Questions tagged [feature-selection]

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

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Is it a good idea to add predictable features in dataset before training?

For example, let's say a dataset has 100+ columns. And the ml has to recognize a complex pattern containing 1000 different conditions. And one of those conditions is, the integer from the first column ...
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Feature Engineering on transactional dataset clustering

I have a data set with transactions details from different business (roughly 1 thousand business entities). Each row is a transaction. The structure of the dataset is as follows: client_id Sex Age ...
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How to run a regression in R on multiple different csv data files within the same folder

I am trying to run a LASSO Regression via the enet function (from the elasticnet library) in R on each and every one of a large number of individual csv file formatted datasets all within the same ...
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additive or multiplicative model?

let's say if I have two scores $x_1^i$ and $x_2^i$ for each data point $i$, and I need to make a final score/loss function out of it. Should I use a weighted sum $w_1 x_1^i + w_2 x_2^i$, or their ...
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Why Multicollinearity is a problem in machine learning algorithms

Is only a subset of algorithms are affected by the multicollinearity problem or all the machine learning algorithms? What is the solution for this?
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What is this method of feature selection called, is it a good idea, and if so how might I implement it?

I hope you don't mind me asking a few questions in one - they're all related somewhat. I'm working on a simple classification problem (Titanic, you guessed it) and I'm trying to grind out the last ...
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Finding the most statistically significant variable(s) in a dataset - logistic regression/feature selection

I'm currently working on a project where I have a dataset which consists of a number of blood samples and the quantity of different biological compounds within each sample. The samples are split into ...
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How to build a predictive model with multiple features?

I built an R RandomForest Regression model. The source training data is a historical monthly report of all closed tickets, and the data for forecasting/prediction is a report of open tickets. These ...
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Datasets with Shared Features, Prediction on Dataset B using Shared Features from Dataset A + Extra Features

The title is not quite enough to describe the question well. Here is the general idea. I am working with a dataset $A$ with features $\{f_1,f_2, f_3, f_4\}$ and output $y$. I also have dataset $B$ ...
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Will summing features improve the Machine Learning models?

Assuming that I have two features, x and y for an MLP model. I know that depending on the model, the multiplication of features ...
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How special tokens in BERT-Transformers work?

I was trying to understand how tokens work and all I understood is that tokens are the representation of the input in a more meaningful way (data preparation for the "encoder of transformer" ...
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How to Predict Probabilty that the Customer will buy specific Product?

We have data consist of previous transaction history consisting of Date,Order-id, Product-id, Product name, ordered or not. We need to predict a specific product probability for all the customers that ...
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Does the result of scipy.stats.f_oneway and sklearn.feature_selection.f_classif should be equal?

I thought that the results of both functions would be the same, but: ...
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Features and LSTM

I have a problem while developing an LStm model. I have 4 feaures that I want to use to make a prediction. When I test my model with a single feaure I get average results but when I test with all 4 ...
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Does an LSTM model use trend in features?

Does an LSTM take into account a trend in a feature? Or does it only see trends from the previous output (Y predicted)? To illustrate, imagine we have a trend in feature A. In our problem, we know ...
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How to select relevant columns from a dataset with many features

I have a dataset with a large number of potential features (>100) and I am interested in finding a relatively small subset of these (maybe on the order of 5, or 20) features which is best suited to ...
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Automated feature selection - Best practice to avoid data leakage?

This question relates generally to all automated feature selection approaches. In my particular scenario, we have a python package called tsfresh and multiclass classification. What has been done so ...
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What is the right way to encode nominal variables for feature selection? Is it ordinal or one-hot-encoding?

Most of the time encoding for machine learning models is straight forward. Numerical variables need no encoding. Categorical variables are encoded. Nominal variables are encoded with One-Hot-Encoding. ...
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Approach to check multicollinearity for data with categorical and continuous features?

I would like to know what should be the best approach to check for multicollinearity if my data has categorical and continuous variables like:- Age, Income, Department(more than 2 category), Gender(2 ...
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RandomForestRegressor.feature_importances_ applied on classification problem

friends! I am new to the machine learning field, but so motivated to study more. I am currently conducting a study on the Wisconsin Breast Cancer Dataset and I apply some ML algorithms to study their ...
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Library for Phi correlation coefficient in python?

I want to calculate correlation b/w categorical features in my data. I reviewed the literature and found phi coefficient can be used for this purpose. I found one library called phik in python enter ...
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How to use a material number as a feature for Machine Learning?

I have a problem. I want to use a classification algorithm. I also have materialNumber as a column. Could I use that as a feature for my Machine Learning algorithm? ...
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How to return selected features with different feature selection models?

I use the below function to detect the effect of those feature selection models on my data, it works perfectly. what I want is to return the name of selected features for each model, is there any ...
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What are the differences between the below feature selection methods?

Do the below codes do the same? If not, what are the differences? ...
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Merging two datasets with different features for machine learning prediction

I'm trying to create a model which predicts Real estate prices with xgboost in machine learning, my question is : Can i combine two datasets to do it ? First dataset : 13 features Second dataset : ...
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What is the Purpose of Feature Selection

I have a small medical dataset (200 samples) that contains only 6 cases of the condition I am trying to predict using machine learning. So far, the dataset is not proving useful for predicting the ...
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Feature creation ideas for propensity models?

I'm working on a propensity model, predicting whether customers would buy or not. While doing exploratory data analysis, I found that customers have a buying pattern. Most customers repeat the ...
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For feature selection, do we use Chi-squared with Mutual Information together?

Or do we only choose one out of two for categorical data.
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how to align sliding window to extract features from multi modal timeseries data?

I have two datasets that are collected at different frequencies at the same time. One is recorded at 128Hz and another one is recorded at 512 Hz. I am trying to extract some features using the moving ...
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How To Develop Cluster Models Where the Clusters Occur Along Subsets of Dimensions in Multidimensional Data?

I have been exploring clustering algorithms (K-Means, K-Medoids, Ward Agglomerative, Gaussian Mixture Modeling, BIRCH, DBSCAN, OPTICS, Common Nearest-Neighbour Clustering) with multidimensional data. ...
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How do I get the "Ideal Characteristics" of a candidate for least attrition in Machine Learning?

I am working on a project to predict whether a candidate, after joining our organization, would leave us within 1 year or not. The model is based on different features present in their resumes (...
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Feature selection before or after scaling and splitting

Should feature scaling/standardization/normalization be done before or after feature selection, and before or after data splitting? I am confused about the order in which the various pre-processing ...
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how to deal with features in pairwaise comparison models?

I am working on a dataset of ATP (Association of Tennis Professionals - men only) tennis games over several years. I want to predict the outcome of tennis so one way to do that is using a Bradley-...
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VIF Vs Mutual Info

I was searching for the best ways for feature selection in a regression problem & came across a post suggesting mutual info for regression, I tried the same on boston data set. The results were as ...
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do feature selection and model selection must share the same ratio between development set and test set?

As the title, after I performed a Feature Selection, is it mandatory to respect the same ratio (between development set and test set) in Model Selection?
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Can you use gplearn library to improve an SVM model?

I want to know your thoughts on this. Someone on the internet recommended this process to me in order to improve the accuracy of my SVM model: Split dataset with 5 folds stratified k-fold (SKF) Apply ...
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How to deal with feature with different sample size?

I got a dataset that contains 50 features starting from 2009 to 2018. But one of the feature was only availiable since 2015 and unable to recover. I am concerning about if I train a model on the whole ...
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Multicolinear Predictors Effect on Model

I know that multicolinear predictors in a model aren't ideal because it causes the model to be sensitive to very minor changes, which then reduces our ability to interpret the effects of each ...
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Machine learning cost/benefit for including priors in input vector

Is there a trade-off in accuracy/generalisation/performance when providing priors to a general machine learning algorithm vs training the machine learning algorithm with enough data so that it could ...
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Sequential feature selection stopping condition

When using sequential feature selection approach, say forward feature selection, I want to stop adding new features when the improvement in model scores is smaller than a certain level. Is there a ...
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Using F_regression to find the best significant features

We are trying to use SelectKBest F_Regression scoring function on a pool of 1000 numerical features, and solve a regression problem. Also, we wanted to paralellize the execution of SelectKBest and we ...
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Feature engineering before splitting

This is a sister post to the original closed post (here). Since the data transformation part is done after data spliting on the TRAINING data only, I wonder wouldn't such transformation has dependency ...
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How to represent a time duration feature for cases where time is still counting

I have a problem where I am trying to classify the outcome of costumer complaint cases. I have several features already such as type of item bought, reason for complaint etc... I am trying to add a ...
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Searching machine learning algorithm for regression problem with many features

I have a machine learning problem with about 160 features and 400 cases and I want to find the best predictors for a continuous outcome. The dataset contains variables of psychotherapists and clients. ...
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What are the business consideration while creating features?

I'm creating a model to predict energy consumption in one food production facility. From business, I know that Downtime due to power failure, machine failure and maintenance, etc. is one of the major ...
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Lagged Features

Lets look for example, at the forecast the sales of a retail outlet. If I understood the concept correctly, than a lagged feature would be the sales of a previous month t−1. Would it make sense/is it ...
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Feature selection with "overly important" features

I am very new to machine learning modeling, but I encountered a feature selection problem that I hope can get your insights on: For example, I have A,B,C,D as my independent variables and y as my ...
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How to do feature selection or feature engineering in datasets with a lot of features?

To make a good ML model, we have to select features that increase model accuracy and, if needed, to "engineer" features (e.g. apply some function like logarithm or square to linear ...
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What kind of features can I obtain from IP:Port data?

I have a dataset that consist of the fields below. IP_Version,id,IP_TTL,IP_Source,TCP_Source_PORT,IP_Dest,TCP_Dest_PORT,data_size,timestamp What kind of features ...
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Is there anyway to do feature selection in a dataset which has only cases

I have dataset which has only cases in it and no controls. Is it possible to do feature selection in such datasets. Ultimately, i want to make a prediction model that predicts the case.
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