Questions tagged [feature-selection]

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

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15 views

How to interpret Variance Inflation Factor (VIF) results?

From various books and blog posts, I understood that the Variance Inflation Factor (VIF) is used to calculate collinearity. They say that VIF till 10 is good. But I have a question. As we can see in ...
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39 views

How can we convert time series data to supervised learning problem?

I am preparing a data for machine learning model. I want to deal with time series data as normal supervised learning prediction. Let's say I have a data for car speed and I have several cars models ...
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How to reduce the Root mean square error

I have dataset which describe "how many passenger arriving in some airport " and I would like to predict how many passenger arriving in monthly bases for next year. The features that I have is the ...
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25 views

How does $\chi^2$ feature selection work?

I can't find the information how $\chi^2$ are used to select numerical features for a model. ...
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Feature Selection algorithm/library for CRF

I am using the Conditional Random Fields CRF suite scikit-learn wrapper algorithm. I have read on the literature various approaches for feature selection, but I cannot find any on that package or, ...
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What are the steps and correct order of the operations in Machine Learning? [from Getting data to optimising models]

I've followed lots of tutorials on Machine Learning but in each of these, they go for a different strategy so it's quite confusing for me. I want to Know that what are the operations involved and what ...
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Should features be correlated on uncorrelated for classification and regression (prediction)

I have seen researchers using pearson's correlation coefficient to find out the relevant features -- to keep the features that have a high correlation value with the target. The physical implication ...
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30 views

Variation in output of Logistic Regression when using SMOTE

I am working on a logistic regression case with an imbalance in the target variable. To fix this I am using SMOTE (Synthetic Minority Oversampling Technique), but each time I run my regression model, ...
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18 views

Audio dataset preprocessing to perform cry detection

I am building a neural network to perform cry detection (i.e., binary classification of cry/non-cry situations) when capturing sound in a house environment. To do so, I performed the following steps: ...
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sklearn.feature_selection vs xgboost feature_importances?

sklearn.feature_selection vs xgboost feature_importances Can somebody explain in-detailed differences between sklearn.feature_selection and xgboost feature_importances? And how the algorithms work ...
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When does random forest feature importance fail?

I'm curious about the assumptions of random forest feature importance. In this paper, the author says that "We show that random forest variable importance measures are a sensible means for ...
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28 views

Linear Regression on data with bimodal outcome

I have a data set with 3,000 features and continuous dependent variables of time with 18,000 instances. The histogram of the dependent variables show that the they have a bimodal distribution. I am ...
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25 views

how to use sklearn without feature selection

I am trying to study the effect of using feature selection onmy text classification code . I want to make a rating without any feature selection, but sklearn use document frequency (df) by default ...
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35 views

Association between features

Given the anonymized dataset of features below, where: "code" is a categorical variable. "x1" and "x2" are continuous variables. "x3" and "x4" are extracted features. They are the mean values of ...
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95 views

How to use additional variables that are not available in test set?

I have additional variables in my dataset that are somewhat correlated to the continuous target variable, but that are completely unavailable in the test set. So, I'm wondering how the best to use ...
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48 views

Increase accuracy of classification problem [closed]

I am trying to build a classifier that predicts the compiler given some operations of assembly code. Here is the pandas dataframe: What I do is using a TfidfVectorizer and select the features that ...
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Can I merge levels or factors having equal mean in categorical variable

I compared levels of categorical variable by their respected mean, obtain from continuous response variable using pivot table. I found that some of the levels is having nearly equal mean e.g 'BrDale' ...
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Extracting Features for Graph transformation

Suppose I have a directed graph G (V,E) whose transformation is defined by a library of patterns. Each vertex is of particular type. The library of patterns contain subgraphs (g1,g2,g3 etc)and it's ...
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54 views

Building an efficient feature vector

I am building a classifier for malware analysis, which predicts if I have a malware by looking at the intructions of an assembly code, such as push, mov,... and predicting the optimization method. ...
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Information Gain & Gini Index for NLP

I know how Information Gain and Gini Index work in General. I have problem figuring out how to apply these techniques in NLP and text feature extraction. Can someone show me an example of how to ...
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Problem building a feature vector

I am trying build a classifier for malware analysis for which basing in the instructions of an assembly code, such as push, mov,... I want to predict the compiler, and in a second time the ...
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1answer
35 views

Find the most relevant columns for each single class in pandas

The following question (this one) did not help me. I have a big dataset, and I want to know which Columns are the most relevant for the Target Variable. I know that, in my case, for each class in the ...
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Find out which attribute of a movie causes the most variation in score

I'm tinkering around with a subset of the IMDb dataset, and have been thinking about what specific attributes of a movie impact its user rating. I am looking to survey what methods can be used to find ...
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Recursive feature elimination on train data or complete dataset and dummy encoding

I am using RFE with logistic regression. I will also be doing cross validation with RFE (RFECV in sklearn) to get the optimum number of features. I am not sure whether to use RFECV on just train ...
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if I got feature importance of xgboost/LightGBM what is next?

If I have feature importances of different variables in a xgboost/LightGBM model, how do I use this information? Is it better to just use the top n features and retrain the model? Does the feature ...
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My models performs better with the arbitrary random feature. How can I interpret this?

I am training 6 different classifiers 'Decision Tree', 'Random Forest', 'Logistic regression' and 'SVM' with different kernels. There are about 80 dependent variables including categorical and ...
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How to choose the features for an algorithm from the given attached screenshot?

How to choose the features from the given attached heat map & correlation factor for the classification algorithm? I have 6 different features i.e., ac233fc01403, ac233fc02eaa, ac233fc015f6, ...
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Increase accuracy of occupancy prediction?

I have a project that's aimed to predict the amount of occupants at my local gym given the date and weather. Here's my Kaggle kernel I have two datasets, occupants on a given hour and weather on a ...
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31 views

Implicit feature selection

I have heard that Random Forest and other tree based machines apply some kind of implicit feature selection. My Question is: Does this also apply for machines like the SVM? As far as I understand is ...
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How to deal with a feature that has lot of categorical values?

I know this question has been asked before and I have tried a few things but those things are not working as expected for my usecase. I have a 500 length feature vector. One of these features is a ...
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(Feature selection) In which cases it is legitimate to remove features manually?

I am dealing with the feature in which only one category takes up about 90%, the instances of more than 30 other categories are sparse. Is it reasonable to remove this feature before building an ...
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36 views

(Feature Selection) different results from L2-based and Tree-based

I am doing feature selection using Sklearn: Tree-based feature selection : RandomForestClassifier.feature_importances_ L2-based feature selection: LogisticRegression.coef_ Target variable is binary ...
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2answers
83 views

What do you call a feature that always has the same value?

Is there a standard term for a feature that always has the same value, i.e. that can be discarded without loss of information? For example I am trying to classify cats vs dogs, and every example in ...
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61 views

Feature selection is not that useful?

I've been doing a few DataScience competitions now, and i'm noticing something quite odd and frustrating to me. Why is it frustrating? Because , in theory, when you read about datascience it's all ...
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114 views

How to install boruta in conda?

I want to install boruta in my anaconda environment, but if I execute conda install boruta It displays, ...
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54 views

Improving classifcation when some are less represented?

I have a multi-class classification problem. It performs quite well but on the least represented classes it doesn't. Indeed, here is the distribution : And here are the classification results (I took ...
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Features selection with a lot of dummy variables in R

I am performing features selection on 3849 dummy variable (one-hot encoding) using Boruta algorithm and the algorithm is taking forever to run. Is there a faster way I can perform features selection ...
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1answer
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Pearson correlation make disapear the target column

I have a dataframe with some numerical and categorical values. I want to do some feature selection to visualise a low-dimensional split in the dataset when the target variable is the grade. Yet, when ...
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1answer
98 views

Feature Importance

I have a dataset with 10 features. I've computed the feature importance using permutation importance with cross-validation from eli5, after fitting an extremely randomized trees (ET) classifier form ...
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63 views

Features reduction for the not correlated data set

I am working with classification problem on a training data set, which have 100 features. All the features in pairs haven't visible correlation. One can see it in the example pair plot for the some of ...
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1answer
40 views

Feature extraction in audio spectrogram

I have audio and its spectrogram of the words in English language. (A spectrogram is a frequency domain representation of a signal) Consider the words: chain, change, chair, chapter. As you can notice,...
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Feature selection, is it possible to combine wrapper and embedded methods?

I'm using neural network to predict PM10 concentration (a regression problem). Since the wrapper method is dependent on the model, so passing the neural network model that's optimized for all the ...
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1answer
102 views

xgboost feature selection and feature importance

when you create the new feature for data analysis for linear regression, it is clear that the feature has to be linear with other features is better but for xgboost what is the guideline to make a ...
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Difference between RFECV and SFS?

I used scikit.REFCV and mlxtend.SFS (backward) on the same data, same classifier, same cv, same scorer,... I also did a third version with sample weights passed to SFS's estimator And i'm conflicted ...
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42 views

What variance threshold to consider in feature selection?

Consider a numerical dataset with continuous variables, that has been scaled to end up with values in the [0,1] range. How can I compute a reasonable variance threshold for all the variables?
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Scikit-compatible network lasso implementation

Is anyone aware of a scikit-compatible network Lasso (nLasso) implementation? These papers have source code as well: D. Hallac, J. Leskovec, and S. Boyd, “Network lasso: Clustering and ...
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Feature selection for string columns

I tried sklearn SelectKBest for feature selection (feature scores) of supervised machine learning before. It looks good, but can only take numbers as inputs. Although I can present some string ...
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186 views

Explanation of how DeepExplainer works to obtain SHAP values in simple terms

I have been using DeepExplainer (DE) to obtain the approximate SHAP values for my MLP model. I am following https://github.com/slundberg/shap and DE's performance is very high in terms of computation ...
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129 views

Finding Feature Importance in CNN's?

Let's say I have images of cars. For each image in the dataset, I have let's say 3 pictures of the same car but in different angles. 1) The first image is the picture of the car from the front. 2) ...
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What is the dimension reduction method to large numbers of independent features while only two of them are important?why?

What is the dimension reduction method to model a data with large numbers of independent features (for instance 5k features), while only two of them are important (are effective in cost function)? I ...