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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30 views

How are cross validation and i.i.d. assumption of of a dataset related?

Is it necessary for the observations of the data set to be IID in order to use cross-validation on it? If so, why ? Could you explain in the context of a classification using decision tree.
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Calculate the confidence interval of a balanced accuracy by taking the mean of the CIs of sensitivity and specificity?

Because sensitivity and specificity are typically estimated as binomial proportions (e.g. k = TP, n = TP+FN), we can use any of the methods used to estimate the confidence interval for binomial ...
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Why does XGBoost have a learning rate?

Original Question Having used XGBoost a fair bit, clearly changing the learning rate dramatically affects the algorithm's performance. That said, I really can't understand the theoretical ...
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ROC AUC of 0.5 on train set

I am trying to build a binary classifie, but my classifier does not seem to learn anything from my data (I get AUC of 0.5 when I try and predict the train set - most of my observations are predicted ...
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Best Machine Learning Model to get the Probability of an Event Occurring

I have a table full of different applications which have various fields that I want to use as features for my model (i.e. vulnerabilities, assessment results, etc.). These applications also have a ...
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Selecting Threshhold from ROC curve

I plotted an ROC curve for a classification problem and I am looking a way to find out the threshold point for the left most top point - Highest TPR and low FPR. How do I do it in Python ?
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What are assumptions for logistic binomial regression if all independent and independent variables are dichotomous?

I have three independent and one dependent variables of the dichotomous type and I am trying to use logistic binomial regression. I have 117 observations in total. When I read the literature, some of ...
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A little help on text classification

Right now, I am working on building a efficient classification for my company. We work as a social monitoring company, basically we collect data from social media sites to see the engagement, comment, ...
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Classification on Dataset with multiple rows per person

I have a data set where there are multiple rows per person. If person1 has 3 rows, out of 10 features, only couple of them change. The remaining features have the same values repeated. There are ...
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How to define a time series classification problem?

I have 3 sets of time series data generated from sensors, I believe they have some correlation themselves. Certain "modes" of the system can be defined from the patterns from these signals. The signal ...
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1answer
38 views

Can you use your original variables after seeing the results of PCA?

I have a dataset of 40 variables and 55 samples. I want to run classification algorithm. Is this possible that I do PCA and based on which variables are more important in each principle component, use ...
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Is it justified to discretize / bin a skewed variable in a classification problem?

How would a skewed variable impact a classification problem (logistic regression, tree model)? Is it justified to bin the skewed variable ? My data set comprises of younger demographic and fewer older ...
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Good starting point for NLP sentence classification on limited computational resources?

I am designing a project where I plan to train a supervised ML model to classify English sentences of 10-30 words long into one of 8 or 9 categories by what kind of meaning it conveys. I have a good ...
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How does colinearity among the features impact classification problem in a) Logistic Regression b) Tree models like Decision Tree or Random Forest? [duplicate]

If I have 20 variables, should I do a pair-wise correlation check before building a classification model with Logistic Reg, Decision Tree or Random Forest ? Multicolinearity is a problem in regression....
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Converting Continuous variable to Categorical [duplicate]

When should one consider converting continuous variable into categorical variable ? Are there guidelines ? Is it justified to bin skewed variable ? How should I determine the range / binning when I do ...
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1answer
130 views

Bayes Optimal Classifier for multinomial classification

I understand the meaning and how to deduce a Bayes optimal classifier in binary classification, but I am not sure how to derive this in the context of multinomial classification. Do we use the naive ...
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1answer
295 views

Using multiple TF-IDF matrices on a classification task

I have a dataframe with a certain number of columns of type text. Let's say column A has the name (random names that cannot be used as keys but resemble sentences) and B has its description(a bigger ...
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Statistics/Data Science: Real world example for a donut destribution?

I'm looking for different real world examples for donut distribution (in the statistic meaning). https://i.stack.imgur.com/VrGq4.jpg For the most other distributions, I found good examples. But for ...
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Multi-class classification with prior knowledge of class similarity?

Backrounds I would like to build a model that predicts a month label $\mathbf{y}$ from a given set of features $\mathbf{X}$. Data structure is as follows. $\mathbf{X} : N_{samples} \times N_{features}...
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Estimation of Sparse Panel Data

There are 1000 students and 100 teachers. Each teacher is given the answer scripts of randomly selected 100 students. So in total 10,000 answer scripts are judged. Now this is sort of panel data, but ...
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What is between regression and ordinal classification (or called ordinal regression)?

There are many articles explaining the difference between regression and ordinal classification, most of them mentioned that regression is for continuous response while ordinal classification is for ...
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Classification - compute residual error

I'm new to the field of time series forecasting and would like to know if what I think I'm doing makes sense. I'm looking to do a classification model to have a positive/negative indicator of power ...
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1answer
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What are some ways to measure the width of desert roads in satellite images?

I'm interested to know what sort of software and/or concepts should I learn to be be able to get a computer to recognize desert roads in a satellite image and measure for example their average widths. ...
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215 views

What is minCases in C5.0Control using R

from Package (C5.0 Decision tree Using R ) definition "minCases : an integer for the smallest number of samples that must be put in at least two of the splits." I very confuse about it . Please ...
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How to do validation of classification rasters in R without having reference data? [closed]

I did supervised and unsupervised classifications for my raster with this code: ...
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Do we have to tune the number of trees in a random forest?

Software implementations of random forest classifiers have a number of parameters to allow users to fine-tune the algorithm's behavior, including the number of trees $T$ in the forest. Is this a ...
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1answer
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how taking multiple tests increases chance of detection of illness if you have it?

I thought of this as I'm reading about covid tests accuracy, and I am thinking how taking multiple tests influences the chance of correctly detecting illness/no illness. So, if I understand this ...
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When is unbalanced data really a problem in Machine Learning?

We already had multiple questions about unbalanced data when using logistic regression, SVM, decision trees, bagging and a number of other similar questions, what makes it a very popular topic! ...
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1answer
251 views

Classification in real time without prior knowledge of the number of classes

Is there an implemented algorithm (with python/R or java in preference) that can classify incoming data from an unknown generator with absolutely no prior knowledge or assumption. For example: Let G ...
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1answer
141 views

unsupervised clustering with “unclassified” items

I have data (some behavioral features, measured on some scales) on people. I want to cluster people based on these features. This is an unsupervised scenario, as I have no prior knowledge on the ...
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What neural network loss function to use for multi-class, multi-label classification tasks where labels refer to counts?

I would like to train a neural network to detect which of N classes is present, and in what amounts. In other words each example x has label vector y where y_i >= 0. For example, this could be an ...
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1answer
334 views

How to build classification model towards some rare response classes?

I was asked to build a predictive classification model that can predict some types of response. I am interested in 6 classes, however, the total occurence of these 6 classes (out of almost half a ...
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What are the signs of noisy labels in a dataset?

When learning a classification model in supervised machine learning, how can we test whether the labels in the dataset are noisy or not? Is there any particular way to check it or any specific sign to ...
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Classification using Mann Whitney U test

Can the results of a Mann Whitney U test be used to classify future measurements into one of the two populations? If not, is there a better way to convert the measurements of two populations into the ...
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1answer
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Is the notion of bias and variance relevant to a classifier?

I understand bias and variance in the context of linear regression and cross validation. Is a similar notion relevant for binary classification ? If so, how may I calculate it ? Let’s say I have 1000 ...
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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 bag-...
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Derive an expression for the decision rule for a binary classification classifier

I want to derive the decision rule for the local constant logistic regression: Consider the log-likelihood for the GLM (general linearised model) \begin{equation} l( \beta_{0}, \beta_{1})= \sum_{i=1}^{...
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1answer
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How should I decide the decision-threshold in a classification built using stacked ensemble classifier

I am attempting a stacked ensemble model to achieve binary classification for the first time. Should the decision threshold be One for which I receive max F1 score (assuming F1 score drives the ...
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NLP multiclass classification with many sparse classes

I am attempting to use natural language processing to geocode "addresses". The address is the result of a write-in of a survey where the respondent is instructed to give their city, state, ...
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How is ExtraTrees different from Decision Tree for classification of dataset with one feature?

From what I understand ExtraTrees has one source of randomness in building an ensemble - random selection of features. But if there is only one feature, shouldn't ExtraTrees be the same as a ...
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How to determine the correct target for classification probability when the observed samples are probabilities of each class?

I have data in which each event's outcome can be described by a probability of a categorical occurrence. For example, if all of the possible class outcomes are A, B, C, or D suppose in one event 7/10 ...
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Different classifiers for positve and negative data [closed]

I am trying to build a model to classify spam and non-spam. My training process involved multiple classifiers and I found out that Model A is good at classifying spam (high true positive rate) and ...
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1answer
40 views

Gradient Boosting Positive/Negative feature importance in python

I am using gradient boosting to predict feature importance for a classification problem where one class is success and other is failed. However my model is only predicting feature importance for ...
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1answer
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Unbiasing One-vs-all

When you train a one-vs-all multi-class classification, the rule of thumb is that for each class (e.g. class A) mark it as class 0 and others as class 1. Then you split the data as you wish and train ...
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How can I implement probability prediction for One vs One classifier specifically in Sklearn?

I am trying to get probability instead of hard prediction by a One vs One classifier. It is not supported by Sklearn implicitly. Is there nay way to implement it by myself? If so please explain? For ...
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Combining multiple classifiers

I am trying to do a binary classification of text articles into {relevant, non-relevant}. The text articles have following features: [[article text, ...
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1answer
763 views

How does feature selection work for non linear models?

A model like a neural network or an SVM is called for only if the interactions between the features and the target is non-linear, otherwise we're better off using linear or logistic regression. But ...
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1answer
804 views

Improve the precision of random forest for count data

I am trying to create a classification model that predicts whether a customer will enquire for a financial product based on some 250 independent variables. 98% of the variables are count variables and ...
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439 views

Is up- or down-sampling imbalanced data actually that effective? Why?

I frequently hear up- or down-sampling of data discussed as a way of dealing with classification of imbalanced data. I understand that this could be useful if you're working with a binary (as opposed ...
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Classification vs. clustering question

So I have a question on clustering vs. classification. I know there are tons of questions on this here and elsewhere on the Internet, but I have not found my answer so far. I think this (A clustering ...

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