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.

2,057 questions with no upvoted or accepted answers
Filter by
Sorted by
Tagged with
3
votes
0answers
436 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 ...
3
votes
0answers
78 views

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 (...
3
votes
1answer
24 views

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 ...
3
votes
0answers
80 views

If I have enough data, can I just model unbalanced classes?

I am tasked with understanding what's causing a rare manufacturing failure (ca. 1 in 5000) to worsen recently (to ca. 1 in 3000). I have a very large database, so I can get sufficient samples from ...
3
votes
2answers
98 views

How to handle machine learning inputs that should be considered as set of vectors, but whoes interpretation is order invariant (order agnostic set)

Basically wondering best practices for input modeling and ML algorithm type(s) for inputs that essentially model samples that are a bag/set of "sub-objects", so order does not matter. Think of the ...
3
votes
0answers
178 views

Why use separate trees for each class in multi-class gradient boosting?

Gradient boosted decision trees can be used to solve multi-class classification problems. Friedman (2001) fit $K$ trees on each iteration—one for each class. Multiple GBM implementations also follow ...
3
votes
0answers
79 views

Supervised document classification with prior distribution on some features

There is a somewhat off-the-beaten-path supervised document classification problem I am trying to tackle. For the sake of simplicity, let's say we are using the bag-of-words approach. Usually, we ...
3
votes
0answers
448 views

What is a good loss function in multiclass multilabel classification where only one of the possible labels is observed?

I am training an ANN in multiclass multilabel scenario, where only one of the possible labels is observed at a time, let me illustrate on an example: I have a state X and the ground truth label Y for ...
3
votes
0answers
267 views

Voting between classifiers : How to prove it works?

Assume m independent binary classifiers with probability $p$ to be correct $p>0.5$. Show that the probability of a voting, e.g. decision is made by the majority of classifiers is correct with ...
3
votes
0answers
481 views

Using confusion matrix to improve my SVM

I ran an SVM classifier on the CIFAR_10 classification workbench. I got about 2/3 accuracy (which I think is Ok, but I want to improve...) Here is my confusion matrix: ...
3
votes
0answers
51 views

Is LinearDiscriminantAnalysis legit for classifying images?

this was moved from SO, hope this is a better place to ask :) on this context: LDA = LinearDiscriminantAnalysis I tried classifying images' descriptors with SVM SVC linear kernel which gave bad ...
3
votes
0answers
401 views

How to not overlook rare but important features when preventing over-fitting in a decision tree?

I have a data set where some binary features divide the sample space roughly in half, whereas other features are much less frequent and occur only for 0.0001 - 0.01 of the sample space. However, those ...
3
votes
1answer
56 views

85% of the samples come from an unknown distribution, the rest come from the same distribution with a larger variance.How to recognize them?

Assume I have a data, say columns are the samples, and rows are the features, the problem is that around 85% of the samples come from an unknown distribution but the rest come from the same type of ...
3
votes
0answers
386 views

Interaction effect in random forest

I'm interested in interaction effect between variables in random forest. I found some information here https://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm#workings. The operating ...
3
votes
0answers
605 views

Extract f-statistic from multiple variable regression model and intercorrelated variables

I'm currently analyzing some microarray data (gene expression) and I'm running the kNN classification algorithm on them. Data consisted from 920 different genes and 8 different observations (with 5 ...
3
votes
0answers
126 views

What information should be released to characterize a dataset for text classification?

I am releasing a dataset for text classification. ​What information would a researcher in natural language processing or machine learning may want to have about this dataset? Here some some ...
3
votes
0answers
2k views

Classification on highly skewed dataset

I have two classes A and B. 98% of the data belongs to class A and 2% of it belongs to class B. Size of the entire dataset is about 2000. I am interested in correctly classifying all the data points ...
3
votes
1answer
65 views

In real clinical diagnostic data set how can we know the “true label” of a patient?

When we were taught about Bayesian probability, we often saw the following example: in a population, there are 5% of people who has disease X, and among the people who have disease X, the current ...
3
votes
0answers
94 views

Classification: a bayesian network for each class?

Is there a technique for classification, where given a feature vector X = (x_1, x_2,..., x_n) and a Bayesian network for each class, which for each x_1, x_2,...,x_n there may be a corresponding node ...
3
votes
1answer
675 views

How to build the feature vector from sentence for intent classification in NLU?

I am trying to develop a NLU (natural language understanding) engine which interprets human language utterance to intent and slots. After some searching, I found this very useful question for NLU ...
3
votes
0answers
80 views

Supervised Classification Machine Learning With Video Project

I am just getting into visual machine learning (currently a mobile developer) and have a challenging project of interest. It involves using video as an input to then determine if a baseball player ...
3
votes
0answers
46 views

PLS between error and negative loglikelihood in classification models?

Consider a large but finite output space $\mathcal{Y}$. Let $\Delta$ denote a loss function between $y^*$ and $\hat{y}$, i.e. $\Delta : \mathcal{Y} \times \mathcal{Y} \rightarrow \mathbb{R_{+}}$. One ...
3
votes
0answers
564 views

Nearest/farthest neighbour between-group distance: an efficient way to find it

This question might be better suited for StackOverflow as it is programming (so you are free to suggest to move it), but it is about a data analysis programming task. The Q: do you know any "elegant" ...
3
votes
0answers
66 views

How should three unordered categories be encoded in a bayesian network framework?

The SAS FAQ suggest that for unordered two categories I should one dummy variables, for example: The common practice of using target values of .1 and .9 instead of 0 and 1 prevents the outputs of ...
3
votes
0answers
711 views

R package for classification and outlier detection together

I have a similar problem as this one. My training samples contain N observations and K>2 classes. I want to classify my test samples into one of the K classes, or as an outlier if it is far from any ...
3
votes
0answers
739 views

Evaluation of a ternary classifier

Are there standard evaluation procedures for non-binary classifiers? In my case I have "nested" classes, being absence and presence of an effect the first and usual binary categorization, but ...
3
votes
1answer
1k views

What exactly is the mathematical definition of a classifier / classification algorithm?

I just started an intro machine learning course, and to get things better organized in my head, I was trying to come up with exactly what is needed to completely specify a classification algorithm. I ...
3
votes
0answers
463 views

Understanding the approach behind variable importance returned with Xgboost method in R package caret

I recently implemented the R package caret, for a binary categorical outcome regarding a transcriptomic microarray dataset. As i used the method from the xgboost package(method="xgbtree"), then i used ...
3
votes
0answers
556 views

How to tune the weak learner in boosted algorithms

It is commonly said that boosted algorithms (adaboost, gradient boosted trees) are composed of many "weak" learners. Let's stick to decision trees as the base learners. Some empirical studies ...
3
votes
1answer
3k views

Is the Laplace/Lidstone smoothing parameter (talking about Multinomial/Bernoulli Naive Bayes) related to the particular structure of the dataset?

I'm working with Multinomial and Bernoulli Naive Bayes implementation of scikit-learn (python) for text classification. I'm using the 20_newsgroups dataset. From the scikit documentation we have: <...
3
votes
0answers
217 views

What is the acceptable event rate to use ROC-AUC instead of precision-recall curve?

It says here However, when dealing with highly skewed datasets, Precision-Recall (PR) curves give a more informative picture of an algorithm's performance. My question is; What is the common ...
3
votes
0answers
228 views

Add extra class to a pre-trained softmax classifier

Question I'm wondering if some could add an extra class to a pre-trained softmax-ed neural network, trained for multi-class classification problem, without reusing the old training data. Details ...
3
votes
0answers
65 views

How can I estimate the influence/significance of the every observation on classification?

There are many ways to estimate the significance of the features on the classification model. But how I can estimate the influence of the every observation on the classification quality? My thinking ...
3
votes
0answers
126 views

Lower classification accuracy after dimensionality reduction

Generally feature selection and dimensionality reduction are recommended to raise classification accuracy. Currently, I am working with datasets from neuroscience which are huge. I have 84 patients, ...
3
votes
0answers
466 views

How to build “supervised clustering” for neural networks?

I'm confused as to what the output would be. Consider the "blind source separation" problem. Let's say I have a ton of training examples where the input is the final cacophony of sounds as a sound ...
3
votes
0answers
477 views

Calculating ROC curve for two gaussian distributions with equal variance

I recall a simple formula in a paper which relates the distance between two gaussian distributions (what psychologists refer to as d') to the ROC curve under the assumption that both distributions ...
3
votes
0answers
332 views

For classification w unbalanced datasets, is class-weighing the same as oversampling?

in unbalanced classification problems, I find myself using class_weigh = "auto" or similar parameters often, but I don't think I'm fully understanding what it's doing. I know that it's the industry ...
3
votes
0answers
484 views

How to use KL-divergence in naive bayes classifier to weight features?

I have a dataset consisting of 4 classes. I have implemented the Gaussian Naive Classifier (in Matlab). In the training phase I calculate the mean and variance for each feature and each class as well ...
3
votes
0answers
107 views

Problems with classification in imbalanced datasets

I often read about the problematic of doing classification in imbalanced datasets and methods to address it. Namely, off-the-shelf classifiers learn to minimize some form of total miss-clasffication ...
3
votes
0answers
3k views

Regression models to only predict integers (instead of floating point numbers)?

I have a dataset that consists of about 50 different attributes. One of these attributes I want to predict, using the other attributes as features. The values of the attribute that I want to predict ...
3
votes
0answers
83 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
0answers
800 views

Combining bootstrap and cross validation

I recently read this paper: Estimating misclassification error with small samples via bootstrap cross-validation, by Fu et al. (BMC Bioinformatics, 2005). The authors talk about combining cross ...
3
votes
0answers
146 views

Are regression problems more likely to overfit than classification problems?

I will illustrate my question on an example: Let's say we have a dataset that we want to split into two disjoint sets of similar size. The dataset has a high dimensional feature (several hundred ...
3
votes
0answers
95 views

Missing values with Community structure in networks?

Is there a way to predict Missing values with Community structure in networks? I have a data set with a couple dozen variables, such as age, level of education, self-assessed (via a Likert scale) ...
3
votes
0answers
638 views

Is there an effect size for Kappa's?

I am staring a project on comparing standard ways of creating a classifier with some heuristic methods. The heuristic methods should result in a faster training for the classifier but should result in ...
3
votes
0answers
1k views

Friedman's test to identify best of multiple classifiers on multiple domains

I have several classifiers $f_i (i=1..N)$ and calculated performance measures on multiple domains (D) for each. Thus, there are NxD values. I want to find out (increasing complexity): Is a ...
3
votes
1answer
58 views

prediction from incomplete observations

Suppose I have a linear model predicting class-membership from a set of predictors. Now, I am going to classify a new observation which has, however, some predictor values missing. How can I deal with ...
3
votes
0answers
223 views

What are some classic examples of feature selection in classification?

Is there a classic example showing the importance of good feature selection in classification? The ideal example would be simple, and very easy to understand. I've been volunteered/instructed to put ...
3
votes
0answers
1k views

Probability extraction from random forest classifier

I have a random forest to perform classification. I need the real probability of the predicted class. They take a feature vector X and output a predicted class C. Additionally we can compute the ...
3
votes
0answers
816 views

Clarification on LDA and the multivariate Gaussian

From my understanding, to calculate the posterior probability of a sample $x$ belonging to a class $k$ using Linear Discriminant Analysis you would first calculate the eigenvector matrix $W$ required ...

1 2
3
4 5
42