Methods and principles of selecting a subset of attributes for use in further modelling

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How to interpret the selection of a variable in a model (GLM) when spatialautocorrelation is included?

This is a pretty straightforward question. I am comparing outputs of two models (binomial GLM) one including environnmental-only (ENV) variables and one including environmental and spatial variables ...
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1answer
150 views

Understanding the output of C5.0 classification model using the CARET package

The C5.0 classification model was used in this 4-class problem data with $N_{train}$=165, $P$=11, using caret R-package by ...
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25 views

Estimating confidence of a prediction

Given a set of features vectors $X=\{\vec{x}_1,..,\vec{x}_n\}$, binary ground truth data $Y=\{y_1,..,y_n\}$ and continuous prediction $\bar{Y} = \{\bar{y}_1,..,\bar{y}_n\}\in [0,1]$, I want to perform ...
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31 views

Feature selection methods comparison

I recently run a project that involves a feature selection step before further pattern recognition. The number of features for our data set is very large and instead of running greedy ...
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2answers
65 views

The curse of dimensionality? (linear SVMs)

How do you know whether you suffer from it? Let's suppose I have a 2 class problem - 2000 training examples and 30 features. While it works good for the most part, sometimes I get edge cases that ...
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1answer
35 views

Algorithms/methods to create more features of a limited amount of features?

So, let's suppose that I have a set of 20 features - some of them are continous and some of them are binary. Is there an algorithm/method/solution to create more features ( combine/transform ) those ...
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1answer
118 views

How to interpret this cross-validated sparse LDA figure using CARET package?

Training data with $p$ =11 predictors and $n$ =165 with 4-class problem was cross-validated (5 times repeated 10-fold CV) using the sparse LDA (aka SDA) using caret ...
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1answer
35 views

SVM - combining binary and continous representation of the same feature?

How would this influence the accuracy of the SVM model? Let's suppose that I have one variable which max value is 100 and minimum is 0. Currently, I send it to SVM as a single continuous feature, ...
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15 views

Significance Test for Comparison of Variables

I am not sure how to ask this question without giving an example. I am trying to measure the "cleanliness" of office buildings. I have two variables that try to measure this. Variable one is a ...
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1answer
75 views

Using Principal Components Analysis for feature selection

I have a dataset D made of m samples, and n features with n >> m. For each sample I have a score s which I would like to ...
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2answers
83 views

Mixing continuous and binary data with linear SVM?

So I've been playing around with SVMs and I wonder if this is a good thing to do: I have a set of continuous features (0 to 1) and a set of categorical features that I converted to dummy variables. ...
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2answers
75 views

Selecting a feature modeling approach for text classification

I am new to text processing. Currently I am trying to determine which type of feature vector I need for a classification problem. I am mainly deciding between binary feature modeling and ...
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17 views

Neural net: variable distribution, validation and number of variables - unexpected results

I have been experimenting with ANNs on one of my datasets, they seem to have the potential to be quite effective in explaining my Y variation. Something i am finding is that they very much benefit ...
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1answer
100 views

Are there any techniques that quantify the importance/signification of individual attribute values of a particular data point?

Are there any techniques that quantize the importance of individual attribute values in a particular data point, in terms of the attribute's overall importance/signification/contribution to the ...
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40 views

equivalent of PCA explained variance ratio for SVD ?

i am wondering if there is an equivalent of PCA explained variance ratio for SVD. What are the measures I can get to monitor the number of columns I keep after the SVD ? Are any of these metrics ...
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27 views

Investigating 'minor' effect variables?

Is there any way to investigate minor contributing $X$ variables in a model when there are one or two $X$ variables which contribute to the explanation of a majority of the variation in the $Y$ ...
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1answer
79 views

Explain steps of LLE (local linear embedding) algorithm?

I understand the basic principle behind the algorithm for LLE consists of three steps. Finding the neighborhood of each data point by some metric such as k-nn. Find weights for each neighbor which ...
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18 views

Feature Selection: markov blanket filter

I need to do a markov blanket filter for feature selection for highly unbalanced datasets. There are popular algorithms to do this? I need to understand the algorithm behind this. From what I ...
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24 views

Choice of 0 or -1 for failure in the independent variables of a logistic regression

I am performing some exploratory analysis on a dataset where the dependent variable is a dichotomous variable. I have ~10 explanatory variables, some of which are dichotomous observations. I am ...
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2answers
53 views

Choosing the best featureset for prediction

I have this N sets of features F each with $F_i$ number of features. All the feature sets have 20000 examples and we have 20,000 labels for them. Lets say feature set $F_1$ has 10 features and ...
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1answer
66 views

How to calculate number of features based on image resolution?

Just covered Andrew Ng's Non-linear Hypothesis of Neural Netowrks, and we had a multiple choice question for determining number of features for an image of resolution 100x100 of grescale intensities. ...
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2answers
84 views

improve precision in text classification

I am working on binary text classification using sklearn: The length of each sample is not high (~ 200-500 characters) I use TF-IDF to get important words as TfidfVectorizer(sublinear_tf=False, ...
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1answer
58 views

Purposeful selection and confounding

I conducted purposeful selection as outlined in Jewell's Statistics for Epidemiology. The log likelihood tests showed covariates, which I considered to be confounding though not significant in the ...
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45 views

Inferring dimension weight in a mapping from a triangle to a distribution over its vertices

I have a dataset $(y_i, \mathbf{X}_i)$, where $\mathbf{X}_i$ is a $3 \times n$ matrix of reals and $y_i$ takes a value in $\{1, 2, 3\}$. Essentially, $y_i$ represents a "selection" of the row vector ...
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3answers
86 views

Classification performance and the feature set selection

I am now working on a classification problem. The generated feature set can be separated into two group. I did a comparison study: use all of the features; use the features of group 1 only; and use ...
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1answer
86 views

penalized regression applications in epidemiology

I am seeking advice on penalized regression models for selecting covariates in epidemiological studies. A difficult tasks I face is feature selection while still attempting to account for confounding ...
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1answer
50 views

Should I exclude predictor variables if used to create a new one?

I have a dataset that includes race, gender, income, and family size. In addition, a variable for "sliding fee scale" tier is included, which is determined by income and family size. Should income and ...
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7 views

How to find entropy of vocabulary terms in multilabel document classification problem?

I have 5 million of document s with varying number of labels for each. I intent to find entropy value for selecting discriminative terms to degrade the size of vocab. However, having that multiple ...
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19 views

ARIMAX feature selection

I am implementing the ARIMAX model and need to implement feature selection. I have >100 features and a lot of data, so I need a method that isn't too computationally expensive. I tried a wrapper, ...
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1answer
120 views

Steps followed when Binary logistic regression when both dependent and independent variables are binary

I had set of binary variables. To apply logistic regression, I have checked association between dependent and independent variables and considered only those independent variables in the model which ...
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1answer
400 views

Term frequency/inverse document frequency (TF/IDF): weighting

I've got a dataset which represents 1000 documents and all the words that appear in it. So the rows represent the documents and the columns represent the words. So for example, the value in cell ...
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52 views

Random Forests and Feature selection [duplicate]

First, I split my training set into 10 parts. 9 parts of it, I use as LS and the other one as TS. I now want to do feature selection, so I do feature selection on 9 parts. I use Random Forest to do ...
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4answers
418 views

When wouldn't I use LASSO for model selection?

Assume that you need to build a linear model to make predictions for new observations, and that there is uncertainty about which subset of variables should be included in the model. You are only ...
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2answers
107 views

CV on training set with feature selection

I've got a problem with CV on feature selection. I've used a method, but I don't know it's correct... I split my data into 70% training set and 30% test set I work now with my training set. I do on ...
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1answer
123 views

Feature selection and cross validation

I'm working on a project and I would like to know if the following strategy is good/correct. Sorry if this is a basic/stupid idea (I'm new to this). The input is a dataset with 2.500 features and ...
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17 views

Comparing 2 different sets of features

So let's say I have two different sets of features A and B. I'm trying to determine which set of features is the best. I'm using leave-one-out cross validation as the final metric as my data set is ...
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57 views

How to explain difference of importance between feature selection and model quality?

I have a data collection with a mixed feature set consisting of both numerical features and text features. The number of numerical features is quite small, i.e., 6, comparing to the number of text ...
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1answer
57 views

Random forest importance differs between rf$importance and importance()

My model is working ok (the AUC is 0.7) but the importances from a randomForest run for my binary classification problem differ depending on how I retrieve them. Is ...
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34 views

Predictive features with high presence in one class

I am doing a logistic regression to predict the outcome of a binary variable, say whether a journal paper gets accepted or not. The independent variable or predictors are all the phrases used in these ...
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1answer
54 views

Numeric example of data for special case of stepwise linear regression

Stepwise Regression works as follows if I'm correct: fit the initial model add the variable which has its f-stat larger than a in-threshold and repeat step 2. if there are no candidates to enter - ...
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13 views

Why the maxStages argument in biglars.fit does not work

Why doesn't the biglars.fit function work when maxStages is specified? I've tried multiple values and multiple ways of casting $y$ but it doesn't work. ...
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3answers
211 views

How to divide feature set for selection and training

I have training data with 260 observations that have a total of 7 classes. Each observation has 120 features. I applied feature selection based on the Bhattacharyya Algorithm and got the top 40 ...
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1answer
44 views

finding an optimal subgroup of binary indicators

My dependent variable is continuous variable that measures the (potential) success of a person in some activity. I have hundreds of binary indicators, each indicates about the existence of a specific ...
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106 views

Cross validation for variable selection and coefficient shrinkage?

Is cross validation an appropriate technique for variable selection and regression coefficient shrinkage? A former colleague of mine used 10-fold CV to compare the regression coefficients from the ...
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0answers
89 views

Metric warning using caret's rfe

I am using the caret package to do feature selection with rfe while training a knn ...
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37 views

Variable selection (automated)

I was wondering whether the following mechanical selection procedure will result in a possible bias. First let me introduce the first procedure, we start with a model and only look at the t-value and ...
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0answers
24 views

Lasso-ing the order of a lag?

Suppose I have longitudinal data of the form $\mathbf Y = (Y_1, \ldots, Y_J) \sim \mathcal N(\mu, \Sigma)$ (I have multiple observations, this is just the form of a single one). I'm interested in ...
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10 views

Sparse variable selection algorithms that account for parameter changes

I am variable selecting for a time-series forecasting model that has parameters sampled from a high variance sampling distribution centred near zero and that undergo changes over time. Each predictor ...
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2answers
392 views

Why does the Lasso provide Variable Selection?

I've been reading Elements of Statistical Learning, and I would like to know why the Lasso provides variable selection and ridge regression doesn't. Both methods minimize the residual sum of squares ...
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116 views

When would I choose Lasso over Elastic Net

What are the scenarios where Lasso is likely to perform better than Elastic Net (out of sample prediction)?