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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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Is it valid to have all zeroes in a One-Hot Encoded categorical feature?

I'm building an MLP classification model and one of my features is the name of certain products. These names can be anything and in theory there could be an infinite number of different names in the ...
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2answers
78 views

What is the difference between overfitting and “not learning”

I am trying to build a Random Forests (RF) model using around 2000 observations and a number of features (can be 50 or can 1000, I still do not know which features are to be used). One way to ...
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0answers
20 views

How to properly add spatial features for a precipitation time series forecasting?

I am reading this paper. The center of the circle is the site where the model should forecast precipitation. Red stars in the picture are nearby sites and each site has these features: I want to ...
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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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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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0answers
8 views

Finding Semantically Similar Learned Features

I have learned features from text and image and are projected in a hyperspace. Once I have the feature space, I am looking to find those features which are similar to each other. I have tried ...
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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 ...
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1answer
75 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 ...
0
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1answer
35 views

(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 ...
2
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1answer
23 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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0answers
32 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
51 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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23 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
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
16 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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0answers
12 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
44 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
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
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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0answers
10 views

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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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
32 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
30 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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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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1answer
38 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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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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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 ...
0
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1answer
110 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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0answers
27 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
25 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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0answers
12 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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0answers
15 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
50 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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0answers
15 views

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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0answers
262 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. ...
2
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1answer
55 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
47 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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0answers
12 views

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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0answers
30 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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0answers
22 views

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
202 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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0answers
38 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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0answers
26 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
63 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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0answers
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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0answers
21 views

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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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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0answers
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Aggregation of sequence data

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