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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Random forest feature adjustment

so, I've found the misclassified instances in my Random forest model have lower values in some predictors, how can I adjust the model so that the threshold is more sensitive to these predictors? ...
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1answer
34 views

Feature that is numeric if true, or single value if false

In regards to feature engineering for machine learning models. I would like to engineer a feature that encodes the following: value can be true and if so it will measure a numeric (maybe ...
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27 views

How to determine whether 2 code snippets are functionally same? [migrated]

Given 2 code snippets I want to check whether they are functionally similar or not. By functional similarity I mean that they should yield same output when provided with same input. I am extracting ...
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15 views

What is the general procedure or general rules for grouping factor levels?

I am attempting to build a predictive (machine-learning) logistic regression model that contains mostly categorical (non-ordinal) variables. As part of a variable selection process I run a Pearson ...
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29 views

Neural Net in high dimensions for images

I'm trying to build a neural net for a image recognition problem. My images are way too large to build a straight up NN from just the pixels; they are about (1000, 1000) width,height. So naturally i'm ...
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8 views

How to analyse periodic data obtained from a vibration experiment?

I have data from a vibration experiment on a structure. This data was obtained by attaching an accelerometer to a FFT analyser, which records the data at a high sampling rate (typically in kHz range) ...
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10 views

Feature Normalization & Learning

I'm working on a cell classifier (as in Biological Cells) using images obtained by microscope. Right now I have about 12 Features written (color,width-height ratio, shape, couple of texture features, ...
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2answers
31 views

Why we need to extract a lot of features from a dataset for classification

I am newbie in machine learning. I have been studying about features extraction and some classification approaches, in the term of my study, I have a question in my mind, what the reasons we need to ...
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15 views

Storing a kernel matrix

I'm reading a paper on feature hashing and the authors state in the introduction that "limited memory makes storing a kernel matrix infeasible." I'm confused as to why the kernel matrix needs to be ...
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1answer
29 views

Representing signals as feature vectors for deviation detection

I want to monitor (automatic-)gearbox failures on some vehicles. For each vehicle I have a captured signal representing the selected gear at each one millisecond (the values are discrete between 0 and ...
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1answer
45 views

Weighting features prior to SVM

I'm building an object detector using HOG features and linear SVM. Some of the regions of the object are more "distinctive" so I would like to give more weight to the features extracted from those ...
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30 views

How can I extract features from fMRI network connectivity analysis (FSL nets)?

I have a set of 37 fMRI images from mice which are divided into 4 classes (different drug doses applied). My task is to train classifiers (SVM etc.) on this dataset. Of course feature extraction is a ...
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1answer
35 views

What to do with important features?

I am currently solving the titanic problem in kaggle. The data of the problem consists of several features such as "sex", "class in society", etc., and you are to predict whether a person survived the ...
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15 views

Binary Classification of Labels with Similar Feature Distributions

I wish to classify gene interactions as 'Validated' and 'Unvalidated' based on certain features of each interaction. Each interaction has 10 different features. However, the feature distributions of ...
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44 views

Does feature size affect polynomial regression?

(I'm still trying to learn all this, sorry for any wrong terms or mistakes I might have made in this question) By feature size, I mean the value of the numbers. For example, let's say I have input ...
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2answers
42 views

How well should features discriminate to build a good classifier from them?

For my (binary) classification problem I'm developing several features and tune them with ROC curves. At some point, I want to combine them with in classifier. How well should the features perform, ...
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51 views

kMeans unsupervised feature learning on multiple layers

I'm trying to develop an unsupervised feature learning pipeline. I have a train set with 512x512 images. I've extracted 16x16 patches, performed preprocessing steps (normalization and whitening). ...
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2answers
27 views

Should you create a word vector before cross validation?

We are doing a lot of experiments in my research group with text data, and what usually happens is that a corpus will be transformed into instances with features as bag of word or n-gram features. We ...
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24 views

What kind of feature selection do I need for text mining?

I have a data set of questions belonging to 10 different categories namely (definitions, factoids, abbreviations, fill in the blanks, verbs, numerals, dates, puzzle, etymology and category relation). ...
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1answer
23 views

How to compare utilization rates?

I have the utilization rates of several machines for each week over a year. These differ per week because of the occurrence of machine failures and changes of orders. Meaning that one week a machine ...
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76 views

Generating features: What level of interaction?

I have multi (3) level data indexed by i,j,t. As such, I can generate fixed effects (dummies) for either ij, it, or jt, (and still achieve identification). I can also do i,j,t separately as well. ...
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10 views

Enhancing one feature set with another feature set

My problem is I have two feature sets lets say those feature sets as A & B where A does have features A1, A2, A3.... AN and B does have features B1, B2, B3.... BN. 'A' features are computed always ...
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10 views

“Featurization” of motion data

Assuming that both gps and speed information is available (timeseries), as anyone worked with or can provide some guidelines on the creation of relevant features for motion classification? Besides ...
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54 views

Feature extraction based on correlations

I have a small problem regarding feature extraction with correlation. I have divided my question in four parts hoping that somebody can help me. I have a dataset consisting of fMRI images. Each image ...
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40 views

Feature selection of non stationary data

I am working with EEG signals which are non-stationary. I have used spectrogram to analyse the data in specific frequecies. I have to select some features from the specific-frequency time signals ...
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52 views

In feature engineering, is using x*y as an interaction term different from using x/y?

I want to engineer some new features for a classifier, say, logistic regression. Let's say I have two continuous (numeric) features $x$ and $y$. I know that you can create a new feature from $x$ and ...
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1answer
386 views

Automatic keyword extraction: using cosine similarities as features

I've got a document-term matrix $M$, and now I would like to extract keywords for each documents with a supervised learning method (SVM, Naive Bayes, ...). In this model, I already use Tf-idf, Pos ...
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1answer
34 views

Fit multidimensional feature into design matrix

I'm having trouble understanding how I can have a multidimensional feature in my design matrix. I understand the concepts of PCA, but I'd rather avoid it. I have the feeling that I'm missing out on ...
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Evaluation of features, how to find which feature is the most effective?

My question is following, which approach should i use in order to make a evaluation of features. To be more specific, for example we have a tweet message: "The weather is nice outside, it makes me ...
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1answer
64 views

Feature engineering

I recently realized, that feature engineering (designing input vectors for machine-learning algorithm) is one of the most complicated tasks when applying known algorithms (for example kernel ...
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0answers
45 views

Principled way of collapsing categorical variables with many categories

What techniques could I use to optimize the collapsing of many categories to a few, for the purpose of using them as an input to a statistical model? Consider a variable like college student major. ...
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1answer
57 views

Classifier with variable number of features

I am trying to make a classifier when each sample has a variable number of features. An example of how this could occur is, for example, if the features are the purchases (type, dollar amount, etc) ...
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2answers
114 views

How To Better Represent A Problem To A Machine Learning Algorithm

I am familiar with the basics of how to present a problem to a machine learning algorithm using binary encodings. I am also familiar with, but still learning about, feature selection/extraction and ...
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1answer
27 views

What kind of general strategy can you apply after selecting model and hyper parameter training?

As a rookie to machine learning area, I tried to play some Data Science tutorials and beginner competitions to gain some knowledge and experience. The problem I encountered in every scenarios is ...
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1answer
44 views

How much prediction accuracy of SVM (or other ML models) depend on the way features are encoded?

Suppose that for a given ML problem, we have a feature which car the person possesses. We can encode this information in one of the following ways: Assign an id to each of the car. Make a column ...
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28 views

Learning if instances from a dataset are part of the same subset

I was wondering if there are some well-known machine learning methodologies for subset learning. In other words, to learn if two instances are part of the same subset or not (boolean label?). One ...
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1answer
48 views

Useful Representation of Continuous and Nominal variables

I want to develop a prediction model (e.g. using SVM, Neural Networks...etc) to predict the relationship between a protein and its DNA target. Each proteins is represented using ~100 continuous ...
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4answers
267 views

How to prepare/construct features for anomaly detection (network security data)

My goal is to analyse network logs (e.g., Apache, syslog, Active Directory security audit and so on) using clustering / anomaly detection for intrusion detection purposes. From the logs I have a lot ...
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1answer
63 views

How to do machine learning (regression/classification) when the samples are of different sizes?

In standard cookbook machine learning, we operate on a rectangular matrix; that is, all of our data points have the same number of features. How do we cope with situations in which all of our data ...
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1answer
52 views

Automatic feature building/extraction

I have a large time stamped data set (several millions of rows), with known measured inputs xi, where i is a large number to the order of magnitude of 20. The goal is to predict a response yi given ...
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18 views

Need to order a set of colors to build a feature vector

I am working on Computer Vision task of object classification with python and OpenCV. Currently I am extracting some characteristic colors of an image using K-means clustering on all the pixel to ...
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11 views

Is there an algorithm to determine if too few features are selected for k-nearest-neighbor?

Is there an algorithm to determine if too few features are selected for k-nearest-neighbor when no test set is available, --when the input vectors are unknowns? Here's my problem, I have a massive ...
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2answers
257 views

Machine learning feature encoding

I'm new to Machine Learning. I've just finished the Coursera course. :) And for my first practical attempt I wanted to "analyse" a local used cars selling website in order to compose a modal that ...
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1answer
32 views

Using a set as a feature in decision tree classification

I'm faced with a data set where one of the features is a set of 4-5 categories (this number of categories isn't constant). I need to use this feature for building a decision tree. I searched online ...
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24 views

Statistical technique for data when three interventions are administrated and multiple responses measuring different constructs are measured

I have a data where three interventions were administrated on subjects and different response variables (say 15 variables) on Likert Scale were observed. These response variables measure three ...
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2answers
1k views

Autoencoders can't learn meaningful features

I have 50,000 images such as these two: They depict graphs of data. I wanted to extract features from these images so I used autoencoder code provided by Theano (deeplearning.net). The problem ...
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1answer
55 views

pairwise distances used as features for classification

I have a feature matrix 977x3 features = rand(977,3); where each row is an observation and each column is a feature. I calculate the pairwise distances between ...
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70 views

DecisionTreeClassifier scikit-learn : knowing the leaf to which an example belongs to

I am currently reading this paper http://quinonero.net/Publications/predicting-clicks-facebook.pdf , where they are using trees to generate feature that are afterwards fed to a linear classifier. My ...
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1answer
110 views

Optimal construction of day feature in neural networks

Working on regression problem I started to think about representation of "day of a week" feature. I wonder which approach would perform better: one feature; value 1/7 for Monday; 2/7 for Tuesday... ...
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58 views

What's the potential reason that by combining two feature sets the performance of random forest dropped?

I am building random forests on high dimensional, sparse, and class unbalanced training datasets (around 500 - 5000 examples) using two different feature sets. I did stratified 10-fold cross ...