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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 properly integrate data from multiple studies in a training/testing classification framework?

I currently have data from several studies, with each having different sample sizes and possibly different set-ups. There is a common binary variable of interest across all studies that I would like ...
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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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Knn Classification on iris dataset

I'm following along https://rpubs.com/Drmadhu/IRISclassification to understand Knn classification. Here's the code I have: library(FNN) iris.sample<-sample.int(n=nrow(irisdat),size=floor(0.75*...
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Chi-square based tests tending to zero for large sample sizes [duplicate]

I think I have some misconception about chi-square based tests such as Pearson's test or McNemar's test. The way the tests are defined, the test statistics produced by the formulas are not stable ...
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No need for bias term if data is standardised? Linear classification models

For linear classification models, e.g. perceptron, bias term allows to move separating hyperplane away from origin. If data is scattered around the zero does that mean that we don't need bias term?
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online news readers engagement index (inverse of churn model?)

I would like to get a second opinion of the problem of assigning engagement scores to online news readers. Currently, I build a churn model as required by the company, that mainly predicts the ...
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Combining classifiers built on different features

I have a binary classification problem and two sets of features(for the same target variable) . Due to some reason(domain knowledge), I can't combine those two feature sets into one and then do ...
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Improve precision/recall for class imbalance?

Trying to get better precision/recall for both classes ... any tips? I have heterogeneous features [a few num vars, a few cat vars, and 2 text vars] Target is a binary classification w/ class ...
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undersampling before or after cross-validation

I have a classification problem with highly unbalanced classes. In 4000 samples, 1% is 1s and 99% is 0s. Normally, I would use the balance technique only in the training set. However, I expect that ...
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AUC of single model vs AUC of separate models on same data

I have created two separate binary classifiers that predict the same kind of label using 2 separate datasets. The data is in the same format. They both have a AUC of 0.94 and 0.95 I have then created ...
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How to get similarity matrix from a random forest model? [on hold]

I have trained a randomforestclassifier on a dataset with 652 samples. I have achieved 89.6% accuracy. Now I want to extract the similarity matrix from the trained classifer and do some clustering. ...
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How to separate training set and validation set using 80/20 rule?

I have two folders: One folder contains images of non-dogs, and the second folder contains images of dogs. I am to divide these folders into a "training set" and a "validation set" with the 80/20 rule....
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Strings coming from distinct distributions

I have a binary classification problem. Two distinct classes of strings are available. The strings are of fixed size and made of three different characters only. I have around $10000$ instances of ...
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how to statisticaly test data collection methods

I have 2 dataset collected with different data collection methods (very large datasets). I use the same classification algorithm on both dataset and get a greatly improved result for one compare to ...
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Feature engineering with cross validation, then testing on a holdout data set?

We have 3000 samples for two classes, roughly 2000:1000. Our plan is to train a classifier on the samples but first to set aside 30% randomly selected stratified samples as a "holdout data set" for a ...
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39 views

Model evaluation metric when interested only on class distribution on sets of samples

I need to evaluate a sentiment classifier applied on posts gathered from some blogs. I am not interested on which post gets which sentiment value (positive/negative) but solely on the percentage of ...
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Are there any other image classification methods besides using neural networks?

When reading about image classification, the only occurring terms are "neural networks", "deep learning" and "CNN". It seems like there are no other methods for this task. I have worked with neural ...
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How significant are the results of my classifier?

I have seen this and this questions, but all of them are about accuracy. I have 5 different binary classifiers on imbalanced datasets (most of the samples are negative). I need to prove that one of ...
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Normalising predictions across datasets

I am currently training a model to predict a binary attribute. The model gives the output in range [0, 1]. The metric is TPR@FPR, e.g. I need to achieve maximum ...
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Best Machine Learning Algorithm for Grouping Similar Census/Survey [on hold]

I am working in a company which have many census/survey. The problem is there are so many census/survey that have similar questionnaire variable which made many of our census/survey seems to be ...
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Training error higher than test error and validation error

I am training a genetic algorithm for classification and strangely, the training error is consistently HIGHER than the validation and test error. The training and validation set are both small size ...
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How effective is SVM over big datasets?

I have a dataset of 800,000 observations and 11 features that I am using for a classification problem. I tried to optimize my model many times but in vain. The one thing I haven't tried is using SVM. ...
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Randomized search on big dataset

I have a dataset of 700,000 rows that Im applying random search on. My parameter grid looks like this: ...
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Naive Bayes missclassification rate across classes

I have a dataset with income, age sex and education as categorical features. I used R to create a Naive Bayes classifier as follows: income ~ age + sex + education. I got the following a-priori and ...
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McNemar or Cochran's Q for evaluating multiclass classifiers?

full disclosure: I did a semi-cross post of this question due to low traffic. Once I get an answer on any of the two questions, I will link the answer back to the respective other. tl;dr For ...
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Is it possible to have recall and precision of 0 while having an area under PR ~0.5?

As the title suggests, I am running a Random Forest classifier using Scala. To evaluate this classifier (and since I am handling highly imbalanced classes), I used the ...
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1answer
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How to Compute the Brier Score for more than Two Classes

tl;dr How do I correctly compute the Brier score for more than two classes? I got confusing results with different approaches. Details below. As suggested to me in a comment to this question, I ...
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Estimating the False Reject Rate (FRR) of a classifier in production

I have trained a binary classifier which runs in production on remote distributed devices (which are out of my control). The model was trained on positive and negative samples, and I have chosen the ...
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Suggestion on Papers to Read on Classifier Selection

I'm looking for some papers to read to get started understanding classifier selection method in a computer security system. I wanted to develop a Multiple Classifier System based on a pool of ...
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Why Converting Regression to Ordinal Regression

Intro: Ordinal Regression/Classification is a classification where the labels have orders (https://en.wikipedia.org/wiki/Ordinal_regression) Question: Can you comment what are pros and cons if ...
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1answer
18 views

Indicator function expression for two sample test

I am trying to understand the theory of this paper. Basically, the paper tries to lay down a framework for using two sample tests using binary classifiers. Let there be two samples $S_p$~$P^n$ and $...
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1answer
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Statistical test/method to create clusters or determine similarity between groups with many values for a single classifier

Situation: We are utilizing an application that transcribes phone calls into text and identifies when certain phrases (that we define) are said. We then enter logic for "categories" that use the ...
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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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Classify a vector with M values each drawn from a different categorical distribution

I have a classification problem that is formulated as follows. Each observation belongs to one of $N$ (unknown) classes in $C = \{C_1, C_2, \ldots, C_N\}$ Each observation is an $M$-length vector $O =...
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How does logistic regression “elegantly” handle unbalanced classes?

Frank Harrell in this interesting blog post "Classification vs. Prediction" points out that using stratified sampling to handle unbalanced classes is a bad idea, since a classifier trained on an ...
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Visualising sentence vectors by averaging word vectors

I have $82114$ sentences for which I have found the vector representation by summing over individual word vectors(using Word2Vec). Now I have a vector representation for each sentence in my dataset. ...
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correct setting of eval_set in multiclass classification xgboost python , error is “ Check failed: preds.size() == info.labels_.size()” [closed]

i have a multiclass classification problem with 3 classes [-1,0,1] . i'd like to use eval_set in xgboost. but it fails with error: ...
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What is the rationale for assuming that prediction values of a classifier are normally distributed per class?

A standard image to describe how to understand ROC curves is by showing the distribution of a model's predictions, grouped by real label. In this image, a histogram of predictions for class 'good' (in ...
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Accuracy of classification vs. accuracy of class probabilities

I have a dataset that contains a binary response variable: equal to 1 if the person responded to the survey and 0 otherwise, as well as a host of auxiliary variables X. What I want to do is use this ...
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how to check the distribution of the training set and testing set are similar

I have been playing the Kaggle Competition and I find there is a situation that the distribution of the training set and testing set are different, so I am wondering how to check the distribution of ...
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Evaluating Unbalanced Multiclass Classifiers: Which Tests to Use? [closed]

I am looking for some comprehensive instructions and ideally out of the box solutions (ideally for python) for evaluating different classifiers (which are already trained) for a multiclass ...
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Are these Multi-label document classification experiment steps sensible?

I plan to filter an input document using 4 different labels. Just for an example, a document discussing about movie summary needs to be labeled with 4 labels (Romance, Drama, Fiction, Hollywood). ...
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Classification with ONLY categorical data

Suppose I have a table with some factor characteristics of some plants. For instance, petal color, pollen color, and so on. What is the best way to classify that data? Is it feasible to use some of ...
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Can mixture model used as classification prediction

I am learning a prediction model in statistic. I read that we can used mixture model as a classification. My question is, assume we have a data which can be divided into two groups. Can we in this ...
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LSTM - When to use sliding window in time series classification?

Say I have a tensor of data with shape (30, 16000, 38) - where each tuple element corresponds to ...
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14 views

WoE for Random Forest and SVM

There are a lot written about WoE (Weight of Evidence) transformation for the case of Logistic Regression Classifier. It works great. The question: can one (or does it make sense) to use this WoE ...
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1answer
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Is it valid to use ROC calculated during test/validation to interpret results of final production model?

I've trained a binary classification model which outputs a "probability" between (0,1). During testing and validation, I use the ROC to measure the performance of the model. Also, I use the ROC to ...
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Regression/ classifcation model with Ordinal Outcome types and uses

Can somebody explain me the different types of models for the train function from the caret package in R under the heading "ordinal outcomes"? Specifically, "Adjacent Categories Probability Model for ...
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1answer
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Modelling small data set problem

I have a small dataset (20 instances per 13 classes). The 13 classes are human users from their behavior features, I have to classify if an unseen behavior feature is of a user or not. Data: These ...
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Using categories instead of WoE values

As for as I can understand, the Weight of Evidence strategy is the following: For continuous independent variables : First, create bins (categories / groups) for a continuous independent variable and ...