Python Feature Selection: Exhaustive Feature Selection | Feature Selection | Python

Показать описание

Python Feature Selection: Exhaustive Feature Selection | Feature Selection | Python

About this video: In this video, you will learn about Exhaustive Feature Selection in python

Large Language Model (LLM) - LangChain

Large Language Model (LLM) - LlamaIndex

Machine Learning Model Deployment

Spark with Python (PySpark)

Data Preprocessing (scikit-learn)

Social Media Links

#datascience #machinelearning #python #ai #ml #deeplearning #opencv #imageprocessing #ai #tensorflow #neuralnetworks #deeplearning #pandas

Рекомендации по теме

Комментарии

Really enjoy the way you teach. First comes the theory, then the practice.
I have one question though. I saw you pass RandomForestClassifier into ExhaustiveFeatureSelector or SequentialFeatureSelector in the previous tutorial. But what if my actual training model is other algorithm, say SVM or neural network, can I still pass RandomForestClassifier to do the feature selection first anyway? Then after I get the reduced features, I can use those to start training my actual model?

leamon

Hello sir, if in our dataset has many features contains categories and continuous like fertilizer recommendation dataset. We can use correlation to interpret it or need more ? ❤❤

reanwithkimleng

great video, is it possible that you share the demo data set used in this video?? I am doing a small presentation and I think your example is perfect for it. Thank you in advance

cyf

Great video. Sir I am not sure how could be assigned to this also the dummy variables for categorical data? This step you did not show because you data has already 0 and 1. Many thanks!

aslivinschi

Sir, in exhaustive feature selection I see most people are using random forest, so my question is `
1. Is the feature selected by random forest is applicable for all other algorithms like SVM, Naïve Bayes, KNN, Logistic regression and decision tree? or the feature selected by random forest is suitable for it only?

2.In exhaustive search method, instead of using radnom forest to chose feature can I use SVM, Naïve Bayes and decision tree???

beautyisinmind

I applied this code, but I will not divide the data for testing and training with the fit function. I entered all the data with X, y is this considered correct

shahadewadh

Python Feature Selection: Exhaustive Feature Selection | Feature Selection | Python

Python Feature Selection: Exhaustive Feature Selection | Feature Selection | Python

10. Exhaustive Feature Selection | Wrapper Method |

Exhaustive Feature Selection | Wrapper Method Part 3 | Tutorial 9

Python Feature Selection: Backward Elimination | Feature Selection | Python

Python Feature Selection: Forward Feature Selection | Feature Selection | Python

FEATURE SELECTION avec SKLEARN (23/30)

Feature Selection for Machine Learning In 5 Mins

Feature Selection | Wrapper | Filter | Embeded Intrinsic Method in Machine Learning by Mahesh Huddar

Feature selection by Python, 20211013

13.4.5 Sequential Feature Selection -- Code Examples (L13: Feature Selection)

Feature Selection in R programming | Stepwise Regression | Machine Learning | Data Science

Step Forward, Step Backward and Exhaustive Feature Selection of Wrapper Method

Feature Selection in Python | Machine Learning Basics | Boston Housing Data

Wrapper methods in Feature Selection Techniques | Machine Learning Concepts

13.4.4 Sequential Feature Selection (L13: Feature Selection)

Recursive Feature Elimination (RFE) | Feature Selection | Python

Machine Learning Project - Feature Selection and Extraction

Session 55 - Feature Selection Part 2 | Wrapper Methods | DSMP 2023

198 - Feature selection using Boruta in python

Machine Learning with Python (Part 8) - Feature Engineering, Feature Extraction & Feature Select...

Python Feature Selection: Chi Square to Select Features | Machine Learning | Python

8. Forward Feature Selection | Wrapper Method |

Learn Feature selection in Machine Learning | Feature Selection Techniques with Examples | Edureka

Feature Selection En Python - Approches Automatiques et Approches Intégrées