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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16 views

Is there ever any reason to discretise continuous ground truth if doing classification?

Is there a case where discretising continuous response improves classification performance? For example: A response variable is in the range 0 to 99. There are 10 classes defined by the following ...
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2answers
40 views

Categorical as a dependent variable in regression

I am trying to use a regression model which can predict the category of an object.One object has many variables (these are used in the model as independent variables). My question is what kind of ...
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1answer
18 views

What are the benefits for semi-supervised learning over unsupervised clustering? Or any limitations?

I have another question about semi-supervised learning vs unsupervised clustering, what are the benefits and limitations? I have got some data with labels and some without labels. I performed ...
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0answers
11 views

Semi-supervised learning vs supervised learning, what are the benefits and limitations?

Just wondering if any previous work compared semi-supervised learning vs supervised learning? Currently, I have got both datasets with and without labeling. And therefore, it is intuitive for me to ...
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0answers
10 views

Learning over Multinomial data

I have a training data with 68 features... Each of which is a different multinomial distribution. Eg. Feature 1 can take 1 of 4 values while feature 2 can take one of 10 values. Which classifier or ...
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0answers
4 views

Apache Mahout Classification [on hold]

i need a corpus to try mahout classification, i've tried the AG's corpus of news articles downloaded from this site http://www.di.unipi.it/~gulli/AG_corpus_of_news_articles.html but that was not ...
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0answers
7 views

Is it possible to apply SVM on one dimensional dataset in R? [on hold]

Is it possible to apply SVM on one dimensional dataset in R? if yes, please suggest me the procedure.
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1answer
22 views

Isn't leave-1-out insufficient for proper classification evaluation?

I encountered several papers that used some classification method (for instance, LDA), with leave-1-out validation, and posted the classification results as an aggregation of all results (for all ...
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0answers
17 views

Decision Tree in test, a Wrong Problems? [on hold]

I took a test two days ago. one of our question is as follows: decision tree with depth 2 is constructed for two binary feature. hypothesis spase that can be shown with the following tree has ...
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0answers
31 views
+50

Using Kernels with Fisher's Linear Discriminant

I am a bit stuck implementing the Kernel Fisher Discriminant. $$ J(\mathbf{w}) = \frac{\mathbf{w}^{\text{T}}\mathbf{S}_B^{\phi}\mathbf{w}}{\mathbf{w}^{\text{T}}\mathbf{S}_W^{\phi}\mathbf{w}} $$ $$ ...
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0answers
16 views

K-cross validation and Naive Bayes

I am doing an exercise of machine learning, and I have built a Gaussian Naive Bayes classifier (i.e., I have defined values of mean and standard deviation) using scikit-learn. Now I am supposed to ...
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1answer
31 views

Least-squares training error

In classification problems, the training error typically decreases as further training examples are acquired. However, in my current least-squares problem, the training error actually increases as ...
2
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1answer
37 views

Simple way for histograms classification

I'm trying to classify a histogram. I have 4 classes and I generate 4 histograms (h1, h2, h3 and h4) for each class. Each histogram contains 10 bins (attributes describing an object) on the x-axis and ...
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0answers
34 views

Support Vector Machines vs KNN

It was my understanding that in a separable case, SVMs produce the best separation possible and therefore will always produce the same or a better classification rate compared with say, 1NN, ...
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0answers
17 views

Full Information Maximum Likelihood, Imputation and Classification

I need to do a classification of a dataset, I have some missing data and I would like to try some "missing data techniques" to achieve the best accuracy. I already tried multiple imputation and ...
2
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0answers
48 views

Deep learning: representation learning or classification?

For classification, I have often heard about deep learning / deep neural networks as a form of representation learning. I am confused as to what "representation learning" means in this context. Which ...
3
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1answer
24 views

why do decision tree packages convert factor variable into two binary variables

Why are decision tree packages like say, rpart slow with increasing the number of factor levels in R. I read that it basically converts each factor variable into two binary variables representing ...
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0answers
8 views

SVM-Light displays corrupted precision/recall results

I run SVM-Light classifier but the recall/precision row it outputs seem to be corrupted: ...
1
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1answer
16 views

Linear Discriminant Analysis for $p=1$

I'm studying 'Introduction to Statistical Learning' by James, Witten, Hastie, Tibshirani. In page 139, of their book, they began by introducing Bayes' Theorem $p_k(X)=P(Y=k|X=x) = ...
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0answers
28 views

SVM output to probabilistic affiliation

How can I convert the svm output for multiple class classification(one vs one approach) to probabilistic values? Meaning that I want to have a probability for a tested element to be in each available ...
1
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1answer
23 views

How to interpret + and - precisions and recalls?

I understand the general calculation and concept of precision and recall. But when I am trying to predict people's ethnicity using some feature, say for example, predicting a binary class Chinese vs ...
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0answers
17 views

Ideas to classify shipping addresses

I have a dataset of addresses for a bunch of users. I need to classify an address into residential/commercial or office/educational. Moreover, every user has multiple addresses. So every user has a ...
3
votes
1answer
53 views

Effect of categorical interaction terms with random forest machine learning algorithm

Thanks in advance for the help. I have moderately large dataset (around 7000 samples) with numerous categorical predictors and a single binary response. All of the predictors are categorical. ...
0
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0answers
17 views

Estimating class probabilities given discriminative functions per class

What is the effective way to estimating class probabilities per class, if I know discriminative functions for each class (I have trained ML models giving some scores). My naive implementation is to ...
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0answers
23 views

How to draw plot of the values of decision function of multi class svm versus another arbitrary values?

I am trying to draw a plot of the decision function ($f(x)=sign(wx+b)$ which can be obtain by fit$decision.values in R using the svm function of e1071 package) versus another arbitrary values. From ...
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1answer
38 views

Interpret learning curves

I am training a Decision Tree on a dataset of around 580.000 data points. I took the following steps: Split the dataset in training (75%) and validation (25%) set. Determined the best depth for the ...
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0answers
12 views

How use F1 score in an unbalanced binary classification problem?

I have two trained models (MLP and SVM) that want check on unbalanced binary samples (out of sampl - True samples =3000, False samples = 200). I found that i can use ...
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0answers
24 views

Area under ROC curve vs. Accuracy in unbalanced sample

I have a binary classification problem with 3000 samples (number of 1 as outputs = 300, number of 0 as outputs = 1700). After balancing database (selecting 300 samples from 0 outputs) I trained the ...
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1answer
24 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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0answers
11 views

Can Platt Scaling to calibrate probabilities be used for classifiers other than SVM?

I am using Gaussian Mixture Models as classifiers and I compute posterior probabilities from them for a 2 class problem. However, the probabilities are pushed towards 0 and 1 due to very skewed ...
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0answers
6 views

Classifier with interchangeable features

I have a situation in which the features used in a classifier are multiple instances of the same kind of measurement, in random (or unknown) order; thus, a sample x1, x2, ... xn -> classA could with ...
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5answers
310 views

How to calculate Area Under the Curve (AUC), or the c-statistic, by hand

I am interested in calculating area under the curve (AUC), or the c-statistic, by hand for a binary logistic regression model. For example, in the validation dataset, I have the true value for the ...
1
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1answer
14 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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0answers
21 views

Classifier suggestion(27 dimensions, 9 classes)

The restriction of my classification problem is: 27 dimensions, 9 classes, 50.000 entries in the training set, 150.000 in test set. I need a machine learning classifier(open source code) that fits on ...
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0answers
14 views

How to drop variables in statistical classification analysis?

Given a set of data with variables and a training set, we can proceed classification analysis using Mahalanobis distance etc.(discriminant methods) But how do we know whether all these given ...
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0answers
17 views

Best Random Forest model converging to bagging: What does it mean? (R)

I am performing a grid search to tune the Random Forest parameters m and nodesize. I have 79 variables, and the best model, in terms of OOB error, is a model with 76 variables (OOB error = 0.137). So, ...
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0answers
9 views

basic implementation of Non-parametric Bayesian model in python

I am having a problem in understanding infinite Bayesian model with its implementation. I have tried looking scikit-learn package of python, DPGMM. I dont know why there is an argument for defining ...
3
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0answers
20 views

False positives and False negatives of three or more classes

I have a $10\times 10$ confusion matrix $M$ generated after to execute an KNN classification process for digits recognition (0,1,2...9). As usual, each row of $M$ represent the "true/real" class of ...
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2answers
55 views

Choosing an algorithm for classification

What determines what classification algorithm you should use for a certain classification problem? e.g. If there is >5 features or you only have 1000 training examples, or there is multiple class's or ...
2
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1answer
447 views

Why is n-gram used in text language identification instead of words?

In two popular language identification libraries, Compact Language Detector 2 for C++ and language detector for java, both of them used (character based) n-grams to extract text features. Why is a ...
1
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0answers
14 views

GradientBoostClassifier(sklearn) takes very long time to train

I'm using dataset with 61879 datapoints and 102 features. On this dataset Randomforest(sklearn) takes less than 90s to train for 100 estimators while GradientBoostClassifier(sklearn) is taking forever ...
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0answers
4 views

Analogies between Gaussian Discriminant Analysis and Joint Probability Distribution

Can I say GDA is like a full joint probability distribution over all the feature random variables? I mean, if we are given some random variables, we try to inject some conditional independencies, and ...
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0answers
25 views

Random Forests: duplication and stratification when dealing with imbalanced data

From here, here, and here, it seems that one option when applying Random Forests on imbalanced data sets is duplication followed by stratification. I definitely see the benefit of oversampling rare ...
2
votes
1answer
53 views

How does Scikit Learn resolve ties in the KNN classification?

I have a multi-class classification problem, in which I'm using Scikit Learn's k nearest neighbour classifier, (5 classes), which means that an odd number for k won't prevent classification ties. So ...
1
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0answers
23 views

Probabilistic degree of confidence for the kernel SVM with RBF

Let $f\colon\Bbb{R}^n\to\Bbb{R}$ be the decision function of an SVM using the radial basis function (RBF), $$ k(\mathbf{x},\mathbf{x}')=\exp\Big(-\gamma\|\mathbf{x}-\mathbf{x}'\|^2\Big). $$ That is, ...
1
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1answer
20 views

Classification on variable-length time series

I have a series of transactions like the following: [0, 2, 2, 3, 1, 0, 0, 0, 1] [1, 0, 0] [3, 3, 1, 1] I would like to classify each transaction as being part of one of two categories: class A or ...
0
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1answer
26 views

Valid result when adding two kernels with negative coefficient?

If $k_1$ and $k_2$ be a kernel in $ \mathbb{R}^n \times \mathbb{R}^n $. we know $k(x,z)=ak_1(x,z) + bk_2(x,z)$ (kernel addition) is still a valid kernel if $\: a,b \geq 0\,$ ($a,b$ is real numbers, ...
2
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2answers
147 views

Applying PCA to test data for classification purposes

I've recently learned about the wonderful PCA and I've done the example outlined in scikit-learn documentation. I am interested to know how I can apply PCA to new data points for classification ...
2
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1answer
26 views

Statistical Classification Method to Compare PURELY Categorical Data?

I have about a half-dozen variables, each of which can have anywhere from three to ten outcomes. I have to measure the degree of separation/similarity between rows. Either we can do some sort of ...
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0answers
20 views

Sampling from Study Population for Labeling

I have the opportunity to get labels for a portion of my study population, and I can determine the subjects that will receive a label. Every subject in the population can be targeted. The labels are ...