Statistical classification is the problem of identifying the sub-population to which new observations belong, where the identity of the sub-population is unknown, on the basis of a training set of data containing observations whose sub-population is known. Therefore these classifications will show a ...

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8 views

How do I choose the right classifier?

Broadly speaking, is there a good tutorial, possibly online, about which classifier is good for what, depending on number of samples and other statistical properties of the input data?
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1answer
7 views

Convolutional neural network with non-image input data

Can CNNs be used with input data which is not an image? The reason I'm asking is because the original image is often clipped in size because of border effects when doing the convolution. But if the ...
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8 views

How to create a feature vector with scikit-learn? [on hold]

I have extracted some bigrams from a corpus, how can i create a feature vector with those bigrams with scikit-learn?, could anybody provide me some example?. Thanks
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12 views

cross-validation to predict distribution of errors on finite test sets

In one use of k-fold cross-validation for evaluating classifiers, one trains k models, each on n(k-1)/k examples, and tests each on n/k examples. The average accuracy on those k test sets of size n/k ...
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9 views

How to measure when error stabilizes (convergence) on Random Forests (or, when do I stop training)

I'm doing an implementation of Random Forests. As I was the original paper (page 11) and this nice book on the subject (15.3.1, page 592), they mention that when the out-of-bags error stabilizes (when ...
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1answer
33 views

What is considered to be “good” classification rate?

Let's say I am trying to figure out whether two classes can be differentiated. My methods may not be perfect, but I would like to know whether my features "mean" anything that may possibly be added to ...
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1answer
31 views

Range of predicted probabilities by logistic regression

I have a binary classification problem with unbalanced classes, e.g. I have 500 examples of negative class(0) and 20 examples of positive class (1) and I need to estimate the probability of positive ...
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0answers
6 views

klaR package and the stepclass function

(R studio) Hi, I'm running LDA on a dataset with 250,000 observations, 2 classes and 30 variables. My goal is to create a classification model using the LDA function. After loading my variables I ...
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18 views

Good way to categorize count data?

I want to use some count data to train a classifier. The count data range from 0 to 400 something. There are a bunch of smaller counts (0's and 1's). I wonder what would be a good way to categorize it ...
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14 views

Regarding Naive Bayes and conditional independence

We all have been talking about how Naive Bayes may, in some cases, not perform well due to the fact that this assumes conditional independence of features and MOSTLY, this is not true for real world ...
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27 views

Multivariate regression with categorical response variables

Explanation of Data: I started with a data set where each user belong a specific group and their contribution to different domains. After multiple pivots and pre-processing attempts, I got my data in ...
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2answers
24 views

Good metric to distinguish between fat tailed and narrow distribution

Could anyone point me to a good metric to distinguish between the following distributions? One distribution seems to be exponential type whereas the other is fatter and sometimes also has a peak ...
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26 views

What is the relationship between vector space models & support vector machines?

Is there a relation between them? Specifically, if I have a VSM can I classify it through SVM?
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20 views

classify with 3 class [on hold]

How do I calculate average rate error using Bayes and neural network classification, for example, on the three classes in Fisher's iris data?
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0answers
16 views

Graphically, how does the non-linear activation function project the input onto the classification space?

I am finding a very hard time to visualize how the activation function actually manages to classify non-linearly separable training data sets. Why does the activation function (e.g tanh function) ...
2
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2answers
151 views

When can a continuous variable be treated as categorical?

I have a continuous variable, which can take any value between 0 and some large, though not infinite, number. Let us assume that the maximum possible value is 1000. The values are nowhere near ...
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0answers
7 views

Bi-normal separation feature selection (BNS) in R

I'm doing binary classification on highly dimensional text data, with a biased class distribution. After reading this paper, i found out about BNS feature selection. Is there any package that ...
2
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0answers
24 views

R programming, correlation of quantitave variables with one qualitative variable

I have a flat CSV file that has one column of student names, one column of grades (outcomes) coded as a factor A-F, and about 100 columns of test scores (independent variables) of various sorts, on ...
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25 views

Accuracy low if test data belong to a single class

For my classification task I have two classes labeled 0 and 1. I am using Random Forests from sklearn package in python. I have two files for different classes. So I loaded the files, combined them ...
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2answers
37 views

Handle missing values in factor variable

I have a huge dataset for a binary classification problem (about 1.5 million rows), and the feature space is quite large (145 dimension). Some of these features are factors (YES, NO), but there is ...
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0answers
14 views

Make a classification dataset with binary features using scikit-learn

I would like to illustrate a classification algorithm by using this algorithm on a 2-class dataset with binary n-dimensional features. In the past, I have used the scikit function make_classification ...
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0answers
65 views

Any advice on how to improve my accuracy rate in text classification?

I'm trying to do a text classification task. Here are some specs: Context file size = 1M+ documents already labeled Number of top-labels = 17 Number of sub-labels = around 130 Each document is ...
1
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1answer
71 views

Which PCA (or kernel PCA) basis better describes a single test sample?

I have two PCA bases obtained by decomposition of two groups of training data. I also have some samples of test data. How can I decide which PCA basis fits better each test sample? I tried to ...
1
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1answer
46 views

Probability to Likelihood

I have a problem on calculating the likelihood of observing a data point x given the predicted lable. My application is on text classification where I have to detect Spam and No Spam documents. I ...
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0answers
10 views

Binary classification with KNN

I post here because I don't know how to improve the performance of my binary KNN. The problem is that I have 99.8% Specificity and only 82% Sensitivity, but I'd rather have more Sensitivity than ...
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1answer
34 views

Random Forest confusion matrix

I've been creating some random forest models using the caret package in R. I don't have a large amount of data to work with so I'm using 10 x 10-fold CV in lieu of an independent test set. When I ...
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0answers
16 views

When would Probabilistic Graphical Model be more useful compared to other commonly used models?

When would PGMs be better compared to other classification algos like DT, or LR? I see that it will be better if there are relationships / dependencies between the features. Are there any other ...
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14 views

soft training and classification (class membership)

there are several soft classifiers in r, such as linear discriminant analysis. Functions such as lda {MASS} show the likelihood of each case being classified to belong to each of the classes defined ...
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25 views

Prediction for new data using trained Gaussian Mixture Model

I am not sure how to do the prediction for some new data using trained Gaussian Mixture Model (GMM). For example, I have got some labelled data drawn from 3 different classes (clusters). For each ...
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14 views

binary classifiers and linear classifiers

I'm a newcomer in the field of machine learning,and my interest is keyphrse extraction using machine learning methods. 1.i need to know differences and similarities between binary classifiers and ...
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3answers
44 views

Is kNN best for classification?

I wanted to know if kNN might produce the best result for classification? Since, it is not model based, it does not loose any detail and compares every training sample to give the prediction. Hence ...
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2answers
54 views

Classifying by performing PCA for positive and negative datasets separately

I have a dataset with binary labels, and I try to figure out whether the data can be classified and yield the ground-truth labels. I thought to try PCA for the data with each of the labels, and see ...
2
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2answers
65 views

Why does the scaling of feature vectors improve performance of SVM classifier?

I've found that performing scaling in SVM problems really improves the performance of SVM ... But I don't understand why! I have read this explanation: "The main advantage of scaling is to avoid ...
1
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0answers
31 views

How can I make sure that an LDA implementation works?

I am currently experimenting with neural nets for classification of on-line handwritten data (hence: not pixels, but time series data). To do so, I use several toolkits (internal development of my ...
2
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1answer
54 views

Can someone explain to me the Bayesian classification model?

I often read about converting from a normal classification model like logistic regression and then using an equivalent Bayesian model. As I understood, it's somehow the same model but with a different ...
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2answers
21 views

Supervised classification using tree methods

What work has been done for supervised classification using tree methods that utilize linear combinations of variables instead of single variables?
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13 views

Importance of class frequency in classification

Suppose we are classifying instances into n classes that, in practice, occur at frequencies p1, p2, ... pn, (for example classifying news-articles as one of n different topics). For the purposes of ...
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13 views

How we can statistically compare performance of two models before and after outlier detection?

As you know we can use Mcnemar's test to compare performance of two models in binary classification problem. But in my case i ...
1
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1answer
131 views

scikit multi label classification

I am trying to classify data into four different labels. The training data looks something like: ...
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0answers
20 views

Two tails or one tail McNemar's test in a binary classification problem. Which one should i choose?

I'm using McNemar's test for compare two designed models in a binary classification problem. As you know we have two kinds of this test. ...
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21 views

Stochastic Gradient Descent Validation

I am trying to implement Stochastic Gradient Descent algorithm using Gaussian basis functions. The equation I am trying to implement is as follows: $$ w \gets w + \alpha (y_t - w^T\Phi(x_t)) ...
0
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1answer
47 views

CHAID decision tree - Binning continuous variables

I am running a CHAID classification tree on SPSS to classify my data set. I have a couple independent variables including categorical and continuous ones. For continuous variables, I've noticed that ...
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0answers
9 views

How to classify test objects?

I'm coding a program that tests several classifiers over a database weather.arff, I found rules below, I want classify test objects. I do not understand how the classification, it is described: "In ...
0
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1answer
52 views

How to use k-fold cross validation in naive bayes classifier?

I'm trying to classify text using naive bayes classifier, and also want to use k-fold cross validation to validate the result of classification. But I'm still confused how to use the k-fold cross ...
0
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0answers
8 views

How to define my custom cost function to be used in (stochastic) gradient descent?

I have a text classification problem were the classes are 20 cities and the input is text Bag of word features. I am using Logistic Regression and my cost function is negative log likelihood: ...
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0answers
12 views

Experiment design: classifying 3 classes (2 easy, 1 hard)

Bit of background: I have a problem of classification of 3 classes. Given the training set (80%) and a held-out set (20%), I found out that 2 classes are easy to discriminate/classify. The third class ...
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0answers
27 views

Neural Networks sigmoid activation with bias updates

I am trying to figure out if I am creating an artificial neural network using the sigmoid activation function and using bias correctly. I want one bias node to input to all hidden nodes with static ...
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0answers
6 views

Detecting false positives of a classification algorithm

I run a web service with a lot of users. Some of these users are involved in undesirable behavior (e.g. trolling). I've come up with a classification algorithm to detect these users (and deactivate ...
0
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0answers
20 views

Regression/classification, how to accommodate the missing columns of data?

I would like to apply any regression methods, such as the ones available using WEKA libraries (for example, SVMs, NNs, Random Trees,...) . However, I am getting very low results since I am missing the ...
0
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0answers
31 views

What is the next step after acquiring the parameters(means, covar, priors) from GMM via EM

I am comparing the results achieved from clustering via K-means and GMM. For comparison I have accumulated a dataset of images. The training set consists of 359 images. I used SIFT to extract the ...