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Questions tagged [feature-selection]

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

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Define difference between feature selection and feature reduction [duplicate]

What is the difference between feature selection and feature reduction? When do we use feature selection and what happens when we don't use it? How is this different than feature reduction?
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Key pixels, key “features” detection in CNNs

I am working on a dataset but I don't know what the labels mean. I was wondering if using CNNs there was a way to understand which pixels where most significant for the network. A little bit in the ...
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Feature Selection with one-hot-encoded categorical data

I have a dataset with 400+ columns. Almost 90% of these are categorical data with One-Hot-Encoding (OHE). I'm using the dataset for a classification problem. My professors asked me to perform feature ...
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Can I add features that are parts of another feature?

I am building a model (implementing both logistic regression and Xgboost) to understand the importance/significance of each feature in whether a customer is going to repurchase to understand what ...
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Text classification 'features imput'

I have a text classification task that consists of classifying text into classes (literary genres). I have computed the average word length and sentence length. Also, some POS relative frequency so ...
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1answer
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How to deal with a biased feature in Machine Learning (date)

I have a model that predicts the lifespan of a horse. The dataset has samples from 1980 to 2019 and among the features there is one called birth_date, labeled with the lifespan in years for each horse....
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Class-inflated factor. At what point do we decide to remove it?

If we have a dataset with a few string-type factors that have a lot of one class, at what point do we decide to remove the said class? This question just came to mind since I was practicing on a ...
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2answers
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How to select features when performing classification with a dataframe of multiple columns?

I have a dataframe of 50000 observations and I want to perform a classification task. But I'm struggling with features selection. I have 89 columns, which after getting rid of some redundant features, ...
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We know the subspace generated from the data instances, but we cannot constitute the origin space

I was wondering, what if we know the subspace generated F from the data instances, but we cannot constitute the origin space E that can be in higher dimension, and can easily lead us to the true join ...
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1answer
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how to deal with two high correlations feature which both has a low correlation with target

I am doing a prediction of house trade money. Here is the correlation matrix of features whose correlations are larger than 0.3 as follows: ...
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1answer
148 views

Why is Random Forest feature importance biased towards high cadinality features?

I understand how a random forest algorithm works but could someone tell me the rationale behind Random Forest feature selection being biased towards high cardinality features?
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974 views

Is numpy.corrcoef() enough to find correlation?

I am currently working through Kaggle's titanic competition and I'm trying to figure out the correlation between the Survived column and other columns. I am using <...
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Does it make sense to randomly select features as a baseline?

In my paper, I am saying that the accuracy of classification is $x\%$ when using the top N features. My supervisor thinks that we should capture the classification accuracy when using N randomly ...
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difference betwen predicting seen and unseen data

I tried to test my model with seen and unseen data (seen data are data that i used to learn the model). I figure out that as much as i increase the number of features seen data can be properly ...
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which is better: feature selection after dataset selection or dataset selection after feature selection?

In the datascience packages, normally feature selection is done from the features of the provided datasets. Which approach is better? a) feature selection by data scientist manually analysing and ...
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1answer
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Does feature selections matter to Decision Tree algorithms?

Background: Currently I'm working with my thesis project, which is to build a Tree-based ensemble methods for classification on a large data set. Before I started with modelling, I've spent a large ...
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1answer
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What measures can I use to find correlation between categorical features and binary label?

For analyzing numerical features, we have correlation. What measures do we have to analyse the relevance of a categorical feature to the target value? If there isn't a direct measure, how can we ...
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Getting the right architecture with dynamic feature selection

I am building a NN (with keras) to address a problem that is mappable to the following: Each sample is composed of ~250 features of which ~100 should be used to determine the importance of the other ...
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Orange 3 - How can a String feature behave as a coefficient?

I'm studying machine learning with data. I have a table including features and a target variable which is the price as in the following. When I want to figure out the coefficients to obtain linear ...
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convert significant features to a set of rules or information

Is there any way to set up some rules from features in a classification model? Assume that we want to classify an employee as someone who will be terminated or not. We found that average hourly pay ...
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1answer
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Best way to classify plots which are overlapping?

I have an experiment in which it was done under two conditions. For each condition, the experiment was performed 26 times. The output of the experiment is a plot with 70 time indices. I would like to ...
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How to deal with small amount of labeled samples?

I'm trying to develop skill to deal with very small amount of labeled samples (250 labeled/20000 total, 200 features) practicing on Kaggle "Don't Overfit" dataset (Traget_Practice have provided all 20,...
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Predicting U.S. suicide count based on a set of inputs

I'm trying to design a model (or multiple) that can predict the number of U.S. suicides for a future year, based on a few inputs--"age", "sex", "population" (of the age/sex), and "gdp_per_year". I'm ...
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How does SelectKBest order the best features?

I'm trying to run a quick univariate filtering on some data, using a t-test of independence, since my target is binary. However, when I run the filter using sklearn'...
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1answer
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Can we use pca for supervise classification?

My questions are: Can we use "pca feature selection" for supervised classification? What will happen to labels when we use dimension reduction? If I understand it right when we use pca for feature ...
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Selection of Features and Data in Random Forest

First, I am confused whether at each node in all the trees, do we randomly pick features from the lot to be pitted for best split or does each tree get a random subset of feature and then all the ...
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How to identify one-to-many relation and discard one during feature selection

My data has many features out of which two features have one-to-many relation something like state and country. Now I want to do feature selection to identify key independent features for given ...
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Accuracy of the model

I'm using this dataset and i'm trying to do logistic regression ...
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1answer
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How to combine features with different temporal scale in machine learning

We have various types of data features with different temporal scale. For example, some of them describe the state per second while others may describe the state per day or per month from another ...
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How to find which features have been selected by PCA algorithm?

I used PCA function in MATLAB to decrease features on my data set. By this code I can reduce features from 12 to 8(as an example). It works good but my question is that how can I found with feature ...
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1answer
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Does PCA decrease the feature on my Data set or just decrease the dimension?

I'm new in AI and sorry if my question is simple. I have a data set and want to use PCA to decrease the feature but after some research on the internet I'm confused about decreasing dimensions and ...
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Model for Differing Number of Rows per Observation

Looking to build a response model (click or no click) on marketing data which displays varying number of offers to a person. I don't want to model which offer they click but do they click any of the ...
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if new feature downgrade the score for xgboost what do I have to look at?

let say I'm predicting the housing price of Boston(kaggle). if I got some score x then I added new feature y_K if this new feature drop the score. what is wrong with this feature and what do I ...
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1answer
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xgboost and linear regression new feature analysis

For linear regression, seems like a new feature has to be a linear relation with the target variable. But If you make the new feature for the Xgboost, what do you have to consider to make a new ...
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Categorical vs continuous feature selection/engineering

I'm working with a dataset with a number of potential predictors like : Age : continuous Number of children : discrete and numerical Marital Situation : Categorical ( Married/Single/Divorced.. ) ...
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Having averaged trials which are less than the number of features

Suppose I have an experiment where I have 70 features and 48 samples. The target variable is binary (0,1) and the 48 samples are divided such that 24 of them correspond to outcome 1 and the other 24 ...
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Regression vs Random Forest - Combination of features

I had a discussion with a friend and we were talking about the advantages of random forest over linear regression. At some point, my friend said that one of the advantages of the random forest over ...
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Random Forests Feature Selection on Time Series Data

I have a dataset with N features, each one with 500 instances in time. For example, let's say that I have the following: Features ...
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2answers
439 views

Feature selection - SelectKBest sklearn

I would like to ask how to set paramater k in function SelectKBest for feature selection. I have now around 2300 features, so I think that default value 10 is not enough. Is there any approach, how ...
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1answer
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Score Columns in Azure ML Studio

So I have a data set I have successfully used to train a model, and have decent results. I am using a Two Class Boosted Decision tree for a Boolean output. So far so good. I now want to analyze each ...
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3answers
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Machine Learning, Imputing values that should be blank

Sometimes data sets contain variables that indicate the presence of an event and the value that represented the event. As an example say a teacher wants to predict the grades of his students. Some of ...
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1answer
320 views

Correlation between Time Series Indicators ( Stock Prices )

I am new to time series analysis and I am currently tackling a stock market prediction problem. I have a set of market indicators (such as Bollinger Bands, ADX etc) which are derived from the time ...
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How exactly do I extract the important features from strings for machine learning?

Forgive me for my ignorance. Linked below is an image of my dataset with 1000 tuples. https://i.stack.imgur.com/WHIlx.png I have the following questions (1) How exactly do I go about extracting ...
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2answers
133 views

Manual feature engineering based on the output

So, I'm working on a ML model that would have as potential predictors : age , a code for his city , his social status ( married / single and so on ) , number of his children and the output signed ...
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1answer
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Difference between Information Gain and Mutual Information for feature selection

What is the difference between information gain and mutual information? At this point, I understand that information gain is calculated between a random variable and target class for classification ...
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1answer
194 views

Collinearity and Multicollinearity in the features?

What are some advanced or basic methods most used by data scientists/ML Engineers to detect collinearity (or) multicollinearity between features?
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361 views

Including identifier in machine learning model as feature vs separate model for every identifier

I am new to machine learning and i am building a model to predict number of customers for the model branch at specific hour/season/other feature. I know it will be bad idea to pit id(...
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How do Bayesian methods do automatic feature selection?

Someone asked me this question and I do not know I answered it correctly. I answered the question in the following way: One type of Bayesian method is Bayesian inference and feature selection has to ...
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1answer
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LDA as a dimensionality reducer [closed]

I know how to use LDA as a classifier. But how to use Linear Discriminant Analysis as a dimensionality reducer to reduce the number of features and apply logistic regression on top of it. I am using R ...