Questions tagged [feature-selection]
Methods and principles of selecting a subset of attributes for use in further modelling
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7 views
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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20 views
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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1answer
28 views
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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15 views
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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16 views
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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1answer
23 views
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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22 views
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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21 views
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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15 views
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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1answer
12 views
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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1answer
33 views
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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0answers
11 views
Showing a graph with PCA selected features in Random Forest Model
So I created a Random Forest Model like so:
...
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15 views
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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27 views
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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0answers
17 views
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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1answer
12 views
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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17 views
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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25 views
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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0answers
27 views
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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1answer
13 views
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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13 views
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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16 views
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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1answer
14 views
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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1answer
19 views
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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0answers
11 views
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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1answer
34 views
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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8 views
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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0answers
16 views
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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1answer
53 views
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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1answer
14 views
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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1answer
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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1answer
30 views
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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0answers
47 views
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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1answer
24 views
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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2answers
29 views
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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0answers
18 views
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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1answer
21 views
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4 views
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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10 views
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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1answer
26 views
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2answers
44 views
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 ...
0
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1answer
24 views
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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2answers
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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1answer
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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1answer
22 views
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 ...
0
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1answer
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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0answers
13 views
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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0answers
27 views
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 ...
2
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1answer
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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1answer
29 views
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 ...
