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Questions tagged [feature-construction]

Variables (used for prediction or explication) used in regression or regression-like models (like clustering, discrimination). Use this tag for questions about constructing such variables or selecting the best among them.

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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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38 views

SVD PCA reconstruction of data [duplicate]

I have some data about the $\{noise,~ size,~ speed,~ length,~ width\}$ of cars. I have performed SVD, and I want to reconstruct my data using only the first 2 principal components. I subtracted mean ...
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(Feature Selection) Meaning of “importance type” in get_score() function of XGBoost

I'm trying to use a build in function in XGBoost to print the importance of features. My code is like ...
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1answer
18 views

Interpolating principal component

In my thesis, I use PCA from a bunch of WVS responses to measure the social capital of a country (aggregating principal components to country averages). However, WVS provides a quite low frequency of ...
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16 views

Number of lags in Ljung-Box test for feature extraction

I'd like to cluster time series based on static features, one of which is the Ljung-Box autocorrelation. After reading this question on "How many lags to use in the Ljung-Box test of a time series", I'...
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1answer
47 views

What's the term used when identical feature vectors map to different target variables?

Context: Fitting a Machine Learning Algorithm on a labeled dataset. For a feature vector [a,b,c] and a labeled output/target variable, what's the term used when identical feature vectors map to two (...
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18 views

Classifying matching object pairs with Python / sklearn

I have a ground truth dataset of around 600,000 objects. Each object has 100 features, and for each pairwise combination of objects, I have a 0-1 relationship ("equal" / "not equal"). What is the best ...
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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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13 views

Learning useful semantic representations of data

Training a neural network on its final task (e.g. classification) right from the beginning is not always the best way to go. I'd like to make a short list of recognized methods of motivating a NN to ...
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11 views

Data Representation for sequential input NN

The essence of the problem I want to model is to go from input sequences(length n) to a "distance matrix"(n x n). Although for this distance matrix we are not actually predicting $n^2$ values because ...
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1answer
22 views

What is the definition of a “rectified conv feature map” in a convolutional neural network mentioned in the paper of “visual explaination”?

I have read the answer of the question What is the definition of a “feature map” (aka “activation map”) in a convolutional neural network? But I don't think that it is same as what I want. I ...
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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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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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Predicting based on regressor measured over time

Suppose I want to predict whether a patient has post-operative complications. In addition to some 'usual' regressors, such as age and weights, I also have access to variables that are measured over ...
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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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25 views

Sparse coding and feature learning

Recently I tried to understand sparse coding and its application to classification. But there is no way to check whether I understand correctly, so I have a few questions about this algorithm. I ...
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1answer
28 views

Algorithms for Graphs Clustering

Which methods is available for graph clustering? The most information by query "Graph clustering" concentrated on the finding set of nodes in the one large graph (or graph partition), but it isn't my ...
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116 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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1answer
26 views

Addressing “Unbalanced Features” or Feature Taxonomy for Nearest Neighbor / Similarity Calculations

The main question is how to address an imbalance in representation of feature "sets" when calculating similarity. I'll motivate with an example scenario: Suppose we have objects described by a binary ...
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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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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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24 views

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
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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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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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14 views

Combining framewise features in video classification

I have a task of classifying videos based on feature vectors generated for each frame in the videos. The individual features are numbers between 0 and 5, and the feature vectors have about 20 elements....
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2answers
44 views

Hiding features from your client

I have to automate a yes/no type business decision problem for a customer (think: Is the use of chemical compound X beneficial in combination with chemicals A,B,C?). He dumped on me a very large ...
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Proving new features theoretically help learning

I've been recently working with feature construction. I was wondering, whether how one would go about proving, that given a standard classification setting, a new feature can improve a classifier's ...
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195 views

How to interpret/choose alpha in ridge regression

I have questions on how to apply ridge regression on my data set, which has about 75 samples with 8 features (x's) and usually 3 targets (y's). I tried the following feature engineering methods. ...
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14 views

Any way to force positive loadings in factor analysis?

I want something like factor analysis, something that will tend to yield factor loadings that are simple linear combinations of the factors (where most loadings are 0), but I have a very strong prior ...
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1answer
53 views

Correlation between continuous variable and a vector of unknown sizes

I wish to calculate the correlation between two different types of variables, namely a continuous variable and a variable containing a series/vector/list of floating numbers. I'm unsure about the ...
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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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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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27 views

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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Bag of Visual Words: is feature extraction even needed?

I'm currently implementing a BoVW as part of my lab project. The steps the algorithm used are as follows: spliting all photos into patches cluster these pathces using K-means based on pixel values of ...
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2answers
144 views

Timestamps in Ridge Regression Scikit Learn

I am trying to transform data for use in regression, most likely the Ridge or Lasso technique implemented in sklearn.linear_model. My training data contains time ...
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33 views

How to engineer binary vectors (representing store layout) into new variables (for predicting store sales)?

Suppose we want to predict store sales with store layout. The raw data contain a binary vector for each store representing the configuration. For example, when it has a table, a counter, and a shelf, ...
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32 views

How to efficiently transform a qualitative variable into a quantitative variable?

Some context : I'm looking for an optimal way to transform qualitative variables into quantitative one, in order to combine several models (boosting), because some of them don't handle qualitative ...
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24 views

Testing predictive value of different feature sets with random forest

I have two sets of features, each with the same targets (binary patient/control). Set A had five features, while set B has 3 features. For each set of features I tune the hyperparameters on the ...
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1answer
55 views

DNN methodology and feature concatenation

I'm using someone else's job and I have a question that I cannot solve. This work uses a DNN to match an electrical resistance to a bend angle. This is not very important, just for the context. So,...
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15 views

How to model a field whose feature values can be all 0 in regression model

I want to predict price of products. For each product, I use one-hot encoding to model their features. These features come from a limited set of fields (i.e., product attributes). For example, a field ...
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Assigning scalar values for PID for order in Neural Network

I have built a neural network using Windows Processes. I started off with only two features, the file path with parent process, and the file path with child process. I am slowly adding features for ...
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1answer
92 views

How can I know when not to answer questions and shut up?

I'm trying to find a way to prevent Intelligent Agents with Reading Comprehension and Question Answering abilities to answer when they are not confident enough that the y can find an answer from the ...
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17 views

Data transformation for ANN models

I'm trying to predict the customer churn using company's user care center data. The initial table looks like this: ...
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1answer
14 views

Stubbing features in text classifications

I'd like to classify short texts of chats to sentence types (i.e. an informational question, a request, a statement etc). In some of the texts there is extra information, like mentioning of another ...
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Aggregation of sequence data

I have sequence data of students playing exercises and succeeding/failing. For example: ...
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17 views

Find function that best explains response variable [duplicate]

I played around a little, creating a new predictor as a combination of the first three: $p_4 = (p_1-p_2)/p_2/\sqrt{p_3}$, and realised that almost all of the predictive power came from this predictor. ...
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24 views

Adding features after classification

I would like to add featuers to an existing model, without training it again. I'm training a model in scikit with partial fitting and would also like to add new upcoming featuers to my existing model....
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147 views

Target encoding a categorical variable in a highly imbalanced dataset for binary classification

I have a categorical variable, Industry, that has different values in a dataset that is over 400K datapoints. This dataset is highly imbalanced, the ratio of roughly 99/1. What I am doing is ...
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3answers
247 views

PCA to choose variables based on its loadings on PC1 [duplicate]

I have a dataset of cave dimensions (and other variables related to their features). The problem is that 3 of these variables are: Length, Area, and Volume. These 3 are highly correlated as they ...