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 add location information to input features?

I'm working on a project to monitor the health of experimental sensors by building a ML model to classify if a given time history signal is looking as expected or if the sensor that outputed it is ...
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How to compute/estimate the probability of the mean value of a number of x results of a classification NN?

I use a neural network to classify the sentiment of some news articles per day, regarding a specific topic. Possible results are $[1,2,3,4,5]$ (1=very negative, ..., 5 = very positive). Using one ...
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117 views

Adding (meaningful) features does not improve model performance

I am struggling with confusion matrices and their outputs. I thought to follow all the steps right, but unfortunately it seems that something is not going well. I had a dataset built and labelled on ...
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Is there any relationship between an SVM classifier performance and the distribution of the dataset?

Do the accuracy and performance of an SVM classifier have a relationship with the distribution of a dataset? For instance if the dataset is distributed uniformly, how does it affect the performance of ...
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Why does the Youden rule does not recommend a threshold of 0.5 on balanced data?

Suppose I have a logistic regression model estimated using a balanced target (equal group sizes). My questions concern the optimal threshold for prediction and it's relationship with the Youden's rule ...
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43 views

Parameters of LDA

I was learning from Elements of statistics p.109 under the topic LINEAR DISCRIMINANT ANALYSIS and I saw the function below for linear discriminant function $\delta_k = X^T\sum^{-1}\mu_k -\frac{1}{2}\...
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Estimating Irreducible error from kappa statistics for medical applications

In many medical applications that involve some form of classification the data is annotated by expert humans (e.g. histopathologists, radiologists). This is then taken as the ground-truth. However, it ...
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74 views

FInding stationary points of Logistic regression with cross-entropy loss

Let's say that I want to find the stationary points of the Cross-Entropy Loss function when using a logistic regression The 1 D logistc function is given by : \begin{equation}\label{eq2} \begin{split} ...
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60 views

Best method to handle unknown class in supervised classification

I have a training dataset, where the records are labelled into 3 classes: A, B and C. My testing dataset consists of records that belong to classes A, B, C and records that do not belong to any of the ...
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25 views

How to decide which features are important in this binary classification task?

Consider a binary classification problem, where the dataset is highly imbalanced, with only around 20% positive labels and 80% negative labels. Feature A has higher AuROC when considering all the data,...
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33 views

Multi-Class Text Classification with Negative Training Examples

I have a problem similar to the questions Here and Here. In short, for a multi-class text classification problem, how to incorporate samples where I only know the classes they do not belong to? In a ...
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53 views

Loss Function for Testing and Validation

For classification problems, while the "real" loss function is often the 0-1 loss, we often choose a surrogate loss function that makes the learning easy. It is often the case that the loss ...
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209 views

Is there a label smoothing version for multi-label classification?

I use label-smoothing for multi-class single label classification as follows. ...
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49 views

Analyze misclassifications

I've trained a classification model (XGBoost) but I'm having some issues with misclassification (especially false positive). I want to analyze the misclassified observations to see if the model misses ...
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147 views

Confidence/Prediction Interval on binary/multiclass problems

I've recently fine-tuned a deep learning framework/model BERT for a sentiment classification task. I'd like to look at the confidence/prediction interval of the predicted sentiment scores (class 1 and ...
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35 views

Does it make more logical sense to model the discrete FFT output as a categorical variable or a numerical variable?

I am training a time-series data classifier and some of my features are the output of CT FFT. The results are of course discrete frequencies. I understand that they are in numerical order and higher ...
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196 views

Metrics for Multilabel Classification

From what I've read, F1-score is a commonly used metric to assess the performance of a multilabel classification problem. However, I recently came across mAP@K and mAR@K as metrics used for ...
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43 views

Logistic generalized Additive Model (GAM)

How are the smoothers fitted in case of Logistic GAMS? For Gaussian response variable, many smoothers are defined such as splines and local regression etc. But how are they used in case of Logistic ...
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32 views

Error metric to compare ratios derived from a binary prediction task

I'm working on a research problem where a binary classification task ultimately produces a ratio downstream. I would like to understand the best way to quantitatively compare the resultant ratio to ...
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41 views

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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140 views

Is the AUC an incoherent measure of classifier performance?

I'm learning about performance measures for binary classifiers. Reading about the AUC-ROC score I came across the article Measuring classifier performance: a coherent alternative to the area under the ...
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How to prepare the training data for Support Vector Machine?

I'm currently doing some comparison of Naive Bayes Algorithm and Support Vector Machine classifying news to see each algorithm's accuracy. I already know how to prepare the training data for Naive ...
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44 views

Do imbalanced categorical predictors do any harm when classifying?

Assume I want to do some, say, churn analysis on a dataset. Decision Trees, for instance, are relatively robust to skewed distributions in the (numerical) features, but rather poor on imbalanced ...
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70 views

What is a better way to inference on chained machine learning models?

I'm trying to figure out a better way to make inference for my audio gender classification models. I have created 2 models: (1) model1 predicts adult/child, (2) <...
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Literary author classification from books content

Some background: I'm pretty much a newbie in NLP and in machine learning in general, I'm currently following some courses in my university about these topics. I'm working on my first ml project using ...
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36 views

Two definitions of logistic loss function?

During my studies I have come across seemingly two different definitions of the logistic loss function. Please see the pictures attached. What do I make of these two different definitions? Which ...
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99 views

What is the error for ImageNet “Object localization” challenge?

I have been reading some papers which use the ImageNet-LOC (ImageNet-Localization) dataset. I tried to read up on it to understand what the goal of this dataset is, and hence, what the error we are ...
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33 views

Similarity between clusters/groups?

I have a dataset consisting of multiple groups in a high dimensional space. An example is shown below: What would be the best way to calculate similarities between groups. Say how similar is group A ...
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69 views

Is better to use a multiclass classifier or a set of binary classifiers?

I have to build a general method to perform multiclass classification. The number of class in the target variable is not fixed (it is probably in a range between 3 and 10). I would like to know if it ...
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39 views

Classification ML for sports betting

I'm trying to model the problem on my own and I just want to have feedback if I'm on the right track. Suppose I want to build a model that outputs the decision rule for the outcomes of a football (...
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69 views

How do I change my neural network from a classification task to a regression task?

I currently have a neural network that is doing a reasonable job in classifying an image into a number of classes although not that great. These classes however are essentially buckets on a floating ...
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293 views

Binary Classification Propensity Scoring: High Accuracy in Train/Validation/Test, but Low Accuracy on Production Data

Before I describe my problem, if anyone has material for propensity scoring I would love it if you posted some. I've done a lot of research on propensity, but I think I've bled myself dry on the net. ...
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124 views

Explanation(s) for unimodal distribution of prediction probability computed by Random Forest

I have a typical binary classification problem with a sample of ~700 instances where I fitted multiple classification models including logistic regression, SVM and Random Forest. The instances are ...
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1answer
27 views

Can I learn binary classification and linear regression in the same network?

I would like to train a neural network on an input signal, and have it learn several unrelated decisions simultaneously (performing binary classification, one-class classification, and linear ...
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1answer
49 views

Is the following considered an image classification or an object detection problem?

I've been assigned with the task of creating a model to detect whether and advertisement exists in an image and optionally to draw a bounding box around it. My first thought was that this is an ...
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51 views

Binary classification where I know only one candidate can be positive

I have a binary classification problem, where given a thing I need to determine whether it's of class A or class B. Now, I also have additional information: For each 30 examples for which I need to ...
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1answer
406 views

Semantic Segmentation Multi-Class Single Channel Output Math

For semantic segmentation problems, I understand that it's a pixel-wise classification problem. At the last layer of the neural network, I would basically have a 1x1x1 convolution layer with a softmax ...
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87 views

Gaussian Process for Classification: How to do predictions using MCMC methods

Problem I was reading about Gaussian Processes for regression in the "Gaussian Processes for Classification" textbook and in a few other online resources. Everywhere I look people seem to avoid ...
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208 views

How to choose operation point from precision recall curves for multi-label classification

Is there a commonly accepted method for selecting an operating point for a multilabel classifier to optimize for each of these aggregate metrics: micro averaged recall at some minimal acceptable ...
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1answer
300 views

how to avoid overfittig with xgboost and how to increase accuracy

I am doing a binary classification problem, I got to train 85% accuracy, but test accuracy is 72%, I tried different parameters, Cross valid, But overfitting doesn't change, please help me on how to ...
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80 views

Why does training performance suffer when scaling a feature in logistic regression without regularization?

I am training a model using scikit-learn via logistic regression with no regularization applied. Scaling one of the features by factor 10^6 negatively impacts training performance. To my ...
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2answers
128 views

Why does linear non-logistic regression work as a linear classifier? What classification error does it minimize?

Suppose the data has two attributes and a label -1 or 1. So, we have a three-column matrix $X$ (two attributes and a column of ones for convenience of working with matrix notation) and a column vector ...
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2answers
2k views

F1-Score in a multilabel classification paper: is macro, weighted or micro F1-used?

I read this paper on a multilabel classification task. The authors evaluate their models on F1-Score but the do not mention if this is the macro, micro or weighted F1-Score. They only mention: We ...
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398 views

Softmax + CE vs Sigmoid + BCE for batched training with negative sampling, for training similarity properties

This is a follow up to this question Machine Learning: Should I use a categorical cross entropy or binary cross entropy loss for binary predictions? I am training cos similarity properties for ...
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59 views

Combining conditional classifier probabilities

Context I have several document classifiers trying to predict the correct document type for a document. For a given file, each classifier outputs a list of the probabilities of each document type. I'...
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1answer
65 views

How to model the correlation between predictions in classification problems

In most of deep learning classification problem settings, the predictions $p(y|x)$ are often modeled to be independent, namely, $$p(y|x) = \prod_{i=1}^{N} p(y_i|x_i)$$ However, this assumption may ...
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521 views

RandomizedSearchCV - worse accuracy than standard parameters

I am currently training a text classification model to infer product category (198 different ones) from product names. After evaluating a few models I have decided to stick with a Random Forest (...
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108 views

How can I leverage a pairwise scatter plot to help choose a machine learning model/classifier to distinguish the categories of iris flower?

This is a visualization of the Iris data as a pairwise scatter plot. The goal is to learn to distinguish three different kinds of iris flower, called setosa, versicolor and virginica. We can extract ...
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Variable selection, variable reduction, and handling sparsity for binary text classification

I am trying to do a binary text classification using support vector machine. I am wondering if I am doing it right and I'd like to look for some answers to the questions in mind. The following ...

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