Questions tagged [feature-selection]
Methods and principles of selecting a subset of attributes for use in further modelling
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 |
|:---- ...
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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 ...
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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 ...
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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.
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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.
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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 ...
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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 ...
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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. ...
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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 ...
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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
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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 ...
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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 ...
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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 <...
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integration of Feature Selection in Pipeline
I have noticed integrating feature selection in a pipeline alters results.
Pipeline 1 gives slightly different results with pipeline 2. Why should this be so?
Pipeline 2
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ML feature selection pipeline advice for high dimensionality binary classification problem (UFC WINNER preds!)
I am looking for some advice on how I can improve my predictive accuracy on a binary classification problem for betting on UFC (i.e., predict whether Fighter1 (usually the favorite, or Fighter2 (...
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Unique Feature Selection's for customers using Azure ML Studio
I have this weird kind of requirement that i need for my azure ml model, not sure how to make it, so its that i will provide the model a bunch of data and in that data each row signifies a single ...
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When using Chi-Squrare test in feature selection makes sense?
What are the prerequisites that need to be fulfilled before conducting a chi-square test (Bivariate analysis)? For instance, before having a correlation matrix, we should first ensure linearity. What ...
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how to deal if input feature are all categorical but target feature is discrete numerical, it is giving exact numerical for known data
the following data is to detect malnutrition among children under age 5 and the value_r is the percentage estimate of wasting among the population. should I apply the decision tree as an entirely ...
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Feature Selection - determining the significance of imbalanced categorical data column
I have a dataset with a categorical column that contains three categories. One of the categories represents 98% of the data, while the remaining 2% are distributed between the other two categories, ...
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Effect of removing duplicate and identical entries on dimensionality reduction
I have huge data with thousands of observations and millions of features. I need to do clustering so I use PCA/t-SNE/UMAP for dimensionality reduction followed by K-Means.
Currently, I retain only ...
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Clustering task: drop or not drop a categorical attribute/feature for which each row in the dataset contains a different value
I am dealing with a clustering task. In the dataset I am using there is a categorical feature and for each row in the dataset I have a different value for that feature (my dataset consists of 1000 ...
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How to implement Ant Lion Optimization (ALO) feature selection for KNN Classification Problems?
I have been assigned for a project related to text data classification, i have preprocess and vectorized the data with TF-IDF. For feature selection i am using pyMetahueristic library to implement ALO ...
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RFECV giving same test scores for every feature size
I am trying to reduce the feature space of my training dataset down from the 16000 it currently is before engaging in Random Forest Classification.
Initially I removed all features in less that 5% of ...
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How to handle using input feature (clicks) when it is used in target too?
I am trying to create a ranking model, where I am thinking about creating ground truth based on clicks by user. But at same time past clicks made by users seems like a vital input feature too. Any ...
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Different Algorithms for 50-50 A/B Testing
We are running A/B tests on web app customers, given a customerId. Each customer will see different web-feature designs. Trying to prevent usage of Feature Flags as its not currently setup yet in our ...
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Different patterns in dataset
Suppose some features in a dataset have a linear correlation with the target variable, some have a polynomial correlation, and for categorical features, the target values tend to distribute ...
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Testing accuracy is higher than training accuracy
My testing accuracy is way higher than my training accuracy. I have used feature selection and split the data into training, validation and test sets.
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If my target variable is binary, is it better to use Pearson's or Spearman's for my correlation vector?
I'm using a corr vector, combined with RFE, to perform feature selection.
I keep reading conflicting things online as to whether I should use Pearson's or Spearman's...
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Chi-Sqare test and Continuous values
How does chi-square test work for continuous variables. I see that it is used in most papers to test dependencies between continuous explanatory variables and the target variable. Please how does this ...
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Feature Engineering and Score Generation for a List of Features
I have a decision tree trained for a binary classification task. It uses a model score plus some other attributes as features. It performs well, majorly because the model score is a strong feature ...
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Where do I find dataset for rectangular patch antenna?
I am doing a project in my college, and for that I need a dataset containing the length, width , height along with return loss for different frequency of operation of the rectangular patch antenna. ...
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How to treat single column with both continuous and categorical data for ML model
I am working on financial data where I have a feature(column) with 90% values between 0-1000 (continuous) and 10% values as -1, -2 and -9. (default values)
Default value definition:
-1: data not ...
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important feature selection using dimensionality reduction algorithms
I have a dataset having more than 25000 features. I did perform noise removal using the histogram approach, and this dataset gets reduced to more than 5000 features. There are two classes, healthy and ...
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Feature engineering from HTML & CSS attributes
I have a bunch of HTML files, and inside each there's a particular occurrence of a "substring" which I need to find out. The substring can occur in any tag, and can also have multiple ...
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SequentialFeatureSelector and scoring for clustering
I want to use SequentialFeatureSelector from scikit-learn to do feature selection for clustering (K-Means). [Here][1] is a list of available evaluation scores for clustering. What I do not understand ...
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Feature engineering for interest-based age classification
I have a dataset which has users (rows) with the list of their interests (IABs), which looks like this
...
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Using time serie to predict another variable
I would like to analyse head rotation data in space. For this I measured at 15HZ the rotation around the X, Y and Z angles for a little more than ten minutes. I would like to use these movements to ...
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How is self-supervised learning different from feature selection or using dimensionality reduction
I am getting a bit confused in understanding self-supervised learning and how is it different from normal feature selection method. I understand that in self-supervised learning we are actually ...
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Classification Algorithms for Geospatial Vector Data
I am new to the world of Geospatial Analysis.
As such, I am interested in Machine Learning / Deep Learning techniques for classifying geospatial vector data specifically (as opposed to raster data). I'...
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Should I use Boruta feature selection if I'm comparing two datasets?
I'm a beginner to machine learning and AI so I really could you some guidance on this.
I am currently comparing two different datasets with different classification algorithms. One of the datasets has ...
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Neural networks and input filters
In my use case scenario, I have a neural network that should filter the input and pass a specific value of the input array to the output.
In particular, let's define the input as:
...
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Having problems replicating the results of a series of N LASSOs fit to N datasets in R
I have fit n LASSO Regressions on n different data sets (the 'datasets' object is an R list of length n where each element is a data.table which is a light and fast data frame from the data.table ...
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