Questions tagged [feature-selection]

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

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Literature on selecting specific dimensions in a word embedding vector

I am aware that the different dimensions in the word embedding represents different information and algebraic operations can be performed between two embeddings for example. Can anyone point me to ...
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use genetic algorithm as a feature selection for text classification

how to apply the genetic algorithm as a feature selection for text classification in python I need to use GA to select most relevant feature in text classification
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Feature selection inside Random Forest

I understood random forest is building a model with multiple decision trees, Row sampling is based on bootstraping My question is how feature selection is happening for each tree ? Any help would be ...
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feature selection in sklearn?

the RFECV takes the estimator and finds which features are important according to that estimator. But, it gets slow when we are dealing with data with many features. But, if we use trees they give us ...
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What does all zero mean decrease in accuracy (MDA/permutation importance) signify?

I have a model I've trained on ~3400 features with ~500 samples (40:20:20 train:test:val) that I've calculated MDA on using the eli5 package. However, all the features are zero when I calculate this ...
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How to deal with different audio formats for audio classification?

I am working on an audio classification problem statement to classify between two audio classes. I have collected samples from jotform, they are providing audio widget to collect .wav audio but it ...
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Python Clustering : Best preprocessing and how to see cluster characteristics?

It seems I need your help yet again leadies and gents. I've been working on this dataset using Python, mostly sklearn stuff, trying different kinds of algorithms, like K-Means, some density based ...
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how to improve score of automl regressor

I was trying to solve a regression problem on HackerEarth. I got to a score of 81.20 using XGBRegressor after some data preprocessing, the top ranker had a score of 81.55. Some one suggested me to use ...
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Should I scale high ranging ordinal fields?

In the left column, I have an ordinal integer field. In the right column, I have a scaled float feature. Should I scale the ordinal field since it is getting so much bigger than the other feature?
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Is this attribute numeric or categorical (ordinal)? Help!

So I have this dataset I need to perform several techniques on as part of a data mining/machine learning project of some sort in PYTHON. There are a couple of features however, that have me very ...
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Can I apply feature selection before splitting by requiring selection occurs > 90% of time

I want to move the feature selection step to before splitting to save time and allow bigger input dataset. If, in repeated subsamples, a feature is selected in over X percentage of cases I will keep ...
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XGBoost is it possible to prevent a feature from being used twice in the same tree?

I'm using XGBoost and all its doing is using the feature in the first column of my data. My feature importance chart correlates perfectly to the position of the feature in my xtrain. If I shuffle the ...
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XGBoost - feature importance just depends on the location of the feature in the data

I'm trying to do some feature selection using XGBoost, but the feature importance chart just spits out the features in order of appearance. The feature that is in the first column in the xtrain data ...
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How do feature selection on a sparse matrix?

Say I want to do features selection on a sparse matrix, i.e., 10,000 rows x 1500 features, but the matrix is mostly sparse. Let's say the features are all numeric and the target is binary and discrete....
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Is there any problem with dropping only part of the OneHot generated features?

The one hot encoder adds more columns to the data, one for each category in the encoded feature. In the example below, the column City was transformed into 4 other ...
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When should mutual information be used for feature selection over other feature selection methods like correlation, ANOVA , etc?

I have a data set with categorical and continuous/ordinal explanatory variables and continuous target variable. I tried to filter features using one-way ANOVA for categorical variables and using ...
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Random Forest command in R for selected variables

All I can find online is how a random forest is run on all variables in a dataset using the period: ...
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Find non overlapping area between two kde plots in python

I was attempting to determine whether a feature is important or not base on its kde distribution for target variable. I am aware how to plot the kde plot and guess after looking at the plots, but is ...
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How do I view my K features with SelectKBest? [closed]

I am getting an error for "no attribute columns". Because of this, I can't see the selected K features and can't build plots: First occurrence of error: Second occurrence of error: I'm also ...
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Shapley plot use for feature importance in Orange visual programming

I would like to visualize feature importance using Shapley plot. Could you tell me how to do it. I have found a new explain prediction widget but it does not seem to work...
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PCA for complex-valued data

I'm quite shocked for encountering this error on PCA from sklearn ValueError: Complex data not supported After trying to fit ...
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Using the input record's distribution as a composite input feature

I have a very interesting dataset that I need to use for doing regression. It is production data from stainless steel production and I have about 290 input features, so I need to start reducing the ...
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Random Forest feature selection

I am using Random Forest model for feature selection with data with bias. I have tried the Random Forest model on my features increasing number of trees and found features importance moving. Could ...
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Data Selection & Visualization Tool

I am looking for a tool that will show neatly all the rows from a large table (100GB+) for complex selection conditions from an sql/text/csv table. DB may be on linux or windows. Looking for easy ...
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Importance of features

It is common to say in ML feature selection that features that are irrelevant in isolation can be important in combination with other features. Is there a simple example (one or two features) to ...
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PCA vs.KernelPCA: which one to use for high dimensional data?

I have a dataset which contains a lot of features (>>3). For computational reasons, I would like to apply a dimensionality reduction. At this point I could use different techniques: standard PCA ...
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Handling a Pandas Data Frame containing multiple non-ordinal categorical features

I'm currently trying to analyse a dataset containing multiple non-ordinal categorical features and a binary target variable. The table looks something like this: ...
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Variance as criteria for feature selection

I'm working on an unsupervised clustering problem. I read multiple times that a variable with higher variance can be chosen over a variable with a lower variance. For example, scikit-learn implements ...
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Using feature importance to decet latent variables and grouping

Is it possible to use feature importance from Random Forests (e.g. based on gini impurity) or other models to determine which features I can use to group the rows of my dataset homogeneously? For ...
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Consecutive Feature Selection-CV and Model Selection-CV

I want to ask a question about general workflow of algorithm development. I want to include a "feature selection with Random Forest" step into my workflow but I have doubts about data leakage. It is ...
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Determine how each feature contribute to XGBoost Classification

so for a summary of what I have done: My dataset has 5 classes and 10 parameters. I used XGBclassifer from sklearn to investigate if I could use those 10 parameters to predict the class of each data ...
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Approipriate use of rfe with xgboost classifier

I have an imbalanced data set with about 6000 features. I am using the XGBoost classifier to classify the labels. However, due to the large number of features, I am opting for rfe function to select ...
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Feature relevance in PCA + kmeans algorythm

Working on the World Happiness Report dataset, i have N countries with M features and a happiness score. This is the parameter I built 3 classes from: happy, medium, unhappy (numerical intervals of ...
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Categorical Feature with Most of the Data in a Single Category

I am a beginner with machine learning and I ran into something that made me question qualities for a good categorical feature in a regression model. Picture of Feature and Distribution Here. From the ...
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Feature importance in neural networks

Hello I am using keras to develop a neural network model and I have a data of 45 numerical predictor variables, 2 categorical targets that will be predicted each with a different model. As I found, ...
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Unsupervised learning-Feature selection in python

I need to select the most important features from my data frame before starting with nearest neighbours problem. Which methods are the best to do this? My data frame has around 8 categorical features ...
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Genetic algorithm - Feature selection packages in Python

Can you share some packages in Python which are implemented that I can use for selecting features based on a genetic algorithm? I did refer to this AUTO-ML post and found out that it is useful but ...
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What is the reasonable max number of features for LSTM?

I've been trying to find information on how many features I can use in LSTM. I found that LSTM can handle up to 500-1000 timesteps, but what about features (sequences)? Can I use 1000 features per ...
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How to understand ANOVA-F for feature selection in Python. Sklearn SelectKBest with f_classif

I am trying to understand what it really means to calculate an ANOVA F value for feature selection for a binary classification problem. As I understand from the calculation of ANOVA from basic ...
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How to group categorical columns into similar types?

(Forgive me if the question is ill put. I am a novice in data science. Please comment or edit so that the question can be improved) I have a dataset where we have to predict the future sale of a shop....
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Decision tree with multiple outputs

I have a sample with 10 independent variables (X1, X2, X3 ....), and multiple output labels (y1, y2, y3). Here y1 will depend on X1, X2 y2 will depend on X3, X4 and so on. y1, y2, y3 might or might ...
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How to interpret PCA rankings in Weka

I am struggling to understand what the rankings in Weka are representing. I.e. the coefficients for each attribute in the rank. What is the output in the Weka program for PCA telling me with these ...
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Feature Selection with non-linear numerical and categorical variables

I have a dataset of 45 non-linear numerical values and 2 categorical values. I am making a feature selection to predict categorical variables one by one or together. I used the correlation ratio and ...
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Feature Selection Using Linear Discriminant Analysis

In this post it says that when input variables are continuous and response is categorical, in that case we can use Linear Discriminant Analysis (LDA). But as far as i know it is a dimentionality ...
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How and When features are attached to target label

I am using Mallet CRF library and having training set sequences like below. ...
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Should we do Feature selection in parallel with feature engineering?

I'm working with LightGBM on a large data set about 3M row and about 8 columns. When i ...
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Can we use both ridge-lasso and PCA in the same model for better results?

My question here is if we are using the PCA, the dimensionality is reduced and no question of feature selection is required using ridge & lasso. So should I use ride-lasso followed by PCA or I ...
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Using ARIMA parameters when transforming time series to Supervised Learning

When forecasting time series one can change the problem from a classical time series (ARIMA type of models) to supervised learning (by adding lag features). When the time series is long and you ...
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Which target variable should I use?

I have a problem where I want an LSTM to predict the resistance of a body. This value can also be calculated if we know the drag coefficient and the speed of that body. In my case, at inference time, ...
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Apply feature importance obtained from entire dataset to individual row

There are various methods for calculating feature importance. These are generally obtained from computing the entire dataset. Can this feature importance be then applied to specific rows? In other ...

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