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.
2,314 questions with no upvoted or accepted answers
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Convolutional neural network for multi-variate time series?
I want to use CNN architectures for classification of multivariate time-series, where we apply one label to each sequence.
I searched the net for the available designs in the literature and i found ...
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Logistic regression for classification: are there any analytical solutions for the out-of-sample accuracy?
I run a binary logistic regression, with a binary dependent variable and a continuous independent one.
Now I want to evaluate the out-of-sample performance of the classification algorithm so obtained. ...
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Precision and recall of a random classifier
My understanding of precision and recall tells me that there is a tradeoff between these two measures: you can improve one at the cost of the other.
However, when I think of a random classifier (on a ...
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Computing a bootstrap confidence interval for the prediction error with the percentile and the BCa method
I have two related questions regarding the computation of a non-parametric bootstrap confidence interval for the prediction error.
Setting: I have a sample S from a data population P and a learner L, ...
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Random Forest: Class specific feature importance
I'm using the bigrf R-package to analyse a dataset with ca. 50.000 observations x 120 variables, classified into two groups.
After growing a forest of 1000 trees, ...
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Is there a ML or DL tool that can learn to detect periodically occurring patterns in a one dimensional time series?
I am trying to create a tool that labels refrigerator temperature readings. A reading is taken every 5 minutes, and its label identifies whether of not it was taken while the refrigerator was ...
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How to predict routes using clustering data
I've been working on a ship route prediction algorithm such that given the past and current trajectory of a ship I am able to estimate the future one. The trajectories are represented as a sequence of ...
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When is there a free lunch?
The no free lunch theorem (NFL) states that
Theorem (Wolpert and Macready 1997) Let $A$ be any learning algorithm for the task of binary classification with respect to the
$0−1$ loss over a ...
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True positive, false negative, true negative, false positive definitions for multiclass-multilabel classification?
I'm trying to apply some evaluation metrics to several clustering methods. I thought that I knew them basing on the multiclass confusion matrix, considering the rows as the actual classes and the ...
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Micro vs weighted F1 score
In a multi-label or multi-class classification setting, when choosing between a micro or a weighted F1 score, what shall I take into account?
The main upside of choosing macro is that one gets a ...
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How to subset alternatives in nested multinomial logistic regression?
I am trying to predict whether or not captains in a particular groundfish fishery choose to fish on any given day and what variables may influence that decision. Originally I had planned on using ...
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Features for binary time-series event prediction
This question is somewhat inspired by the answer to Features for time series classification.
The difference to that question is that I have a dataset with multi-dimensional time-series where I have ...
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How to combine noisy and noise-free datasets to train a model
Overview
Suppose I have two datasets, both of which consist of rows of features and their matching labels. One of these datasets is noise-free and its labels correspond to the ground truth, but the ...
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Multi-label classification: Predict product category
I want to predict to which product category a product belongs. A total of 400k products need to be translated from the old (less refined) to the new product category tree. (E.g. alarm clock used to ...
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why use diagonal $\Sigma$ when working with Bayes decision theory?
My prof. said in the class that for Bayes decision rule, the likelihood is Gaussian and in practice, we will almost always work with a diagonal $\Sigma$. Why is that? I know that a diagonal $\Sigma$ ...
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Combining posterior probabilities from multiple classifiers
I am new to machine learning and can't get my head around this problem. I have two patient datasets, the first ($D_1$) contains $Y,Z,X$ that convey blood-sample information and the second ($D_2$) ...
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k-fold cross validation vs k times hold-out validation
I am facing the evaluation of a genetic programming algorithm. I am using the Proben1 cancer1 dataset to evaluate the models created by this algorithm. This dataset contains 699 samples, which is ...
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Reasons for transforming multiple class classification problem into a set of binary sub-problems?
Does anyone know of a good reference that list the reasons for transforming multiple class classification problem into a set of binary sub-problems?
In response to comment: One reason to transform a ...
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Computation of log-likelihood in semi-supervised naive bayes
I have the following 2 questions about log-likelihood computation in semi-supervised Naive Bayes.
I have read on several documents online that, in every EM iteration of the semi-supervised Naive ...
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Classification of multiple time series and case level attributes
I'm pretty new to machine learning so wondering whether someone can help check my thinking or point me in the right direction!
I need to create a classifier which can predict an outcome for a person ...
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Maximum entropy classifier and sentiment analysis
I am doing a project work in sentiment analysis (on Twitter data) using machine learning approach. In order to find the 'best' way to this I have experimented with naive Bayesian and maximum entropy ...
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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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What do Cross Validation results actually tell about Bias and Variance?
I am trying to get a deeper understanding of the common ML pipelines and I have some doubts regarding Cross Validation, why do we really use it and what does it really tell us about Bias and Variance.
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How to compute gain statistic for the multinomial Naive Bayes classifier from Jurafsky and Martin (2018)
I'm trying to figure out how to compute the gain statistic G(w) following the fitting of the multinomial Naive Bayes model. This statistic is described on p17 of the new edition of Jurafsky and ...
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logistic regression prediction: changing interpretation with changing prior
The data include 3 equally sized subsets A, B and C, belonging to two classes:
A belongs to class 1.
B and C belong to class 2.
The prior probabilities of an observation coming from class 1 ...
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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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Estimating parameters using Kullback-Leibler or Kolmogorov-Smirnoff via Nelder-Mead
I want to find the parameters of a model which specifies a set of classification probabilities, for say M classes. (I'll use the parameters in another model later.)
Given a set of parameters $\theta$,...
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Regarding the size of training data for building classifier
When we build a classifier, like SVM or Naive Bayesian, are there any generic rules or theoretical derivations on the size of training data set? For example, to train a SVM-based classifier, what ...
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Understanding a calibration plot for lightGBM binary classifier
I wanted to assess the performance of my lightGBM classifier using a calibration plot. If I understood correctly, a calibration plot visualizes the alignment between the predicted probabilities by the ...
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Predict churn in a range of time after observation window is finished
I'm building a churn model. Each user's historic data (observation window) is a constant period, but each observation window contains different dates.
For example the next figure:
Let's say, that the ...
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What is the difference of "normal" F1 and macro average F1 score with binary classification
Please note that I always talk about binary classification here. I do not speak about multi class classification.
In case of unbalanced binary datasets it is a good practice to use F1 score. While ...
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Measure agreement among experts in multi-label classification task
I was wondering whether there is a metric that can be used in order to compute the agreement, and therefore something like an upper bound for classifiers, among expert-labelled data.
Assume there is ...
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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 ...
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Confusion matrix for multilabel classification
I know that a similar subject was treated here, but my question is a little bit different.
I have a result of multilabel classification, like this (2 observations, 3 labels in the example, in ...
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Effectiveness of Standardization and Normalization in Machine Learning
I am just studying the basics of machine learning and had a question about the standardisation and normalisation of the features and its effectiveness.
I have read this CrossValidated question and ...
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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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Comparing classifiers using McNemar Test
When comparing the performance of two binary classifiers using McNemar test, should the two confusion matrices of the models be based on the training set, the validation set, or even a second ...
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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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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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Probability distribution over classes as labels in classification task
Classical classification problem has next formulation.
Given a set of $n$ attributes, a set of $k$ classes and a set of labelled training instances:
$(i_i, l_j),...,(i_j, l_j)$, where
$ i = (v_1, v_2,...
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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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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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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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Transferring to new domain
Suppose I have a set of characters with both natural scene and synthetic images, and another set with only synthetic images. I'd like to make a classifier which is trained on only on this data and ...
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Number of states and symbols in multi class Hidden Markov Model classifier
I'm designing a multi class classifier (for 4 classes) using Discrete HMMs with States N and Symbols M for each of the HMM.
However, I found that recognition performance(i.e highest log likelihood) ...
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Asynchronous data stream matching
Suppose you have a classifier $C^n$ which continuously outputs a stream of classification labels $K^n_i$ and corresponding timestamps $T^n_i$. Also, we know the prior probability $P(K^n) \forall n$. ...
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Unlearning Neural Network? Prevent learning from a specific feature
Is it possible to train a NN to avoid the features that a different neural network finds? For example, let's train a simple 1 layer CNN with 1x1 kernels on a supervised binary classification problem. ...
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Does artificially balancing outcomes in regression lead to poor calibration? If so, how to show the poor calibration?
In "classification" problems, it is common for there to be unbalanced-classes. To combat what appears to be a problem (though I would argue that it usually is not a problem), it is common ...
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Trying to understand the theory behind my similar / better results than XGBoost using a calibrated linear model (GAM)
I just opened a discussion on reddit asking about why/how the calibrated linear models I've been training have been getting similar / better results than XGBoost in my experiments. I was told to cross ...
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Is there a decision tree impurity metric based on maximum of probabilities?
I'm trying to understand impurity metrics in decision tree learning, in particular the Gini impurity. Questioning one of the assumptions of Gini impurity has led me to another impurity measure which ...
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