Skip to main content
Share Your Experience: Take the 2024 Developer Survey

Questions tagged [feature-selection]

Methods and principles of selecting a subset of attributes for use in further modelling

Filter by
Sorted by
Tagged with
0 votes
0 answers
9 views

Estimating the Increase in Rademacher Complexity after Feature Selection

I'm trying to estimate how much the Rademacher complexity (or empirical Rademacher complexity) increases when performing feature selection using methods like Sequential Forward Selection or Genetic ...
x H's user avatar
  • 1
2 votes
0 answers
22 views

How to properly select features for time series ML models

I've been trying to get good references on how to solve a problem that's been bothering me regarding the modelling techniques I've used. I'm currently interested in making forecasts using ML for ...
loguimaraes's user avatar
0 votes
1 answer
20 views

how to handle a variable number of feature-values (1:many) without one-hot

I am using Catboost and one thing I notice in the guide is that it says to not preprocess to one-hot encoding. My data has a single target per row however the feature can have both thousands of values ...
tuj's user avatar
  • 101
0 votes
0 answers
14 views

Whats a suitable feature selection method for Time series data across multiple files?

My problem is basically a higher dimensional regression, where my input is (100 levels, 300 timesteps, 23 features) My goal is to build a deep learning LSTM model that finds which level the data ...
Youssef Badr's user avatar
0 votes
1 answer
14 views

Data splitting for OLS regression

This is what I have done :: divided my dataset into training and testing sets--> got significant features via. feature selection using sequential feature selector ( MLxtend) on the training set--&...
pomelo's user avatar
  • 1
1 vote
1 answer
33 views

Feature selection in binary classification

I have a dataset with two classes and am interested in learning which features are 'important' for predicting the class. There are a lot of features available and I want to find subset(s) that lead to ...
Shawn's user avatar
  • 35
0 votes
0 answers
17 views

Variable Selection and model prediction

In a supervised problem, I used randomForest for variable selection to identify the most important features. Question: am I required to use a random forest model for subsequent predictions, or can I ...
Zakaria Faouzi's user avatar
0 votes
1 answer
19 views

Missing data in train set and test set

I have a dataset of N columns. Now I'm able to preprocess data and find a subset of features that I can use to train a model and make predictions. In the case where the train data has missing feature ...
0-0's user avatar
  • 1
0 votes
0 answers
18 views

RFECV with Random Stratified K-Folds returns different features everytime

I am trying to learn more about Feature Selection in machine learning. I am working on a dataset that contains 17 features, and I have achieved about 75% accuracy on a Random Forest model with no ...
rehanqb's user avatar
0 votes
0 answers
8 views

Feature Selection in no labeled data

I'm new to this field and trying to learn by working with a fraud dataset. Initially, I used the dataset as is, but now I'm trying unsupervised learning without the labels. I've tried clustering ...
DrGenius's user avatar
  • 101
0 votes
0 answers
22 views

Questions about the process of feature selection through feature importance

'Shap feature importance' was obtained through xgboost, and variables with the lowest feature importance were removed one by one from 50 variables until only 1 variable remained. As a result of ...
JAE's user avatar
  • 13
2 votes
1 answer
81 views

Why would having less features work better for LASSO in test?

Given a large set approx 1K features (2K samples), I am trying to find a good regression for my independant variable. As the features are very correlated, I have been drawn to using LASSO. Which does ...
ManInMoon's user avatar
0 votes
0 answers
8 views

Best subset selection includes predictor with high p-value

I'm trying to use subset selection to find out the best model according to AIC. However, it is recommending a variable with a p-value > .9. My guess is that this is because the subset selection ...
cardinalcat27's user avatar
0 votes
0 answers
32 views

Classifying Players as winners or losers

I have a dataset that I curated from a game that I play. There are currently 130 instances (i.e. players) and an innumerable number of features. Experience tells me <10 features would be sufficient....
Shawn's user avatar
  • 35
0 votes
0 answers
14 views

Are there rules of thumb for which feature selection method to use?

I've recently started playing around with Datasets on Kaggle and am struggling to bring structure into the feature selection part of my predictive model building process. I end up trying most or all ...
user3709's user avatar
  • 101
0 votes
0 answers
8 views

Unsupervised Post-Clustering Feature Selection - Laplacian Score

I know that feature selection methods such as the Laplacian score or Fisher score are typically used for dimension reduction prior to clustering, but is there any reason why the same methods couldn't ...
EB3112's user avatar
  • 203
1 vote
1 answer
51 views

How does a Decision Tree split when two features are tied?

Decision Trees split based on which feature and which cut-off value creates the largest mean decrease in impurity (assuming hyperparameter split="best", criterion="gini"). Now take ...
AvanishM's user avatar
0 votes
1 answer
36 views

Enhance clustering with evaluation function

My goal is to partition a dataset (X) in distinct clusters. I'm using k-means to be able to pick the center of each cluster assuming all other datapoints behave the ...
acocado's user avatar
0 votes
0 answers
41 views

What feature selection method is best for a multi class classification problem with one-hot-encoded columns?

I am trying to solve a multi-class classification involving prediction the outcome of a football match (target variable = Win, Lose or Draw). With a dataset of 2280 rows, which is 6 seasons of ...
pastybake2002's user avatar
0 votes
1 answer
34 views

Effect of feature selection when coupled with XGB models

I ran Boruta feature selection prior to XGB training\testing step and didn't see any difference, although ~30/200 features were rejected prior to going into the training. Can it be that internal ...
Andrey's user avatar
  • 1
0 votes
0 answers
9 views

How to find the minimum data point that predicts the target class in longitudinal data

I am working on medical data where a screening is done regularly for 200 days. I need to know the minimum number of screenings that can predict the outcome. I also need to know the best time/times to ...
Ghof-90's user avatar
0 votes
0 answers
20 views

Random Forest Classifier Removing Features using Top-N Features Method

I am a new-comer to data science and machine learning techniques and processes. I'm working on a personal project that predicts the winner of an NBA game using a random forest classifier. I have ...
Vishnu Vennelakanti's user avatar
0 votes
0 answers
37 views

What feature selection method is ideal for a large dimensional data frame after the result of one hot encoding?

I am trying to solve a sports related multi class classification problem in Python, I aim to train a custom neural network and also a SVM. I have performed prior data cleaning and encoded my data ...
pastybake2002's user avatar
1 vote
0 answers
134 views

Feature importance using random forest vs. SHAP

I recently came across SHAP while looking for feature-importance methods. To use SHAP, first a model needs to be created, and then based on the predictions made by the model, SHAP values are ...
lsr729's user avatar
  • 183
0 votes
0 answers
21 views

How can I quantity feature importance while performing unsupervised clustering with mixed data types?

I wanted to cluster data points into 2 clusters, I am using clustmix package from R I wanted to understand importance of each of the feature, I have 203 features. I have tried featureimp package from ...
ashish pople's user avatar
0 votes
2 answers
54 views

Encode 10k features where each feature is having more than 500 categories

I have around 10k features in my dataset and each feature is having more than 500 categories. what is the best encoding method to convert this categorical features to vector form? "span_dir":...
khushi's user avatar
  • 111
0 votes
0 answers
9 views

Deriving consistency feature for a student using a study app over the days

I want to build a recommendation engine for the revision app. Basic Idea After each module we will ask student questions and based on the correctness of their answers we will decide after how many ...
Keshav Raj's user avatar
0 votes
0 answers
20 views

Alternatives to Model-Based Feature Selection for Unsupervised Clustering

I am running a clustering model on a group of patients who are hypertensive with hopes of identifying different variations in clinical characteristics among hypertensive individuals. One of the issues ...
Zory Dory's user avatar
0 votes
0 answers
85 views

How to do feature selection correctly in xgboost for time series forecasting after obtaining a good predictive model?

I have a very large dataset (~7 million rows) for which I have extracted ~500 features during feature engineering phase. I have trained an XGBoost which has a fairly good predictive capability (based ...
guestar's user avatar
2 votes
1 answer
93 views

Multiple Hypothesis Testing in feature selection process

I am doing feature selection of features which are of binary nature i.e. each feature represents presence or absence of a substructure in a molecule. And I have a target variable of two classes. My ...
As13's user avatar
  • 77
0 votes
1 answer
24 views

Is there a standard data science workflow/decision tree?

I'm looking for some kind of reference that essentially shows an example of an entire data analysis workflow beginning with feature engineering, and ending with analyzing the results. I know the ...
David's user avatar
  • 101
0 votes
0 answers
67 views

Feature selection: ANOVA between features vs within a feature

I am currently performing feature selection on a dataset containing continuous and categorical features. The target is a continuous variable. If I understand properly, ANOVA can be used between ...
Fred vh's user avatar
0 votes
0 answers
15 views

Feature selection on datasets with both categorical and numerical features

I'm proposing a novel methodology for feature selection in the context of tabular datasets that contain both numerical and categorical features. In order to prove the efficacy of my methodology, I ...
Francesco De Santis's user avatar
1 vote
0 answers
36 views

sklearn - OneHotEncoding and SelectPercintile

in sklearn example there is a code ...
Maciej778's user avatar
0 votes
0 answers
22 views

Feature selection for siamese network

I have a regression problem for which two observations are compared by a siamese-like Multilayer Perceptron. Each observation 'O' is described by a feature vector 'X' of a certain number 'N' of ...
Febo Cardelli's user avatar
0 votes
0 answers
10 views

Feature selection methods when input data is continuous but target variable is categorical

I plan on extracting features from a univariate time series, and use a feature selection method to select relevant features to predict a binary target variable via logistic regression. But, I have 2 ...
user155775's user avatar
0 votes
0 answers
14 views

How is it called when instead of creating predective models finding patterns in observed data (ML) you tried to guess the model theorically...?

I'm a college student appasionated of machine learning and I've decided to my bachelor thesis about it. I thought that as an interesting introduction to machine learning, I could introduce it by ...
ADayWithoutRain's user avatar
1 vote
1 answer
45 views

Feature selection for propensity model

I'm trying to build a propensity model for whether or not a customer will buy a second product. I was given data that looks like this: | Age | Income | DaysSince1stPurchase | Bought2ndProduct | |:---- ...
BlueSkyz's user avatar
0 votes
1 answer
18 views

How to represent facial features from video and classify high/low personality traits from facial features?

The dataset has 3-minute 30fps video conversations (no audio) of 150 extroverted and 150 introverted individuals. The goal is to classify them as "introverts" or "extroverts" based ...
TheBiometricsGuy's user avatar
0 votes
0 answers
10 views

Simultaneous Feature Selection and Time Series Selection

I have about 2000 features and 14 times series for them to predict. I am trying to reduce my feature count, but also my time series count. The goal is to find the 30 core features best at predicting ...
David's user avatar
  • 1
0 votes
0 answers
20 views

Why is the feature direction chosen in the direction associated with largest eigenvalue of $Σ_T$ in case of more than two classes?

Why is the feature direction chosen in the direction associated with largest eigenvalue of $Σ_T$ in case of more than two classes? Please see the following.
DSPinfinity's user avatar
0 votes
1 answer
63 views

Unclear points on projection type and selection of distance metric in feature extraction for a set of scenarios

The following is an example from a book (An Introduction to Pattern Recognition and Machine Learning by P. Fieguth, page 85) on feature extraction and selection. Please consider the following figure. ...
user154739's user avatar
0 votes
1 answer
21 views

Approaches to dataset, whose elements have different size

I am working with a dataset where each elements is a square table of size m-by-n, where m (the number of rows) is the same for all the data points, while n (the number of columns) varies from tens to ...
Roger V.'s user avatar
  • 103
0 votes
0 answers
13 views

Optimal method for predicting outcome from many additive, correlated, and sparse features?

Suppose I have many vectors which can take on any of three values, 0, 1, 2. These vectors affect an outcome being predicted, Y. Vectors add together: a vector "A" of the value 2 has twice ...
BigMistake's user avatar
1 vote
2 answers
119 views

Feature selection - How to identify the best subset

I am using three feature selection method on a dataset containing 15 inputs. I need to extract the best 5 features. Each of the three method gave a subset of the input dataset, but they are different. ...
Aparna's user avatar
  • 11
0 votes
0 answers
19 views

Variable selections for Mixed data(categorical and numerical)

I have a dataset I want to do a logistic regression on. The data contains some numerical data and categorical data. The categorical data have many different values. My approach is to do a one-hot ...
Jaquelin's user avatar
0 votes
1 answer
421 views

Feature selection / missing values

What are the top (including new, if any) algorithms to perform features selections without removing or altering the missing data points ? Thanks
Zak's user avatar
  • 1
0 votes
0 answers
13 views

Can we apply cross validation bias correction to explanatory variable selection as well?

As shown in the paper below, several methods have been suggested for bias correction for cross validation. For example, Tibshirani-Tibshirani method and BBC-CV method. These are known to significantly ...
x H's user avatar
  • 1
1 vote
2 answers
4k views

How many features is too many when using feature selection methods?

Now obviously there is no such thing as an ideal number as every problem is different, but I've been Googling, ChatGPTing, & Youtubing this question for a few days now and I am constantly getting ...
T3nt4c135's user avatar
0 votes
2 answers
39 views

Сan a subset of features perform better than the base set?

I have a random forest model which is trained on 100 features for example. After doing feature selection using the same model, let's say I now have 40 features and I name the subset of features as <...
mr.T's user avatar
  • 103

1
2 3 4 5
20