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
Random subsets of 0.02‑1 % of variables matched or outperformed full and selected feature sets in 28 of 30 high‑dimensional datasets across RNA‑Seq and imaging. Read more: https://getnews.me/random-feature-subsets-challenge-feature-selection-in-high-dimensional-data/ #featureselection #randomsubsets
September 23, 2025 at 12:57 AM
Random subsets of 0.02‑1 % of variables matched or outperformed full and selected feature sets in 28 of 30 high‑dimensional datasets across RNA‑Seq and imaging. Read more: https://getnews.me/random-feature-subsets-challenge-feature-selection-in-high-dimensional-data/ #featureselection #randomsubsets
Batch algorithm uses a PMC penalty and FRBS optimization to prune RL features, dropping irrelevant variables while preserving policy performance on benchmarks. Read more: https://getnews.me/nonconvex-regularization-boosts-feature-selection-in-rl/ #reinforcementlearning #featureselection #nonconvex
September 22, 2025 at 10:23 AM
Batch algorithm uses a PMC penalty and FRBS optimization to prune RL features, dropping irrelevant variables while preserving policy performance on benchmarks. Read more: https://getnews.me/nonconvex-regularization-boosts-feature-selection-in-rl/ #reinforcementlearning #featureselection #nonconvex
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
Identifying Key Predictors of Smoking Cessation Success: Text-Based Feature Selection Using a Large Language Model
Hu, Y., Le, T. T. T. et al.
Paper
Details
#SmokingCessationSuccess #FeatureSelection #LargeLanguageModel
Hu, Y., Le, T. T. T. et al.
Paper
Details
#SmokingCessationSuccess #FeatureSelection #LargeLanguageModel
June 21, 2025 at 9:02 AM
Identifying Key Predictors of Smoking Cessation Success: Text-Based Feature Selection Using a Large Language Model
Hu, Y., Le, T. T. T. et al.
Paper
Details
#SmokingCessationSuccess #FeatureSelection #LargeLanguageModel
Hu, Y., Le, T. T. T. et al.
Paper
Details
#SmokingCessationSuccess #FeatureSelection #LargeLanguageModel
A Hybrid Deep Learning–Based Feature Selection Approach for Supporting Early Detection of Long-Term Behavioral Outcomes in Survivors of Cancer: Cross-Sectional Study #CancerSurvivors #DeepLearning #FeatureSelection #MachineLearning #HealthTech
A Hybrid Deep Learning–Based Feature Selection Approach for Supporting Early Detection of Long-Term Behavioral Outcomes in Survivors of Cancer: Cross-Sectional Study
Background: The number of survivors of cancer is growing, and they often experience negative long-term behavioral outcomes due to cancer treatments. There is a need for better computational methods to handle and predict these outcomes so that physicians and health care providers can implement preventive treatments. Objective: This study aimed to create a new feature selection algorithm to improve the performance of machine learning classifiers to predict negative long-term behavioral outcomes in survivors of cancer. Methods: We devised a hybrid deep learning–based feature selection approach to support early detection of negative long-term behavioral outcomes in survivors of cancer. Within a data-driven, clinical domain–guided framework to select the best set of features among cancer treatments, chronic health conditions, and socioenvironmental factors, we developed a 2-stage feature selection algorithm, that is, a multimetric, majority-voting filter and a deep dropout neural network, to dynamically and automatically select the best set of features for each behavioral outcome. We also conducted an experimental case study on existing study data with 102 survivors of acute lymphoblastic leukemia (aged 15-39 years at evaluation and >5 years postcancer diagnosis) who were treated in a public hospital in Hong Kong. Finally, we designed and implemented radial charts to illustrate the significance of the selected features on each behavioral outcome to support clinical professionals’ future treatment and diagnoses. Results: In this pilot study, we demonstrated that our approach outperforms the traditional statistical and computation methods, including linear and nonlinear feature selectors, for the addressed top-priority behavioral outcomes. Our approach holistically has higher F1, precision, and recall scores compared to existing feature selection methods. The models in this study select several significant clinical and socioenvironmental variables as risk factors associated with the development of behavioral problems in young survivors of acute lymphoblastic leukemia. Conclusions: Our novel feature selection algorithm has the potential to improve machine learning classifiers’ capability to predict adverse long-term behavioral outcomes in survivors of cancer.
dlvr.it
June 18, 2025 at 8:16 PM
A Hybrid Deep Learning–Based Feature Selection Approach for Supporting Early Detection of Long-Term Behavioral Outcomes in Survivors of Cancer: Cross-Sectional Study #CancerSurvivors #DeepLearning #FeatureSelection #MachineLearning #HealthTech
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
The "HMDA" #R #Package is finally out. This package implements #Holistic #Multimodel #Domain #Analysis, a new #machinelearning paradigm for stabilizing the result of #exploratory #analysis, performing #automatic #featureSelection, and finding important #factors and #domains in #bigdata.
March 27, 2025 at 8:10 PM
The "HMDA" #R #Package is finally out. This package implements #Holistic #Multimodel #Domain #Analysis, a new #machinelearning paradigm for stabilizing the result of #exploratory #analysis, performing #automatic #featureSelection, and finding important #factors and #domains in #bigdata.
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