Questions tagged [feature-selection]

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

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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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RFECV best n_features doesn't correspond to best gridscore

I am working on a feature selection for a binary classification problem with 977 records (and class proportion of 77:23). I already referred these two related posts - here and here. step size = 1 and ...
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How to deal with date features in linear regression?

I need some help about a project. I have a dataframe like that; YEAR MONTH INDICATOR_1 INDICATOR_2 INDICATOR_3 2014 3 0.123 0.495 0.222 My goal is to predict all of the indicator for the next year (...
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How Decision Tree Classifier works? [closed]

In particular i am using SKLearn with class DecisionTreeClassifier. I would really like to understand how the tree build itself ...
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Selecting Features Derived From Target

I am a ml novice, though I have an extensive computing background. I am about to start a ml project, and there is something that I can't quite get my head around. If, for example, I am trying to ...
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Feature classification - am I doing it right?

I have a system where i get as input array of feature strings: ["kol","bol","sol","nol"] The length of this array is ...
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if feature has 4 unique values ( number of products: 1 ,2,3,4 ) so should i treat that as categorical or discreate variable?

I am using bank churn data (https://www.kaggle.com/kmalit/bank-customer-churn-prediction/data) there is a column in data called NumOfProducts that has 4 unique values so should I treat that as a ...
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How to use a feature recorded in different unit?

I want to use hour as a feature in my random forest model. The challenge that I’m facing is that some observations are recorded based on machine operating hour while others are in engine hour. Without ...
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is using feature selection(supervised) methods after running kmeans and taking the 'cluster' variable(0,1,2 for eg.) as the labeled data correct?

Feature selection in a gist from what i understand is reducing the variables but retaining the labels as much as possible, from that pov this seems correct but i haven't found anything on this. Any ...
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Brute-force feature selection and cross-validation

There is an existing score made of 10 parameters; each parameter is equally weighted & the total score is found by summing the score for each parameter. I want to try to reduce the number of ...
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Feature Selection: How to select categorical features in a regression problem

I am reviewing information for feature selection based in filter methods. I got info (link1, link2, link3, link4, link5) for: Numerical input, numerical output Categorical input, categorical output ...
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Interpretation of statistical features in ML model

I have a data like as shown below (working on classification problem using traditional classification and DL based approaches) I see in feature engineering tutorials (and tools) here and here, they ...
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Interpreting mlxtend Sequential Forward selection output

I am running a SFS on binary classification problem with 15 features and 977 rows. My dataset is imbalanced with 77 (class 0) : 23 (class1) I tried the below code ...
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When to use best hyperparameters - Feature selection or Model building?

I am working on a binary classification with 977 rows using different algorithms I am planning to select important features using wrapper methods. As you might know, wrapper methods involve use of ML ...
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Automated feature selection packages - Python

I am working on a binary classification with 977 rows. class proportion is 77:23. I have lot of high cardinality categorical variables and couple of numeric variables such as Age and quantity. I would ...
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Extending a classifier with specialised features

Let's say we have an app and a classifier (GBDT) predicting whether a user is a good user or bad (whatever that means) based on generic signals that every user has like profile fields, how long they ...
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How do I fine-tune model performance after the initial run? (Scikit-Learn)

I've just started learning regression using scikit-learn and stumbled upon a problem. For a given dataset, let's say that I've imputed the missing data and one-hot encoded all categorical features. ...
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how to test if labels have actual dependencies on features?

I am trying to train an LSTM(many to one) model with multivariate time series input and a categorical output. after training for quite some time, the resulting model still has low accuracy and high ...
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Discover the geographical position of weather historical data using weather historical predictions

I have too little experience in neural networks and I wanted to solve a problem that I don't know how to raise. On the one side, I have a 1 year hourly historical data of the temperature in a specific ...
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Understanding which variables impact your variable of interest the most (correlation, linear regression) and correctly interpreting results

How do you ascertain which variables lead to the greatest increase in another variable of interest? Let's say you have a correlation matrix. You look at the row of the variable you are particularly ...
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Does the sign of correlation matter in feature selection?

If I understand correctly, the correlation between features and the target can be used to quantify whether those features are relevant to keep, hence the ritual of plotting the correlation matrix as a ...
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Correlation with target variable for regression problem

Given the following dataframe age job salary 0 1 Doctor 100 1 2 Engineer 200 2 3 Lawyer 300 ... with ...
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Is there a way to output feature importance based on the outputted class?

I'm running a random forest classifier in Python (two classes). I am using the feature_importances_ method of the ...
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Regarding pos tagging

I working on a dataset, I did the pos_tagging using nltk. Now I want to know which sequence of grammar is most common in my rows, then I want to define a chunk grammar based on a common grammar ...
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What is the best algorithm for feature selection in categorical variables?

Imagine this question in two parts: Part 1 : My Y variable can be only 1 and 0 Part 2 : My Y variable can be only -1, 0, 1 In both cases my X is continous with float variables. What are the ...
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How to interpret CyclicalTransformer output for date columns?

I have a dataset with lot of datetime columns and am working on binary classification problem In order to engineer features, I extracted year, ...
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Why is an ML algorithm performing better with correlated features, than the one with uncorrelated ones?

I have a dataset with all numerical values. Since the features were not many, I created more by multiplying pairs of each other. This created some highly correlated features, as expected. Now, I ...
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LSTM for non consecutive lags

Let's suppose I have a time series with hourly data. Firstly, I was using the previous 168 values aka lags/timesteps to forecast current value, i.e, I was trying to learn the following F function $X_t=...
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In ML why selecting the best variables?

Almost all ML notebooks out there have a section where they select the best features to use in the model. Why is this step always there ? How bad can it be to keep a variable that is not correlated ...
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