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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.

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23 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 ...
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0answers
12 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 ...
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0answers
7 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 ...
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0answers
5 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,...
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1answer
47 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 ...
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0answers
20 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 ...
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0answers
18 views

Performing significance test with respect to cross validation

While performing sentiment analysis, I am trying to assess whether my approach using a novel feature set (similar to the delta-idf technique) outperforms the tf-idf metric using significance analysis. ...
3
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1answer
80 views

Is it advisable to use output from a ML model as a feature in another ML model?

Can I use the probability score generated from a Machine Learning model as a feature in another model? For example, say we have a model which generates the probability of an ad being bad. Lets call it ...
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0answers
29 views

Feature engineering for sheet music

I have a large dataset of digitized music scores that I'd like to use as input to a network. Initially, I'm looking to train networks to identify key signatures, tempo, dynamics, etc. from the raw ...
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0answers
13 views

Sterling vs Combustion Engines [closed]

This may be difficult to answer but, theoretically, if you had a car with a sterling engine that produces say approximately 300 horsepower how much of any specific heat source would need to be used if ...
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1answer
33 views

Feature engineering using the target/dependent variable

I am a beginner and my question relates to feature engineering. My task is to help develop a model which predicts whether a customer request is a fraud case. A variable in the dataset is the ...
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1answer
46 views

Are legacy values useful for regression models?

I'm building a model that predicts house prices in order to learn some regression techniques. Currently I'm trying to engineer features that might be significant when predicting prices. I got a hold ...
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1answer
31 views

On starting feature engineering

I would like to start my feature engineering process by first selecting a subset of features that are highly correlated with the target feature. However, if I do select let’s say the top k in terms ...
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2answers
64 views

Feature selection using PCA for linear regression

I am using PCA to the training data set to do feature selection before applying linear regression to build a classifier model. In this scenario, would it be useful to use ridge regression to ensure ...
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0answers
18 views

Row aggregation of multiple records

We have a syslog dataset with different Timestamps and 3 other features pertaining to syslog information such as Process, Trace information, SeverityType etc. Below is the dataset format with ...
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0answers
9 views

Feature engineering for verb\non-verb classification

Suppose we have data of X = words, and for each word we have a label indicating whether the word is a verb or a non-verb. So, y = labels. Assuming we can build all the unigrams and bigrams of each of ...
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0answers
13 views

Predicting Transformation

One of my friends found the paper An Empirical Analysis of Feature Engineering for Predictive Modeling. We were discussing the models the author wrote about. He wrote: If the machine-learning model ...
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0answers
13 views

Assigning weights to the features used in content based recommendation

I am trying to make a recommendation engine for book business which has following features associated with the books: Book Region Book Market Segment Publish Date Book Genre Book Type and so on ...
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0answers
26 views

Strange encoding for categorical features

I am reading through https://arxiv.org/pdf/1609.06676.pdf which presents an extension of the isolation forest algorithm so that categorical features may be taken into account. On page 5, the authors ...
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1answer
14 views

How to deal with varying number of intervals and hence varying number of features dividing an audio signal while classifying these audio signals?

I've $2000$ audio signals, each divided into a number of time intervals/time frames of $50$ miliseconds (ms) and these signals have overlaps for $25$ ms. Now, the audio signals being of different time ...
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0answers
56 views

Target Encoding: missing value imputation before or after encoding

I want to perform a target encoding for my categorical features although I am not sure when to perform the data imputation if any of them has missing values. Let's say I have a few continuous features,...
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1answer
13 views

preparing free text column for regression

I have a column X which contains occupation/profession as an independent variable as free text, which is very much correlated with a continuous dependent variable. What techniques do you usually use ...
2
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1answer
78 views

Why feature transformation is needed in machine learning & statistics? Doesn't it affect the “interaction” between features?

Before feeding machine learning models, we can do data transformation and feature scaling depending on data distribution. For example, if a column is skewed, we can use Box-Cox transformation to ...
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0answers
10 views

Multiclass classification with a balanced dataset and one high-priority label

I have a balanced dataset for a multi-class classification problem with one high-priority label (this ought to be classified properly at all costs). How do I go about creating a workflow for this ...
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0answers
17 views

How to perform feature scaling on noise removel process?

i'm working on dataset contain machinery sensor data. each column(feature) represent different sensor data(pressure, temperature, speed, etc) of the machine part. here task is to predict normal ...
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0answers
52 views

Longitudinal Data with Equal Outcomes Within Individual Samples

I need to prepare some data for plugging into a predictive model. The data is in tidy format, but it comes from an audit table, i.e. every change made to a record is recorded and stored as a separate ...
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1answer
33 views

Using outlier records as a feature in model building

I am exploring the Big Mart Sales III dataset and trying to understand if using outlier rows to build a feature for predictive modeling is a sound and correct approach. This is how I have proceeded ...
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0answers
59 views

How to use seasonal features in time series regression with models such as xgboost?

I have a hard time understanding how one can create seasonal indices such as a yearly mean or (x - yearly mean(x)) and use them as predictors for monthly n horizon forecast. For example: I want to ...
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1answer
56 views

When using linear function approximation how (and why) should I incorporate the actions into the feature vector?

When reading R. Sutton: Reinforcement Learning - An Introduction (2nd edition), in chapter 10.1 Episodic Semi-gradient Control, the Mountain Car problem is mentioned and as an example it is solved ...
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1answer
11 views

Good way to use word similarity as a feature in supervised ML on text

I have a pretty low N data set of small sentences tagged with a label. I would like to create a classifier on this dataset. The word choice is not very variable since the domain is pretty specific. ...
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1answer
89 views

How to engineer a bimodal continuous feature for use in Decision Tree?

I have a predictor that exhibits "bimodal" behaviour. How can I engineer this feature to improve performance within a Decision Tree? For an intuitive example, consider how a binary flag of "moves ...
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4answers
248 views

Using Trend as a feature in time series sliding window?

I have a time series, and i am using overlapping sliding window to extract features from each window and label it accordingly. In this Overlapping window of size n, i want to extract trend (linear fit)...
1
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1answer
23 views

Given two features, one a string and other a categorical, what are the encoding rules?

I have two features in my dataset I'm using to help predict a binary outcome. Based on my features, I'm trying to figure out which I need to drop a dummy to avoid the dummy trap. One feature is a ...
2
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1answer
109 views

Random Forest Regression with sparse data in Python

I am working on a Random Forest regression model to predict housing prices. I have about 500k rows of data with the following information: 1.House area in square meters. 2.Number of rooms. 3.City. ...
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0answers
26 views

When deciding how to scale features, do the types of activation function matter?

Typically there are two types of feature scaling methods: Z-score scaling (standardization) and Min-max scaling (normalization). Standardization normalizes each column towards a mean of zero and ...
0
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1answer
90 views

Should I apply log transformation to column with long-tail distribution before clustering? [closed]

I am doing clustering on a given data. When I plot the distributions of the individual features of this data, I found there are many columns that shows "long tail distribution". I am wondering ...
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0answers
43 views

How do the kitchen sink approach used to extract Algorithm's feature?

Hi while reading the article of Predicting Unroll Factors Using Supervised Classification of Saman Amarasinghe and al. they mentioned that they used kitchen sink approach for features extraction. ...
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2answers
46 views

How to use last predicted value as feature? NLP NER mission

I'm performing NER (Named entity recognition) For example: Seq: When Donald Trump announced... Tags: O B-Person L-Person O When I'm predicting ...
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0answers
16 views

is random projection a linear or non-linear feature extraction method?

The dimensionality reduction has two different types: feature selection and feature selection. As far as i know, the random projection cannot be a feature selection method. Therefore, is it a linear ...
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0answers
28 views

How to extract static program features automatically?

I did want to know how to extract statistical features from program. Like supposing I wanna do an extractor for loops programs so features in this case could be The loop nest level. Is the loop ...
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0answers
30 views

Feature reduction of Biological time series signals

I have a data set of biological signals (PSG signals); the dimension of the signals is high (850 features for each sample). I am looking for the best way to reduce the dimensionality of the signals. ...
0
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1answer
39 views

Is there an efficient approach in machine learning when I have the confidence (uncertainty) values for the input features?

Could you give me some comments? I'm looking for a better approach when I have confidence (uncertainty) values for each input feature. For example, let's say each class has 3 features. ...
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0answers
78 views

Correct numerical feature transformation for neural networks

Model: I am working on a "shallow" (3-layer) auto encoder neural network. The input layer receives a, say 25-dimensional, vector $x$ of numerical elements representing client purchases. Several ...
2
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1answer
171 views

Feature Engineering: Should I drop features that can be calculated using other features?

In feature engineering, should I drop all features that can be calculated using other features? For example, let us say that we have this dataset: ...
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0answers
12 views

Analysing features of several classifiers

I am currently working on a small sentiment related project and need some advice regarding the evaluation. I trained different classifiers (Naive bayes, SVM with RBF kernel, SVM with linear kernel) ...
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2answers
117 views

Highly correlated engineered features any helpful?

Take a car price predictor for an example. If you know the model and year of a car, you can extrapolate facts ("engineer features") about the car. For example: city and highway mpg, number of doors, ...
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0answers
21 views

Can we use non-Invertibility property of a matrix to detect linearly dependent features?

In order to find whether two features (such as the size of a house in feet^2 and metert^2 ) are linearly dependent or not? One way of finding it is! you take the transpose of the feature vector and ...
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0answers
26 views

State of the art in feature extraction from review text

I am working on a sentiment review classification problem and so far i have explored POS tags, synsets, N-grams, word2vec, tf-idf, doc2vec, glove and fastext vectors as features. I am wondering what ...
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0answers
28 views

Why would the mean and standard deviation of the first and second derivatives of a signal (e.g. EDA) be useful?

When analysing a signal, e.g. EDA, I intuitively understand why one would want to determine the mean and standard deviation of the signal. The mean would tell us the average value of the EDA signal, ...
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1answer
139 views

Why it might be bad to have too many feature levels

I am aware that a feature with too many levels might be bad for a number of algorithms (e.g. Logistic Regression). A typical approach to fix this would be to group the categories with a frequency ...