Questions tagged [feature-selection]
Methods and principles of selecting a subset of attributes for use in further modelling
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3answers
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How to interpret feature importance (XGBoost) in this case?
I found two dominant features from plot_importance. My dependent variable Y is customer retention (whether or not the customer will retain, 1=yes, 0=no). My ...
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0answers
16 views
Feature selection or Dimension reduction in unsupervised learning
I'm trying to do Embedded clustering using kmeans.
This is customer data, so it involves a lot of sentences, so I'm using the universal sentence encoder before clustering.
But I should be doing a ...
3
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4answers
63 views
What does embedding mean in machine learning?
I just met a terminology called "embedding" in a paper regarding deep learning. The context is "multi-modal embedding"
My guess: embedding of something is extract some feature of sth,to form a vector....
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0answers
8 views
Do I discard all my dependent variables as proved by chi-squared test of independence?
I have 134 categorical columns in my data. 7 of which are categorical variables [ one variable is highly unbalanced and has 34 classes while all other variables just has 3-5 classes in each variable ...
2
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1answer
33 views
How valuable is a categorical feature that has a predominant category over all other ones?
Is a categorical feature that has almost equally distributed in it's category more important or the one which one of it's category is predominant over all other ones?
In data prepossessing step for "...
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2answers
39 views
Correlation feature selection followed by regression
I have quarterly results data for a company with around 100 variables. Total 60 quarters results are available (total records 60).
sample data: (only few columns & 10 rows)
I would like know ...
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1answer
20 views
How to predict specific user from session logs?
Let's say I have a dataset with 800 rows (40 entries for each of 20 users). The entries are user session logs (columns are - browser, os, time, date etc for a specific session).
Now each user has ...
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1answer
18 views
How can i test the performance of a model when the test data contains seen and unseen data
To test the performance of my model based on some selected features, i try to use unseen and seen data. However, when choosing the accuracy based on all data, the model is almost overfitting since ...
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1answer
13 views
How to interpret a random variable in the variable importance?
I have a problem, for simplicity let's say it is a binary classification problem.
I am trying to solve this problem using XGBoost.
A standard output plot for any ML algorithm, is the feature ...
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1answer
33 views
No correlation found between dataset features
I'm trying to build a classification model that predicts the price of New York taxi trips (year 2018).
Datasource page
Since the original file is very large (112 234 626 rows), I constructed the ...
2
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1answer
27 views
Multiclass classification with high number of classes, high number of features and small sample size
I am working on a biology related dataset with over 300K features, and I only have about 5K samples. I want my model to classify many classes. For this problem in particular the class is age. Each age ...
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1answer
38 views
Is there a Feature selection process for ARIMA model?
I have a dataset representing sales per day for certain products. It contains 30000 observations and 6 features (target included). Since my task is to make a prediction about the number of pieces sold,...
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3answers
41 views
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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1answer
8 views
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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1answer
69 views
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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0answers
18 views
How to decide the next step depending on the features
I'm a beginner in Machine Learning working on the Titanic problem. So far, I've been working on my features and was able to do a few things like changing genders to binary values, changing city codes ...
2
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2answers
100 views
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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0answers
13 views
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 ...
2
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1answer
38 views
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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0answers
5 views
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
67 views
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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0answers
11 views
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
26 views
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
42 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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2answers
200 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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2answers
35 views
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 ...
1
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2answers
54 views
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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0answers
11 views
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 ...
4
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1answer
52 views
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
182 views
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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0answers
10 views
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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0answers
19 views
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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0answers
21 views
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 ...
2
votes
1answer
46 views
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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0answers
29 views
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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0answers
16 views
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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0answers
136 views
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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0answers
30 views
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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2answers
53 views
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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0answers
17 views
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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2answers
59 views
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1answer
16 views
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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1answer
34 views
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 ...
0
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1answer
30 views
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 ...
4
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1answer
41 views
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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0answers
11 views
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
22 views
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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5answers
472 views
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.. )
...
0
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1answer
31 views
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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1answer
242 views
