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Questions tagged [feature-engineering]

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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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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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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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29 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
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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 ...
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1answer
48 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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7 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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15 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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48 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
31 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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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
34 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
10 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
41 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
152 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)...
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1answer
18 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 ...
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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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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 ...
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1answer
52 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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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
45 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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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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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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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. ...
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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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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 ...
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1answer
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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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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
87 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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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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15 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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21 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
75 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 ...
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1answer
62 views

Which features should I choose to create polynomial features?

Sometimes we want to use some features in our original dataset to create polynomial features in order to add non-linearity to our model. The question is how to choose those features? Do we choose ...
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How to develop features for deep learning from cart items data?

I wonder how to approach building set of features to feed deep learning model (eg convnet) from cart items data: 5pcs of product1 1pcs of product5 2pcs of product8 Assuming 30-50 products per ...
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How to avoid L1 regularization causing informative features to get a weight of exactly 0.0.?

L1 regularization may cause the following kinds of features to be given weights of exactly 0: Weakly informative features. Strongly informative features on different scales. Informative features ...
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1answer
24 views

Using cosine of measurement time as feature for decision trees VS NNs

I have a regression data set and I'm trying to do some feature engineering. The data set is foot fall coming into a store measured on the hour. I'd like to include the time of measurement as a ...
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10 views

transformation of categorical features with high cardinalility for regression

Before looking at 'word embedding' for categorical features in regression as discussed here, I would like to consider transformation similar to supervised ratio and weight of evidence as discussed ...
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1answer
50 views

How we can avoid making L2 regularization causing the model to learn a moderate weight for some non-informative features.?

Referencing to an example explained in free google machine learning course Imagine a linear model with 100 input features: 10 are highly informative. 90 are non-informative. Assume that all ...
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How creating bins for a numeric feature can enables the model to learn nonlinear relationships within a single feature?

I understood How binning of numerical feature would help build correlations between the feature & the predictor. For example For a regression problem, we can bucketize "population" feature into ...
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1answer
71 views

Why the z-scores values stays mostly between -3 and 3?

I was reading through the Google Machine learning crash course and I can't digest the below point: Scaling with Z scores means that most scaled values will be between $-3$ and $+3,$ but a few values ...
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1answer
18 views

Why wider range for a feature in Machine learning affects training?

I was reading through the Google Machine learning crash course and I can't digest the below point: If a feature set consists of multiple features, then feature scaling provides the following benefits:...
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49 views

How to create feature in survival analysis setting

I have a data set in which each row represents one room. The data set contains information/features about each room, e.g. temperature of the room, humidity. In each room there is a device which uses a ...
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63 views

Which features would you extract out of these time series? [closed]

I have around 2000 time series, each of around 40 values. In the image you see a random selection of 4 of those 2000 time series with a smoothed line in orange. I would like to calculate around 3 to ...
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1answer
43 views

Using categorical feature as both a continuous feature, and also doing One hot encoding. Is this overkill?

I am working on a Machine Learning regression problem, with a data-set where I have data from a period of several years. From the "date" feature, I extracted the week number (0-53). Next I am doing 2 ...
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112 views

What are the different influences of outliers regarding the feature scaling methods: standardization VS. normalization?

I've come to know that normalization (MinMax scaling) and standardization (Z-score normalization) on data have different influences from outliers in the data. In About Feature Scaling and ...
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how to quantify the patterns of multivariate distribution (e.g. clustered in the center vs. spread out all over the place)?

I am wondering if there exists a well-established way to quantify such patterns (please see the graph)? I guess there should be multiple ways to quantify it or multiple aspects that can be quantified. ...
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How could a variable length binary string be encoded as an SVM feature?

I have data which is a binary string, e.g. 10001001 or 111100000001. The length can vary between 3 and 13 characters in length. It represents a pattern found in nature where the length is variable ...
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Suggestions on using model in production 1 test at a time

I have created an Artificial Neural Network with 4 categorical features and a binary outcome either 1 for suspicious or 0 for non-suspicious: ...