Skip to main content
Share Your Experience: Take the 2024 Developer Survey

Questions tagged [feature-selection]

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

Filter by
Sorted by
Tagged with
1 vote
1 answer
268 views

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 ...
Romero Azzalini's user avatar
1 vote
1 answer
36 views

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 ...
shadowrain's user avatar
1 vote
1 answer
78 views

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 ...
No Name's user avatar
  • 21
0 votes
2 answers
36 views

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.
arshad's user avatar
  • 101
1 vote
0 answers
529 views

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 ...
The Great's user avatar
  • 2,565
2 votes
1 answer
1k views

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 (...
Alan CUZON's user avatar
2 votes
2 answers
344 views

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 ...
baltiturg's user avatar
  • 143
0 votes
1 answer
64 views

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 ...
Jeremy's user avatar
  • 1
1 vote
1 answer
60 views

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 ...
baltiturg's user avatar
  • 143
0 votes
1 answer
50 views

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 ...
Sahil Lohiya's user avatar
1 vote
1 answer
13 views

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 ...
Jason Wong's user avatar
0 votes
1 answer
24 views

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 ...
naman's user avatar
  • 1
1 vote
1 answer
108 views

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 ...
NotLost's user avatar
  • 11
0 votes
1 answer
266 views

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 ...
user140259's user avatar
1 vote
2 answers
115 views

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 ...
The Great's user avatar
  • 2,565
0 votes
1 answer
52 views

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 ...
The Great's user avatar
  • 2,565
3 votes
1 answer
2k views

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 ...
The Great's user avatar
  • 2,565
0 votes
1 answer
168 views

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. ...
Garreth Lee's user avatar
1 vote
1 answer
250 views

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 ...
Ryan Kung's user avatar
1 vote
1 answer
125 views

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 ...
Learning_and_xbox's user avatar
0 votes
1 answer
83 views

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 ...
william007's user avatar
2 votes
1 answer
1k views

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 ...
Erik M's user avatar
  • 83
0 votes
0 answers
40 views

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 ...
aadil gani's user avatar
1 vote
1 answer
294 views

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 ...
Aditya Singh Rathore's user avatar
6 votes
4 answers
3k views

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 ...
Anatole's user avatar
  • 181
0 votes
1 answer
246 views

How to use hierarchical variable in a ML model

I am working on a binary classification problem with 1000 rows and 20 variables. I have variables like product_id, city, ...
The Great's user avatar
  • 2,565
1 vote
1 answer
183 views

Feature importance has more variables than included in .csv? [closed]

I have a .csv dataset with 26 variables, ranging from Age to Weight and so forth. I plotted a feature importance plot with; ...
user113243's user avatar
1 vote
2 answers
211 views

Hidden Markov Models: Best practices in selecting observable variables

I am just getting started with Hidden Markov Models. In selecting my observable variables, there are some where I believe the recent change in the variable is potentially more predictive than its ...
Tom's user avatar
  • 63
2 votes
2 answers
781 views

Categorical to One hot encoding - Big data [closed]

I have a sales dataset which consists of binary label as output - "Business win" and "Business loss" of our products. We have a set of 1st level customers (lets call that group as ...
The Great's user avatar
  • 2,565
1 vote
0 answers
16 views

Bayesian Linear Regression using the Kernel Trick vs Constructing features using Kernels as Prototypes

How different is it to do Bayesian linear regression using the GP approach (kernel trick) versus constructing features using kernels to prototypes? As far as I know, this very basic question is ...
Robert's user avatar
  • 23
1 vote
0 answers
42 views

Feature Scaling + Selection when target is imbalanced

If my target is imbalanced, when should I do target balancing in preparation for modeling? Before feature scaling and selection? After feature scaling and selection? If I am doing backward elimination,...
Denisse's user avatar
  • 11
1 vote
1 answer
718 views

Why categorical variable with high cardinality is not preferred but not in numerical variable?

I researched online a bit about categorical variables with high cardinality. Many posts and papers just stop short and conclude that 'it skews model's performance' without going into details why and ...
Student's user avatar
  • 411
1 vote
0 answers
135 views

RFECV for feature selection for imbalanced dataset

I am new in machine learning and just learned about feature selection. In my project, I have a dataset with 89% being a majority class and 11% as the minority class. Also, I have 24 features. I opted ...
laguna's user avatar
  • 11
2 votes
1 answer
87 views

Using cross validation score to perform feature selection

So to perform my feature selection I ran cross validation over and over again, each time trying different subsets of my attributes and repeated this until I got the best cross validation score I could ...
Rubiks cube's user avatar
2 votes
1 answer
1k views

Is it normal for exhaustive feature selector to run for three days?

I'm trying to optimize my features in a dataset to get a better predictive model. I used Exhaustive feature selector from mlxtend. This checks all possible combinations of features. I have a dataset ...
PlatinumMaths's user avatar
2 votes
1 answer
48 views

Training a Fuzzy Distance for Clustering later

I have a set of strings $ s_i \in S $ and associated labels $ y_i $, where $ y_i $ could possibly be null. There are many labels, but the cardinality is much smaller than the strings. $$ 1 << |\{...
Nauda's user avatar
  • 23
2 votes
0 answers
25 views

Comparing machine learning algorithms on features selected by a neural network

I'm reading a paper where they use a neural network to select 9 features from tabular input data with 20 features. And then, this is what feels weird to me, they run several machine learning ...
alepfu's user avatar
  • 51
1 vote
0 answers
18 views

vertical or horizontal storage of timesteps in feature store

I'd like to use a feature store to store some time series and I asked myself what's the best way to store the timesteps. Is it better to store each timestep horizontal and then doing windowing after ...
seb2704's user avatar
  • 111
0 votes
1 answer
99 views

Should I add string feature columns?

If my dataframe looks like this: ...
futuredataengineer's user avatar
1 vote
1 answer
50 views

Clustering with Highly separable features

I noticed that in my dataset a particular column is highly separable where it splits the data perfectly into 5 distinct classes (re-evaluated where class2 means better than class1). I would like to ...
Cosq's user avatar
  • 13
1 vote
0 answers
40 views

What is the best way to group similar columns in a dataset

I have a datasets with many columns (from 16 to 2500 columns) I have built a similarity function that rates the percentage of how similar these columns are. Example: ...
asmgx's user avatar
  • 549
1 vote
1 answer
206 views

Algorithms for casual feature selection for continuous Y

Currently I have been trying to find some good algorithms for feature selection. Using correlation or other non casual type of method will not be the right way to do a feature selection. I'm am ...
minattosama's user avatar
0 votes
1 answer
116 views

Feature importance by random forest and boosting tree when two features are heavy correlated [closed]

I have asked this question here but seems no one is interested in it. Here is my understanding, pls correct me if there is any misunderstanding: Tree models is used ...
user6703592's user avatar
1 vote
0 answers
28 views

EDA and Attribute selection [closed]

I have a dataset regarding traffic-violations. The attributes are as follows: ...
user127268's user avatar
1 vote
0 answers
41 views

Transparent Matching / Recommendation System [closed]

I am thinking of a matching/recommendation algorithm which matches students to the right teachers for their individual problems. The dataset would look like this: Student Name Age Gender Weak ...
alexryder's user avatar
0 votes
1 answer
1k views

Find most important and least important features for clustering algorithm

I am experimenting with clustering algorithms, like K-Means. Right now, I use all variables as input for the clustering algorithm. I am wondering if it is appropriate to do feature selection for ...
Bernhard's user avatar
  • 101
2 votes
0 answers
43 views

Random Forest importances vs Feature Permutation: cummulative sum of importances are 1 and 0.1, respectively. Make sense?

I am performing feature selection by using two methods: MDI (RandomForest importances) and Feature Permutation, in order to compare what are the features considered relevant for both methods. My ...
mjbsgll's user avatar
  • 121
1 vote
0 answers
88 views

How to implement kfold and cv into Hybrid feature selection and evaluate the classification model performance?

I have been working on a Hybrid feature selection combined with hyperopt package for hyperparameter tuning and I am thinking about evaluating the performance of several model classifiers. I looked ...
WDpad159's user avatar
  • 111
1 vote
1 answer
98 views

How should I engineer features for Named Entity Identification task?

I was working on Named Entity Identification (not recognition) task. In this NLP task, given a sentence, model has to predict whether each word (aka token) is named entity or not. The dataset used ...
Rnj's user avatar
  • 225
1 vote
0 answers
45 views

Distance calculation for nonlinear features

Dear Data science community, Please see the attached. I plotted my data using t-SNE. In the figure, group A and B are 100% separable with random forest model. I want to calculate the distance of ...
Taku Kobayashi's user avatar

1 2 3
4
5
20