd
#featureselection #machinelearning #datascience #dataengineering
March 10, 2025 at 11:01 AM
New to feature selection? Want to learn what methods are out there?
Then check out one of the iconic reviews on feature selection for machine learning.
A great way to get an overview, for free. 👇
https://tinyurl.com/3hv8fwjd
#featureselection #machinelearning #datascience #dataengineering
Then check out one of the iconic reviews on feature selection for machine learning.
A great way to get an overview, for free. 👇
https://tinyurl.com/3hv8fwjd
#featureselection #machinelearning #datascience #dataengineering
L1 vs L2 for feature selection—what’s the difference? 🤔
L1 and L2 regularization serve unique purposes in feature selection. Want to know why?
Check out the slides to find out!
#MachineLearning #DataScience #FeatureSelection #L1Regularization #L2Regularization #AI #ML
L1 and L2 regularization serve unique purposes in feature selection. Want to know why?
Check out the slides to find out!
#MachineLearning #DataScience #FeatureSelection #L1Regularization #L2Regularization #AI #ML
March 5, 2025 at 11:02 AM
L1 vs L2 for feature selection—what’s the difference? 🤔
L1 and L2 regularization serve unique purposes in feature selection. Want to know why?
Check out the slides to find out!
#MachineLearning #DataScience #FeatureSelection #L1Regularization #L2Regularization #AI #ML
L1 and L2 regularization serve unique purposes in feature selection. Want to know why?
Check out the slides to find out!
#MachineLearning #DataScience #FeatureSelection #L1Regularization #L2Regularization #AI #ML
Adaptive node feature selection lets GNNs prune attributes during training, keeping only the most informative ones; tests on GCN and GAT dropped many features without hurting performance. Read more: https://getnews.me/adaptive-feature-selection-enhances-graph-neural-networks/ #gnn #featureselection
October 6, 2025 at 10:07 AM
Adaptive node feature selection lets GNNs prune attributes during training, keeping only the most informative ones; tests on GCN and GAT dropped many features without hurting performance. Read more: https://getnews.me/adaptive-feature-selection-enhances-graph-neural-networks/ #gnn #featureselection
BoMGene merges Boruta and mRMR, evaluated on 25 gene‑expression datasets, cutting feature count while keeping or improving classification accuracy. https://getnews.me/bomgene-hybrid-feature-selection-boosts-gene-expression-classification/ #bomgene #featureselection
October 3, 2025 at 2:04 AM
BoMGene merges Boruta and mRMR, evaluated on 25 gene‑expression datasets, cutting feature count while keeping or improving classification accuracy. https://getnews.me/bomgene-hybrid-feature-selection-boosts-gene-expression-classification/ #bomgene #featureselection