The Stack Overflow podcast is back! Listen to an interview with our new CEO.

Questions tagged [feature-engineering]

Feature engineering is the process of using domain knowledge of the data to create features for machine learning models. This tag is meant for both theoretical and practical questions regarding feature engineering, excluding questions asking for code, that would be off-topic on CrossValidated.

Filter by
Sorted by
Tagged with
0
votes
0answers
6 views

How to model dependency between input features when building a classifier

I have dataset with the shape of (1000,20) (1000 rows, 20 features) and I want to build a classifier for it. However, most sk-learn algorithms assume the these 20 features are independent. In my ...
0
votes
0answers
12 views

Including ratios as features in machine learning algorithms [duplicate]

Assume that body mass index (BMI) is a good predictor for early death which we are trying to model using a host of different algorithms. A colleague posed the following to me: would it be better to ...
0
votes
1answer
21 views

Add Absolute Value as a Feature

In a machine learning context, does it make sense to have both a measurement and its absolute value transformation as features? There are already ~120 features in this predictive model (an elastic ...
1
vote
1answer
30 views

basic question about feature selection

I am new to machine learning. I have a basic question about feature selection. I have a dataset with 100 features which I used to regress an output Variable. When I do regression with all the ...
1
vote
0answers
16 views

How to treat low frequency continuous variable in machine leanring

Hello I am working on machine learning model for count data, and I have various features that are highly skewed. The frequency table for one of the feature is given below. ...
0
votes
1answer
18 views

Training Dataset vs Feature

What is the difference between training data and features in a machine learning model? Is feature just building blocks of training dataset?
0
votes
0answers
8 views

how to extract features when observations and feature set are almost equal?

I need to do a regression where I have in total 750 observations and nearly 500 features. I am struggling to come up with feature engineering technique that gives top 10 or 15 non col-linear features ...
-1
votes
0answers
20 views

Do I have to scale my features again to the test set that I want predictions for? How do I apply and Export model predictions as a CSV in python?

I was able to generate an SVM model with an accuracy of 96%. The features have all been scaled (StandardScaler) and I have also upsampled the minority class (y=0). I want to be able to apply this SVM ...
0
votes
0answers
25 views

Encoding Ordinal categorical data using Python

I am trying to encode ordinal data. I found a post which suggests a way to do it. Where to find a guide to encoding categorical features? This seems to make sense. For nominal data, I would do the one-...
1
vote
0answers
18 views

Variable selection, variable reduction, and handling sparsity for binary text classification

I am trying to do a binary text classification using support vector machine. I am wondering if I am doing it right and I'd like to look for some answers to the questions in mind. The following ...
0
votes
1answer
61 views

Decide for three Bernoulli samples are some of them from the same distribution (or non of them) ? (do not use t-stat ) [duplicate]

Please pay attention, I am interested in Bernoulli samples, and hope to find criteria specific to Bernoulli distribution, not using s Student's t-statistics or Mann-Whitney or etc., since their use ...
0
votes
1answer
24 views

How to perform feature normalization for training regression CNNs with datasets with different distributions?

I'm teaching a 3D convolutional neural network to learn different functions that map a 3D scalar field into another one. It is essentially a regression problem. The distribution of input datasets ...
0
votes
0answers
6 views

Predict with average values of dependent features

I created linear regression model to predict story points by individual team member (in sprint). Since story points are relative by sprint team, I trained my model after scaling story points to 0 - 1 ...
4
votes
3answers
152 views

Learning a target feature from data

I have a dataset of customers (infos about them, as well as their buying behavior) to whom ads are sent regularly. How can I design a target feature that will result in a good model that predicts when ...
2
votes
1answer
29 views

Feature Engineering for Multiple Regression

My goal: I want to predict a single output value from multiple inputs, some of which are numerical and some of which are categorical. To do this, I plan on building a multiple-regression model (...
1
vote
0answers
10 views

How to learn embeddings from lists of data?

My objective is to learn an embedding for translate sentences from one language to another. The problem is that my data looks like this: ...
6
votes
2answers
519 views

How do I deal with large amout missing values in a data set without dropping them?

I am trying to build a binary classification model which predicts whether a patient would me infected with a certain disease at the the end of his hospital stay or not. The features that I have are ...
1
vote
0answers
18 views

For a regression model, can you transform all your features to linear to make a better prediction?

I was thinking. Would it be a good approach to check your features one by one (assuming you have a manageable amount of them) and see the relationship they have with your target variable, if they have ...
2
votes
1answer
36 views

How to combine different results of a stochastic classifier

I'm writing a paper about a machine learning-based system and using CNNs on a GPU cluster to compare two methods of feature engineering. Due to the non-deterministic nature of the algorithm, I couldn'...
0
votes
0answers
8 views

What are some line feature extraction tools?

I hope to find a feature extraction tool/method that specializes in extracting the features of a line, such as length, curvature, etc. Are there any such tools? The ones I found are image processing ...
0
votes
1answer
17 views

feature engineering - dealing with conditional numeric variable

Let us say I have a dataset with 2 columns X1 and X2. X1 is dichotomous and has 2 level yes and no. X2 is numeric and is only completed when X1 = yes. Is it possible to keep the numeric column X2 as ...
0
votes
0answers
18 views

Effectively Standardizing Time Series Data

I'm currently taking an online course on Machine Learning with Time Series data, only the instructor proposes a calculation, ostensibly for centering and standardizing feature data, that seems way ...
0
votes
1answer
22 views

dealing with missing values on train set Or combined set

When dealing with missing values, I see some people calculate some value on train set (mean, median, zero, etc) and use that value to fill the missing values on both the train set and the test set. ...
1
vote
0answers
21 views

Is consistency among classifiers a good measure for feature sets?

I have two feature extraction approaches (or feature sets) to describe the same data, the feature set 1 has overall consistent results among 4 different classifiers (SVM, Logistic Regression, Adaboost ...
0
votes
0answers
14 views

Feature engineering on test set

I have a train set and a test set given separately. For feature selection part, I think I should use the same features for train and test as mentioned here. feature selection on training and test ...
1
vote
0answers
15 views

Feature engineering procedure using optimism corrected bootstrap

I have a dataset with ~600 datapoints, 49 categorical features (five possible categories), and a binary outcome variable. The dataset is incredibly imbalanced, with just over 3% of the outcomes in the ...
0
votes
0answers
22 views

How to handle associated features in machine learning

I am working on a classification project in which some features are linked and I'm not sure how to handle them. I will simplify my project like that : There are different jobs, and multiple ...
2
votes
1answer
26 views

How to use fresh data when target prediction period is long?

I'm using supervised learning on monthly activity data to predict when a customer buys a particular product. This product is typically bought infrequently and at the moment my target variable is ...
1
vote
1answer
49 views

Why Does a Monotonic Transformation Of Dependent Variable Change Variance Explained In Random Forest

I am working with the Boston data set in R. I have read that random forest should be able to deal with untransformed data. In my example I do a log transformation of the dependent variable. My ...
1
vote
0answers
64 views

Polynomial Regression and Feature Transformation

According to Polynomial Regression concept, high order terms in a model such as $x_1^2, \,\, x_2^3, \,\, x_1^2x_2$ are replaced with new features. In this way, the model equation is converted to ...
1
vote
2answers
54 views

How to handle missing data in timeseries classification?

I'm using 16-channel, 400-Hz, standardized EEG data to train CNN-LSTM for seizure classification. The data contains periods of no recordings (flatlines) - spanning anywhere from seconds to minutes - ...
0
votes
0answers
8 views

Tensile test results

I am currently writing my thesis, where I have to investigate if a surface treatment increases the adhesion (between tape and a plastic surface). For this purpose, I have among other tests, performed ...
0
votes
0answers
12 views

Using groupwise averages of dependent variable as predictors

Let's say I have a binary classification problem with a number of categorical predictors. Is there an issue with creating new features by calculating the group wise average of one or more of the ...
0
votes
0answers
14 views

How important are hyper-parameters in SVM based RFE feature selection?

How important are hyper-parameters in svm based RFE feature selection? For feature selection using RFE (recursive feature elimination / selection), I have seen some publications where only "external" ...
3
votes
1answer
107 views

Encoding IP Address as a Predictor in Machine Learning

Is there some approach to "encoding" IP Address (IPv4) in a way that the new representation can capture both cardinality and the statistical distribution of the full range of IP address and also ...
0
votes
1answer
13 views

Feature selection on full training set, does information leak if using Filter Based Feature Selection or Linear discriminate analysis?

In order to test a potential classification set, usually some data is kept as a holdout set, and not used for inner-cross-validation or model training. However, what happens if too many features ...
0
votes
0answers
18 views

Subtraction between features before performing PCA and training a classification model

In my dataset, I've created moving averages for historical indicators. Is it pertinent to perform subtraction between those moving averages before PCA? My stats/calculus feeling: as PCA is a ...
0
votes
0answers
20 views

Model Based Feature Selection vs Wrapped Method Feature Selection

I read about Wrapped Method Feature Selection, I get that it is to look at the features then test them against the predictive model that we need then find if it has an effect or not and then decide to ...
0
votes
0answers
11 views

Feature engineering on hierarchically structured data

I have a regression problem to predict $y$ and a predictor categorical variable $x$ that has about $500$ different categories. $x$ is an occupation code of a company and is hierarchically structured, ...
0
votes
0answers
43 views

What is the methodology behind Filter Based Feature Selection (i.e. Pearson correlation, etc.) on Azure Machine Learning Studio?

Filter Based Feature Selection on Azure Machine Learning Studio supports feature selection and ranking through Pearson Correlation, Kendall Correlation, Spearman Correlation, Mutual Information, Chi ...
1
vote
2answers
185 views

How to calculate Helmert Coding

I am trying to understand how Helmert Coding works I know it compares levels of a variable with the mean of the subsequent levels of the variable, but what are these levels and how can I calculate ...
5
votes
1answer
197 views

How would someone use curves as an input to a supervised learning model?

I was asked this question during a test and couldn't figure out the answer: You have a set of curves against time $X_i(t)$ that you want to use as input to a supervised learning model. The curves ...
0
votes
0answers
14 views

Data Leak during data acquisition for credit scoring

I have a few questions about data leaks. Particularly, I'm interested in a credit scoring data can have leakages. I'm at the stage of data acquisition and I suppose I have target leak but not sure. ...
0
votes
0answers
22 views

Manually creating target variable, getting f1 score 1

I am building a classifier for user engagement in my website. Basically, since there are no "proxy" for engagement, i.e. there is no pre-defined target variable, I came up with minimum thresholds ...
0
votes
1answer
40 views

What is meant by Low-Order combination of features?

I came across a Machine Learning paper that talks about input with low-order combination of features. A statement says: The initial feature is used as the input of the model, and the non-linear ...
0
votes
0answers
15 views

Feature analyzing methods

I am a little confiused how to interpret following situation: I am trying to implement a image classification task using hog+SVM. For that i tried to analyze and understand the properties of the ...
0
votes
0answers
9 views

Avoid learning certain known features / selecting alternative features in neural network training

In an application of neural network to a classification problem, often time one trains the network to pick out different features in the input data set (learnt by the hidden units) and classify the ...
0
votes
0answers
7 views

Speaker normalization of features before model training

I am building a model using a supervised machine learning based on features I extract from speech signals. The features include MFCC, auto correlation and energy derivatives. According to this paper,...
1
vote
1answer
97 views

One hot encoding vs apply the average of the label to each category

I have a fairly reasonably sized dataset (row>50k). And I'm looking for the best way to utilize some of the categorical columns. For purpose of this question, let's say that one of the categorical ...
0
votes
0answers
29 views

How to handle order invariance (and variable length) of certain sample features in machine learning input vector?

Looking into what can be done (or if it is even an issue) when a sample vector xm contains a variable length subvector of features that are similar and order invariant, so sample vector would have the ...