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

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990 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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30 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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0answers
114 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
225 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
47 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 ...
2
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1answer
326 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
44 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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1answer
30 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
185 views

Using principal component analysis (PCA) for feature selection in regression [duplicate]

I have a dataset $D$ made of $m$ samples and $n$ features with $n \gg m$. For each sample I have a score $s$ which I would like to be able to predict. As the number of features is very high (compared ...
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2answers
637 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. ...
2
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2answers
185 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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21 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 ...
2
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1answer
105 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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0answers
84 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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0answers
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 $Y$ ...
5
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1answer
217 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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0answers
51 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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0answers
26 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
83 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 ...
2
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1answer
113 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. ...
2
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2answers
136 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, ...
2
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1answer
104 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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0answers
52 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
94 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 ...
2
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1answer
108 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 ...
2
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1answer
53 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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16 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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0answers
27 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
150 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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4answers
3k 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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0answers
64 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
709 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
176 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
171 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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79 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 ...
0
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1answer
91 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 ...
0
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1answer
38 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
86 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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3answers
534 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
59 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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0answers
186 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
240 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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44 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 ...
8
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4answers
194 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 ...
13
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2answers
706 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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0answers
188 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)?
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3answers
112 views

What if Lasso selects transformed terms but not untransformed terms

Suppose I have standard normal features $X_i \in \{X_i : i \in \{1,...,1000\}\}$. I extend this set of predictors with transformations as follows: $\{X_i,X_i^2,X_iI(X_i > 0) : i \in ...
0
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1answer
158 views

Best subset selection

My statistical learning text claims that for best subset selection, 2^p total models must be fit through regression if for p covariates, we fit p choose k models at each k, k = 1,...,p. I interpret ...
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0answers
31 views

Recursive Feature Elimination Fails to Output as Expected

Currently I am using rfe function in the "caret" package to do feature selection. There are 380 variables as input candidates. I have done many trials and I noticed that something weird always ...
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140 views

Find weight of features for feature selection

I have a data set of videos from which I need to recognize the emotion of the speaker. For that reason I have some markers on the face of the speaker. I detect their movement as the speaker speaks and ...