Questions tagged [feature-selection]

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

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Can ReliefF implicitly predict non-linear correlation between features and targets?

Let's assume that we have a collection of instances with their features and already labelled and train them for Relief/ReliefF classifier. If the targets themselves have parameters that defined ...
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Using variance Threshold removes all my features (clustering)

I have 100+ features for clustering. I am unfamiliar with unsupervised tehcniques for clustering. I have heard of variance threshold. However the features i have used have low variance.. e.g. some ...
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Explanation of random forest performance difference to when using categories and when using dummy variables

I have some hand coded feature which is a category with values "High", "Low", and "Normal". I created this feature myself and my problem performance (classification) ...
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How can i decide on which features to use for clustering?

I am clustering on a dataset where each row is a customer and each column is a feature. I have 200 features, this seems like alot for clustering. I plan to experiment with a variety of clustering ...
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What feature selection technique would you recommend for selecting many features (in the thousands)?

I have a data set that has a large number of features (~8k features) and I want to limit the number of features so my model does not overfit but performs relatively well. I have mix of categorical and ...
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How to find out what portions of an image is helping CNN to classify it

I am working on an image classification problem using Transfer learning. Right now, I am getting an accuracy of 75% on train data and 67% in test data. Now I want to understand what portions/parts of ...
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Can data science be used to detect fileless malware cyber attacks? [closed]

Is it possible to detect fileless malware attacks with a machine learning model or with any other data science application? Fileless malware exists only in a computer’s random-access memory (RAM), ...
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Mutual Information Score for higher dimensional features?

Quick Background I am building a simple offline Motor Imagery classifier for a Brain Computer Interface system in Python and sklearn for educational purposes. I am following this pre-print. Here's a ...
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XGBoost: is increasing gamma same as feature selection by average gain?

Since gamma limits splits unless they meet a minimum gain threshold, isn't that the same thing as removing features that have low average gain? Both will results in splits with higher average gains. I ...
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when will the incareful features harm the model?

I am working on financial prediction problem(time-series prediction problem). I think feature engineering is importance in this problem. So i am careful to check the feature's effectiveness. And i ...
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Why my regression model always be dominanted by one feature?

I am working on a financial predict problem. which means it is a time series prediction problem. I have three features, which have high correlation(each two's corr is about 0.6) And I do the linear ...
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Showing a graph with PCA selected features in Random Forest Model

So I created a Random Forest Model like so: ...
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Which are the features selection techniques depending on the combination on cat num columns in independent and dependent features?

I am very confused: For what I understood I should: Multicollinearity check with Pearson corr and possibly consider to drop multicolliner features Then? I am very confused feature selection should be ...
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How to apply feature selection methods with undersampling and cross-validation?

I want to at first apply under-sampling and based on the resampled data, apply Recursive Feature Elimination with Cross-Validation to select the best number of ...
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Regression with a feature which has its own depth

I'm relatively new to ML/Statistical Analysis, and I'm facing a dataset structured like this ...
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How to aggregate features to a group level as a feature in machine learning model?

I am building a model to predict some behavior at a household level. I could roll up income or number of cars etc so that I can take everyone into consideration. But how can I roll up something like ...
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How to create training dataset based on sampled or original data?

I am trying to used SMOTE and Feature Selection by following this paper http://jad.shahroodut.ac.ir/article_825_679b8f128dec2874a8fbc314fc922127.pdf In this paper, the authors have mentioned about 4 ...
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Find top features that determine movie rank

I am trying to analyze a movie dataset in order to find the specific features which determine whether or not the movie is in the top-10 movies of the year (or likewise the worst-10 movies of the year)....
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Shapley summary plot interpretation doubt?

I have question when interpreting SHAP summary plot. I have attached the sample plot Here, If I am interpreting it correctly, low values of feature 1 are associated with high and negative values for ...
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Are there readily available models that can handle conditional correlation?

I've been working my way through the features of the Kaggle House Prices dataset (Note: this is a non-ranking entry, so this is just for exercises), and I've found a couple situations where there is a ...
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Not able to get a good accuracy score for the classification problem

I have taken a music popularity dataset which has five classes based on the popularity of the songs.I have made a Random forest model to predict the popularity of a given song(given its features).I ...
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Classification and variable selection with a single known class

I am looking for help and suggestions on how to approach a classification and feature selection problem making use of a single define class and several unknown ones. Using an example, for those than ...
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Using partially defined features in an unified deep learning model

Suppose we have two types of feature A and B. A is defined for all kinds of samples while B is only defined for some of the samples. Here, B is partially defined does not mean B is missing value (such ...
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Should i drop simple features after deriving more complex features from them?

I know for a fact that complex features projects the data into higher dimensions which makes the previously non-separable data linearly separable. But, Is this not true that these complex features ...
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Feature Selection Statistical Test for Nominal Response Vs Continuous Predictors? (in R)

I cannot find much information on this, none so-far useful. I have a sparse data set with 17K+ columns of continuous gene expressions, an example of a typical column: (3.15, 0, 7.1294, 0, 0, 0, 2300.2,...
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Heat map and correlation among variables [closed]

I would have a question on heat map and correlation among variables. I created this heat map, looking at possible correlation among variables and target. I got very small values. I wanted to set a ...
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Is adding geo information to zip codes redundant in feature Engineering?

I was wondering if it is redundant to add geo information like elevation and distance between two points (between supplier and purchaser) as features to a model, if you already have country code and ...
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Best Feature extraction for at the end retrieving audio

I work on a machine learning algo, which basically learns sequences in an audio .wav and generates the most “logical” sequences. The algorithm learns features, so I generate MFCCs from the audio file. ...
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How to Prepare data for LSTM

I'm having difficulties to wrap my head around how I can prepare my dataset to train an LSTM. Below is a screenshot of a subset representation of my dataset. There are several other feature not ...
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How many words should be taken as features in a ML problem?

I would like to ask you how many words should be taken as features in a ML program. For example, if I have 30000 distinct words to make a vocabulary, what would a good number be? I am currently ...
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62 views

How to use GridSearch for LinearSVC / Random Forest with time series data

I have a question related on how to use the GridSearch to find the best models for my problem with time series data. Every 3 rows is 1 one row in the original dataset. To make my time series problem a ...
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Adding extra (meaningful) features does not improve model performance

I am struggling with confusion matrices and their outputs. I thought to follow all the steps right, but unfortunately it seems that something is not going well. I had a dataset built and labelled on ...
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Convert time series data to supervised learning problem

I have a similar dataset like the one below. Each row represents a person and there are 3 different variables m1,m2,m3 with 3 measurements each. I am trying to frame this time series problem as a ...
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Proper feature selection methods for classifying signal into two category

I have a confusion to decide which feature selection method that I should employ in my research whose objective is to analyze which features that are significant in representing a certain condition of ...
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Is there any different between feature selection and pca? If there is could anyone please kindly explain for me please?

First of sorry for asking a possibly beginner question, but i don't understand pca seems to be the same as feature selection, but when i search online they seems to be talked differently. What people ...
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Should I remove a feature that is a non-linear function of another feature if I use it with a non-linear machine learning model?

Three of my features look as follows on the plot I can fit analytic functions to express $x_1$, $x_2$ and $x_3$ as non-linear functions of a parameter $x_0$. Does this mean that $x_1$, $x_2$ and $x_3$...
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How to apply feature selection in cross validated bagging

Normally in cross validation decision tree, feature selection will occur with training data but in bagging ensemble the training data is bootstrapped. How can I apply feature selection in cross ...
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Categorical features for relief based feature selection

I am exploring Feature Selection methods and was wondering if the Relief based feature selection methods can be used for categorical or mixed-type features. From what I understand there is, relief: ...
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Correlations, p-values and features selection

By using correlation matrix, I got some results: ...
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Selecting most important features for multilinear regression

I have a set of 25 features. I would like to choose the best features for my model. Originally, I was looking at the correlation of features with respect to response, and only taking those which are ...
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Chi-Squared test: ok for selecting significant features?

I would have a question on the contingency table and its results. I was performing this analysis on names starting with symbols as a possible feature, getting the following values: ...
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105 views

Figure out relative importance of entity attributes

I'm trying to understand how various aspects of a movie contribute to its gross revenue. I want to rank a movie's attributes in that sense - the attributes that most strongly determine the revenue are ...
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187 views

Features selection in imbalanced dataset

I have some doubts regarding an analysis. I have a dataset with class imbalance. I am trying to investigate some information from that data, e.g., how many urls contain http or https protocols. My ...
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Feature and the Gaussian Distribution (classification) [closed]

I have a question regarding variable following or not a random distribution. I selected 4 features negatively correlated to the label (Fraud/No Fraud). The notebook I'm taking the inspiration from ...
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73 views

How to remove features from a sklearn pipeline after it has already been fitted?

Background: I have created a basic modeling workflow in sklearn that utilizes sklearn's pipeline object. There are some preprocessing steps within the pipeline, and the last step of the pipeline is to ...
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How to do feature reduction for a log-linear regression model

I'm building a log-linear regression model and I have 18 different variables in my model. 13 out of 18 variables I'm using are hot-encoded variables for holiday, e.g. showing which holiday it is. I ...
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Linear transformations making independent features dependent

I read about features and some relevant topics recently. I ran into an easy but very advanced question: Why can two independent features become dependent after applying a linear transformation? I ...
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34 views

Using the whole dataset for testing (not validation) in case of small datasets

for an object detection task I created a small dataset to train an object detector. The class frequency is more or less balanced, however I defined some additional attributes with environmental ...
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Creating features from raw accelerometer data

I have a dataset containing raw 3-axis accelerometer data collected from a users lower leg and I want to create a classification model (as simple as possible) that detects if the user is sat down or ...

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