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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9 views

Variational Autoencoder for feature extraction

I would like to ask if would it be possible (rather if it can make any sense) to use a variational autoencoder Auto-Encoding Variational Bayes for feature extraction. I ask because for the encoding ...
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6 views

converting feature from string to categorical reduces classification accuracy

I am working on San Francisco crime classification problem from kaggle. https://www.kaggle.com/c/sf-crime during the work I encountered something unexpected. I applied scikit learn's random forest ...
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13 views

Part-of-speech tags as Document Term Matrix

For my thesis I need to apply a part-of-speech tagging for sentiment classification in R. I have a dataset consisting of ~800 sentences which were tagged by the ...
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0answers
7 views

Feature Matrix vs Feature Vector

In my application I have essentially $n$ areas of interest in an image. The image is circular in nature so the zones are slices. Each zone has 7 features associated to it. The goal is to detect ...
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0answers
6 views

How to generate count-based features from categorical data for binary classification?

I recently discovered this blog post by Microsoft Azure. In it they describe a method of generating new count-based features from categorical features for a binary classification task. I am a bit ...
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14 views

Rescaling vs Standardization of features

Is there any general rule of thumb or any justified rule to choose whether to scale a dataset using Rescaling (for each feature, subtract the min value and divid by the max - min) or Standardization ...
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2answers
34 views

Can trees or random forests learn ratios

This is a question about feature engineering for decision trees/random forests. Given two continuous variables X1 and X2, is it ...
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0answers
23 views

Encode a tree into a machine learning feature

I am creating/working with a dataset in order to answer all kind of questions using machine learning algorithms. One specific issue is that I would like to create a new feature based on a tree ...
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1answer
37 views

Do word vectors obtained via word embedding techniques really form a vector space?

Word embedding refers to feature learning techniques in natural language processing where words are mapped to vectors of real numbers in a low-dimensional space, the embedding space. Similar ...
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28 views

How to transform feature with peak at zero to normal distribution?

I have a feature in my dataset which has lot of zero values, i.e. a big peak at zero (the zeroes are valid and valuable information). The histogram is the following: I want to transform all my ...
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0answers
7 views

Difference between polynomial regression and polynomial kernel

A few answers on SO suggested that a polynomial transformation and a regularized regression can be used instead of a polynomial kernel regression. What's the difference between them? I thought ...
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0answers
16 views

Create features from a document

I have been given an assignment related to NLP and I am a newbie in this field. Train a named entity recognition system that treats the documents as strings of mentions, x . A labelling of the ...
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3answers
374 views

random forest - summarize two features in one without losing information

I am training a random forest on a dataset including both categorical and numerical features. In particular I have a binary feature, call it $x_1$, which has $0$ or $1$ as possible outcomes. I also ...
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0answers
13 views

Extract a sub-distribution with specific characteristics

Imagine that I have a distribution of some data. For example a distribution of natural numbers [1, 10, 45, 89, 12, 9, 4, 100]. I characterize such distribution with 2 features: the mean = 33.75 and ...
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1answer
19 views

How to upweight an arbitray feature in GBM?

Are there any ways to upweight any particular feature for GBM (tree boosting)? The motivation is: Assuming one would like to put a few dummy indicators, e.g., X_A, X_B, X_C, ..., into GBM, for the ...
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0answers
13 views

Input matrix construction for time series

I am having problems constructing the input matrix for a data analysis / machine learning task. The data set consists of ~300 data points, each one in form of a matrix where rows are time steps, ...
3
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1answer
75 views

How to choose/train Matrix Values of Convolution Kernel in Neural Networks

I'm trying to better understand convolutional neural networks better by writing up Python code that doesn't depend on libraries like Convnet or TensorFlow, and I'm getting stuck in the literature on ...
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0answers
16 views

Multiclass classification with large number of classes but for each user the set of target classes is known

I'm new here but I'm familiar with some machine learning theory (took some courses in school) and my question is more about how to apply ML in a practical setting. I have this project where I'm ...
4
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1answer
116 views

Does the first principal component differ from simply computing the mean of all variables?

I was just wondering if the first principal component, while I am trying to find it for a dataset of 18 variables, is different from simply adding all variables and finding the mean? I.e. to compute ...
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0answers
121 views

Feature extraction for time series classification

I have electric signals represented as 2(internal)d data. Each data point is in the form (<timestamp>, <power_value>). I want to be able to ...
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0answers
26 views

Expand or compact features?

I have a classification task for people with 3 categories. I want to apply machine learning for that. I have 10 sources of data, which have the same fields (say 4: age, job title, number of ...
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1answer
56 views

Standard deviation vs. variance as a feature

I'm using the median of response times (of users) as a feature in a machine learning context. As a second feature I want to use the standard deviation or variance of the response times. Of course, the ...
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0answers
29 views

Feature binarization for RF/GBMs?

Are there any advantages to feature binarization for random forests or gradient-boosted machines? For example, suppose I am predicting snowstorms for the next day using various past measurements - ...
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0answers
13 views

Handcrafted a feature or Convolution nets

I am doing an image classification projects. Image in each class represents the same object deformed with an unknown complicated function. In fact, images in the same class become very dissimilar due ...
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1answer
25 views

How many features should I say I have in my model?

I am running machine learning using name features to predict Y (binary 0 and 1 labels). Using the name entity (eg: John Carter), I derive into 4 name substring features (1: first name = "John", 2: ...
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1answer
35 views

How to take the index of the nearest centroid as a feature?

To create additional features for a dataset I have conducted a cluster analysis and assigned a feature to a data set for cluster membership: ...
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0answers
34 views

Combining categorical and continuous features in DNN

I am creating an application that can take as inputs, two numbers (1 or 0) as well as a class defining a binary operation (AND OR XOR etc) and training the network to preform the operation. Without ...
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0answers
34 views

How feature stacking works?

I don't understand how features stacking works. I found out the following sample guideline: Split the train set in 2 parts: train_a and ...
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0answers
13 views

Features work for pairwise binary classification, but fail in three classes

Let $a,b,c$ be three classes. Let $X$ be a matrix of row/column samples and let $F_i$ be several sets of features (i.e. from a sample matrix $X$ we can extract a set of features $F_1$ for each sample ...
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1answer
26 views

feature hashing as a form of encryption

Is feature hashing a really bad way to do encryption? I want to encrypt my documents but in a format that some ML algorithms can be trained on them one day.
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0answers
9 views

Missing data and feature scaling

I am interested in doing some feature scaling to try and tease out something from my data (box plots by outcome show that the 25/50/75 quantiles are very similar; certain variables have more ...
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0answers
15 views

Correct evaluation/ comparison between undercomplete and overcomplete representations

Suppose I'm performing Unsupervised Feature Learning method to learn a representation of the data that is under-complete (e.g. 100 features) and use another algorithm to learn an over-complete ...
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0answers
24 views

Automated Feature Construction / Feature Combination in Matlab or Weka

I would like to extract more information and to catch important relationships in my numeric, sequential time series data. I suspect I have not yet sufficiently discovered nonlinear relationships ...
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0answers
24 views

How to model Not Applicable (N/A) input in a Neural Network?

Assume I have a Neural Network with an input value to distinguish 2 discrete categories, e.g. Bicycle (input -0.9 in case of tanh) or Car (input 0.9 in case of ...
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0answers
20 views

How do I have select features which are influential for prediction?

I have a dataset which has dependent variable(label) as possible destinations and independent variable(features) as age,language, gender and many other categorical variables. How do i find which are ...
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0answers
23 views

How to implement Vectors in Feature Vector?

Please change the question title if you find a better one for my problem. I have taken some measurements with a 6 DOF (accelerometer and gyro) sensor. Now I want to use this data for a classifier ...
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41 views
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43 views

SVM accuracy after log transformation

I am using SVM (RBF kernel, the LibSVM implementation) to deal with a classification problem. When I use a log 10 transformation for may features values instead of using the default scaling method ...
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3answers
135 views

Is it better to do exploratory data analysis on the training dataset only?

I'm doing exploratory data analysis (EDA) on a dataset. Then I will select some features to predict a dependent variable. The question is: Should I do the EDA on my training dataset only? Or ...
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0answers
54 views

Using probabilities as predictor variables for binary classification

I have training data with each feature being different sources of probability. All of the features are probabilities (between 0 and 1 obviously). This is a binary classification problem. Note ...
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0answers
19 views

classification real vs. made-up words

I am interesting in building a classifier that can separates made-up words (such as brands) from real words (belonging to the English dictionary for example). I have tried using a Soundex ...
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0answers
11 views

Can we derive features from the output variable?

I know this sound weird, but can we use the output variable of the training data set to derive the some new features for feeding the model. If yes then how can it be statistically significant?
3
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1answer
117 views

Best approaches for feature engineering?

I have a regression problem. The aim is to estimate the best fitting curve from a set of features. Now I have extracted a set of features that are relevant based on the literatures found. Now the ...
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0answers
12 views

what is the method in dictionary learning which does not have a overcomplete dictionary?

what is the method in dictionary learning which does not have a overcomplete dictionary? and what is the difference in minimization between these two methods (one using overcompelte dictionary and ...
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0answers
22 views

Position-Invariant feature representation

If I have a protein multiple sequence alignment, in which the canonical protein length is 100 amino acids, I can transform this protein sequence into a sequence feature space by "binarizing" each ...
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0answers
49 views

providing MFCC as a feature to WEKA

I am beginner in WEKA, and for my first study, I am doing a music classification task. For that I need to use MFCC of a music file as a feature. When I extract MFCC, I get a csv file as given below. ...
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0answers
91 views

Denoising Autoencoder

I am trying to apply DAE to my task, but since I have experienced some problem with it, to get some feeling of DAE I tried first to try to follow the study performed in ...
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1answer
150 views

How to convert the text columns to libsvm format using feature hasher from scikit learn

This answer links to a code which only works for numeric data , but I have CSV file for machine learning which has mostly text data and most columns have a large cardinality , eg: a column with name ...
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0answers
47 views

Ensemble LDA on different feature spaces?

I'm working on a classification problem where I'd like to do the following: I have a space of features that live in $R^m$, and another set of features that are related that live in $R^n$. I want to ...
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0answers
16 views

CFA when there are no latent variables

I have reasonable results from a OLS regression with five observed variables. This is in a research article submitted for possible publication. One reviewer has asked for CFA (Confirmatory Factor ...