Questions tagged [classification]

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 variable behavior which can be studied by statistics.

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How to include negative examples in multi-class classification?

I have a problem similar to this question: How do I use negative examples (in addition to positive ones) for training a multiclass softmax classifier (or a neural net with softmax output)? where I ...
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1k views

Improving multi-label loss function

Trying to train a CNN on a multilabel problem, each image can have 0, 1, 2 or 3 labels assigned to it. The number of labels is not known a priori. I figured the standard loss function for such problem ...
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classification: concatenating descriptors vs. using multiple classifiers

Consider a typical machine learning problem where you try to do object classification from a high-dimensional set of features. Suppose we know that the features are actually a collection of distinct "...
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307 views

Google gender-pay gap vs

Background: I read this: google schools US government about gender pay gap. It derives from this google blog post by Eileen Naughton, VP of People Operations. She asserts that google is somehow "...
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Choice of classification loss function with unequal payoffs

Suppose that I'm building a binary classifier parameterized by $\theta \in \mathbb{R}^k$ that maps some observed features $x_i \in \mathbb{R}^l$ to a decision of whether or not to play a game with an ...
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336 views

Unsupervised Anomaly Detection Threshold Selection

If we have a data set that contains only positive examples I am wondering how we can effectively choose a threshold for an anomaly detection technique. Are there anomaly detection techniques that can ...
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322 views

Stop Word removal with RNNs

Is it required or encouraged to remove stop words from documents when using Recurrent Neural Networks for Text Classification? To my understanding RNNs are able to "understand" words in a context and ...
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Can I use HMM to predict the spread of Ebola?

1) Can Hidden Markov Model be used across both a large number of categories (districts) and cases (weeks)? 2) Is HMM appropriate for trying to model such a problem? 3) Would I need to develop a ...
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How do I make use of “soft” labels in binary classification?

Let's say we have a binary classification task, but our dataset contains more fine grained values of how much an examples belongs to the class or not. So the labels are real numbers in $\left[0,1\...
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Training instances importance in Random Forest?

Is it possible to determine the importance of the training examples in Random Forests, analogously to what's done with predictors? Basically the idea would be to find important samples in the data, ...
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How to retrain a production classifier that blocks its own positive examples?

I'm looking for help understanding how to re-train a fraud detection classifier that's been deployed to production (where it successfully blocked much, but not all fraud coming into the system). I ...
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3answers
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Combining one class classifiers to do multi-class classification

I am working on a 3-class classification problem. The classifier I'm using is Bayesian Networks which provides me with a classification accuracy of around 60%. When I do a two-class classification, I ...
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653 views

Understanding calibrating probabilities using R

I am trying to understand R's calibration(package:caret) function. My main interest is binary classification. Calibration function is used for plotting true ...
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Multiclass vs. One-vs-All vs. One-vs-One classification

I am working on a classification problem with 7 classes. Is there any rationale to suspect that the best model might be found with a multiclass classifier, multiple one-vs-all classifiers, or even a ...
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701 views

Difference between training and test data distribution

The basic assumption in machine learning is training and test data follows same distribution. But in reality this is highly unlikely. Covariate shift address this issue in which training and test ...
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1k 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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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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125 views

How to use information about likelihood of classes in a classifier?

General question: How can information about the likelihood of classes be used to improve a classifier? Suppose that the probability of each class is known quite precisely (from a very large sample), ...
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210 views

Student classification with Multinomial Logit

I’m analyzing student performance data. In my dataset each row corresponds to a student and each column contains several performance metrics (continuous) and the student type (categorical, 4 types). ...
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Who will follow who based on tags?

Suppose users in a system like a social network are described by a number of tags. The number of tags can be assumed to be less than 10. Example John: funny musician geek professor Peter: skinny ...
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67 views

Priors for discriminative methods?

Say we want to build a classifier for a binary classification problem using a discriminative method (e.g. SVM) and be able to impose a prior on the classes. For example, let's assume that we want to ...
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2answers
736 views

What methods can be used to transform data?

I am solving a binary classification problem with 4 predictor variables. The variables didn't seem to be linearly separable. I have used Neural Networks and Kernel SVM which work and give desired ...
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259 views

Time series prediction where each datapoint has a sequence

I am Computer Science major, and new to stats, so please bear with me and point me to the right direction if what I'm asking is pretty obvious. I have a dataset, where each data point consists of <...
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230 views

Hierarchical classification

I'm currently working on the classification with massive amount of data. Similar to the kaggle one. Data input consist of features and multiple labels that can be hierarchically aligned. At first I ...
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758 views

Easy to follow tutorial on using Markov Random Fields for classifying pixels in gray-scale images

I am trying to learn how to use Markov Random Fields for classifying pixels in an image. Could someone please direct me to a simple tutorial demonstrating how this is done. The tutorial needs to ...
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357 views

Linear Discriminant Analysis: Using subject as classification

I have a problem where I need to identify from which subject a particular set of data points came. More specifically, my problem is that I need to demonstrate that my subjects (N=9) can be ...
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Minimum training sample size required for a classifier

What is the best method to determine the minimum number of training samples required for a classifier? I am only comparing one classifier (four class problem), discriminant function analysis (DFA) ...
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SVM classifier (with soft-margin) implementation in R, gamma value and quadprog

I'm trying to implement a Support Vector Machine classifier in R and I have to solve the optimization problem using the quadprog R package which solves problems of the form : $$min_b \frac{1}{2} b^...
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1answer
276 views

Determining conserved features using a Bayesian approach

I would like to perform some sort of binary classification, and my data set consists of 100 examples (for each class), which are vectors with 2500 elements. Ideally, I would like to determine which ...
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125 views

Classifiers with post-training constraints on the prediction space

I'm familiar with using tools like SVMs and decision trees for discrete classification problems. But one detail that I have not encountered in that domain is: what do you do if your classifier must ...
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160 views

Independent variable misclassification and statistical tests

Suppose we have our standard DGP, $y=\alpha+\beta x+\varepsilon,$ where $x$ is binary. Let's say the observed $x$ is actually measured with error, so that the explanatory variable is misclassified for ...
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Adjusting the classification threshold of Naive Bayes

I've been involved in a machine learning project recently and am now in the process of writing the project up for a paper submission. We used the naive bayes classifier on the project and developed a ...
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312 views

Category selection for text classification

It is said that to achieve a good result (many different metrics) for text classification, it is not always a business of selecting the algorithm/classifier. Sometimes, it is even more important to ...
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525 views

Using QDA for Non-Gaussian distributions

I am evaluating a Quadratic Discriminant Analysis (QDA) classifier on a high-dimensionality feature set. The features come from highly non-Gaussian distributions. However, when I transform the ...
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2answers
4k views

multiclass classification having class imbalance with Gradient Boosting Classifier

I am using Abalon data for classification from UCI(https://archive.ics.uci.edu/ml/machine-learning-databases/abalone/abalone.data). I have scaled data and used TSNE for visualization. ...
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1answer
798 views

Test for Statistical Significance in the Accuracy of a Machine Learning System

I have what I imagine is an elementary question about evaluating statistical significance, but while I know a lot about probability I can't t-test my way out of a paper bag. From here I'm hoping to ...
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Are there any models that do worse on standardized datasets?

Background I am currently an undergraduate student beginning to explore the field of data science. Recently, our professor introduced us to the concept of standardizing a dataset. My professor ...
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Evaluating classification results when importance of correct classification varies with class

Suppose we have a categorical variable $Y$ and we are trying to classify it. Our decision (the predicted class for $Y$) is $\hat Y$. We are facing a loss function which can be represented by a loss ...
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1answer
94 views

Combining many sparse binary variables

Based on kjetil b halvorsen suggestion, I rephrased my problem: My problem is analogous to the following: i am supposed to predict if a high school student will go to university (Yes/No). I have ...
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2answers
103 views

What is the best way to get the most accurate results with this small dataset?

This is my first question here, I apologize if this is the wrong place or my formatting is not correct. My experience with machine learning and data science, in general, is a graduate-level survey ...
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Question about using Bayesian rule as a classification for continuous data set

Please note that my question is not about coding. I am now learning Bayesian classification and I think I understand it in a discrete case. I have trouble understanding it for multivariate continuous ...
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Is there any strategy for validating the result of a general comparison between several confusion matrices?

Disclaimer: Recently we have developed a python library named PyCM specialized for analyzing multi-class confusion matrices. A compare system has been added in <...
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629 views

Overfitting in Random Forest Classifier?

I would like some help from you in a classification model that I am developing. In summary, the problem is: – Classification problem with binary outcome (0/1) – The classifier is a Random Forest ...
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1answer
161 views

what is the diffrences between online and one pass learning?

as long as I know, online learning takes actions at each time step (for each data), and one-pass algorithm just can see each data once. I already read Wikipedia: about streaming algorithms. These ...
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1answer
496 views

k-means clustered data: how to label newly incoming data

I have a data set with labels that were produced by a k-means clustering algorithm. Now there is some data (with the same data structure) from another source and I wonder what is the most sensible way ...
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Applying boosting to predictions from a Random Forest

I have a class of datasets for a binary classification problem where it is known that Random Forest performs poorly compared with GBM or FFNN, rarely adding anything to an ensemble. I've had an idea ...
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1answer
447 views

Correct cross-validation procedure for single model applied to panel data

Questions What is the correct CV procedure for panel data? I've been thinking of the problem as cross-validating a model fit to multiple time series data. Is the "population informed" CV procedure ...
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428 views

Calibration of penalized (LASSO or ELasticNet) logistic regression models

I would be very grateful for any help me with the following general query regarding calibration of penalized models with a binary outcome. I would like my prediction model to be calibrated (mean ...
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Should I use statistical tests (e.g., Hosmer-Lemeshow) to assess predictive models?

Generally, is it useful to carry out statistical calibration tests on purely predictive models? For instance, if I build predictive model and I choose final model relying on cross validation results (...
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1answer
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Dynamic interactive learning

I am trying to solve a classification problem where I have a set of known X values. I know the classification objective i.e. the discrete set of values the Y can take. However, I don't have any ...

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