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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Without encoding, how can we solve high cardinality issue?

I already referred the posts here but this question is different. I don't wish to use categorical encoding. details given below I have a dataset of 3000 unique customers purchase data. The dataset ...
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Using ML model and decision tree to create a new risk classification

The idea of this project was to use a Machine Learning model to find the best variables to include in a decision tree algorithm. After evaluating with caret a number of different models I found the ...
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Help with terminology & methodology for a hierarchical (& imbalanced) classification problem

I have a dataset that I am not sure how to analyze, or at least I am not sure of the terms to read up on. I have 25 groups. Each group belongs to one of 3 locations. Each group consists of multiple ...
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Image classification using Binary Cross Entropy but with only training examples for one of the classes e.g. class 1 VS anything else

I am training a 'specialist network' to reconstruct images of an object using a Variational Autoencoder (VAE). The training set (~15000 images) is of a single object in multiple poses. I also want ...
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Does Class Imbalance in Audio Classification Occur When There's an Imbalance in Mean Audio Durations or in Number of Instances of the Classes?

I'm currently working on a speech emotion recognition task, and I was curious to know whether my dataset is imbalanced or not. The value counts for each class are roughly uniform except for one class ...
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How to freeze the slope for the logistic regression decision boundary? [closed]

I built a simple binary classifier using logistic regression and then plot the decision boundary: ...
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classifying into 3 groups and measure distribution differences

I'm meeting an issue that I've tried to describe as clearly as possible though I'm sure it would benefit to be reframed in statistics wording, especially the title. Would be grateful to get some ...
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Interpreting Shapley Values on Breast Cancer

I was analyzing Shapley Values on the Wisconsin breast cancer data set (binary classification). I applied it on Random Forest and on Ridge and Lasso Regression. However the summary plot seems to be ...
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Match number of positives in unbalanced data set

I am dealing with a very unbalanced binary classification problem: 1% positives, 99% negatives. Training set is around 10 million rows, 40 columns. I choose the decision threshold (cutoff) on the ...
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Binary Classification Problem with Predicted Probabilities distribution skewed

I have a balancedrandomforest model which was trained on unbalanced data (92/8) for a binary classification problem. The AUC is around 0.98 and the precision and recall are also acceptable being 0.89 ...
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Density estimation on labelled data

I'm looking for a nonparametric density estimation for a particular classification. Let A={2,3,5,7,10,13} And Dens be the density of A Dens={x: x lies within the density region of A} , based on a ...
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AUROC or AUPRC? [duplicate]

I have a highly imbalanced dataset and want to evaluate the performance of a score. Is it better to balance the data and calculate the area under the receiver operating characteristic or to use the ...
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Best way to assign row of data to a group based on the values of other known groups?

I have a few hundred rows of data that includes the following columns One of 2 species’ names, or no name if the species is unknown (Unknown species have to be one of the 2 known species, they just ...
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Implementing kernel alignment for SVM algorithms

I am trying to understand and re-implement the results from Table 2 in the first Kernel-Target Alignment paper. The task that is being done is a simple classification task using an SVM with RBF ...
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Comparison of multi-output binary classification tasks versus separate network for each of the binary tasks in terms of accuracy/AUC

If I have 5 separate binary classifiers that are using a pre-trained Inception V3 each separately would it provide less accurate results if I modify an inception V3 to create multi-output results as ...
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SMOTE parameters optimization problem

I have a date set with 3 imbalanced groups: 10%, 3%, and 88%. I am using the SMOTE algorithm (in the R SMOTE family package) to up-scale the 2 minority groups. I did this twice: dup_size = 3 and 6 ...
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Expert-model and machine learning hybrid approach

Is there a recommended approach how the results of an rule-based expert system can be combined with an ML model? Let's say you have a text classification problem with 100 classes. For some classes you ...
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How to deal with unknown classes with a convolution neural network classifier?

I'm quite new into the DL and ML field. I'm training a CNN able to classify 3 different classes, however I would like in the testing phase to make the CNN able to not misclassify images that do not ...
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Post analysis using raw data or SHAP values in Machine learning

Let's say I have SHAP value returned in dataframe for input variables like below ...
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Usefulness of KS tests and other similar distribution comparing tests

I am working on a machine learning binary classification problem. I have an outcome variable status called as loan paid and <...
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Why PR score is down when balanced accuracy is good?

I just read this discussion here and here. I have a dataset of 977 records where class proportion is 77:23. My balanced accuracy is 75.5, ...
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What methods can I used to assign cases to groups when some variables are dependant on others?

I have a sample size of 42 cases, with about 5 variables for each case. Most of these variables are measurements, and I want to assign the cases into 2 groups (condition present, and condition absent) ...
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Optimize classification rule in multinomial logistic regression

We know that in the case of logistic regression, a classification threshold p=0.5 is generally not an optimal choice when seeking to optimise sensitivity and specificity. This is generally due to the ...
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Why variable representation plays a role in prediction?

I am working on binary classification using a random forest, where the data have 977 records and 6 columns. The class ratio is 77:23. I have two derived input variables. One variable is called ...
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Precision vs Recall Tradeoff plots 2 separated lines

I'm trying to build a binary classifier with high recall and slightly better precision so as to avoid a lot of False Positives. So far the best scores I have got from all different types of model ...
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Is it normal for simple logistic regression to significantly outperform any other statistical ML algorithm?

I'm working on a simple classification project with an imbalanced (minority-to-majority-ratio ~ 0.2) dataset that has ~4000 rows and ~200 features. I noticed that, for my dataset, a simple logistic ...
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How do I perform a train-validation split on data with class imbalance such that the class imbalance ratio is preserved?

My data has class imbalance-- that is, some classes have significantly fewer training samples than the others. I want to perform a train-validation split in such as way that the class ratios are ...
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Risk score uncertainty quantification

I am working on various risk score estimation problems. I assume individual subjects are associated with a true risk $$ r_i = f(x_i) + \varepsilon_i$$ where $x_i$ is some available information about ...
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High Performance Classification or Similarity Algorithim for Mixed Data Types?

I have a database holding 10-ish features that describe different breeds of dogs. They are mostly categorical features, but some provide ranges for values. Here's a demo representation of the database,...
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If the AUC score is 100 percent can F1 value be 99.94 percent?

If the AUC score is 100 percent can the F1 value be 99.94 percent? I would expect 100 percent, too.
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Churn model- how to handle new users without enough historic data?

I'm making a churn model. My observation window (historic data) length is 3 weeks. There are some users that are not been registered to the app that I'm analyzing for three weeks, and as a result, I ...
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Handle User Behavior Change when creating a ML classifer

I'm creating a churn model. My first thought was that the bigger the training set, it would be better. However, 2020 was a crazy year because the COVID 19. For example, a user who was sick and ...
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Can I convert a classification problem with an ordinal dependent variable in a regression problem?

It looks interesting to me to know about the variables related to the students performance, so I started to look into the following dataset: https://archive.ics.uci.edu/ml/datasets/Higher+Education+...
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Clustering while knowing the ground truth: Why would someone choose this approach?

If the ground truth of the class/cluster/segment that our observations belong to, is known in advance, why would someone choose to perform clustering instead of classification? In fact, doesn't the ...
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4 votes
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Why exactly does a classifier need the same prevalence in the train and test sets?

Here are some commonly seen statements about the importance of prevalence in the train and test sets when developing a classifier: "Another reason not to rebalance datasets is that models should ...
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Possible ways to feed two variables containing text data into an ML model in an NLP problem

In a natural language processing (NLP) problem, we have a couple of variables, say A and B. A denotes a phrase (1-2 words) and B denotes another phrase (>3 words). There is one target variable that ...
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Comparing impact of training data size - what testing data size?

I am training a classifier using BERT and want to check how the accuracy changes with increasing training data size. Up until now, I have 1k annotated training samples and tested the accuracy for ...
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class weighted classification

I am working on my multi-class classification project and I have a question: I have three classes in proportion: 50%, 47% and 3%. I decided to use class_weight="balanced" parameter in random ...
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GPT-2: Why should text classification work on the last output embedding?

When GPT-2 is fine-tuned for text classification (positive vs. negative), the head of the model is a linear layer that takes the LAST output embedding and outputs 2 class logits. I still can't grasp ...
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Why is a random forest regressor better than a random forest classifier when predicting a category?

I am building a model that recommends the optimal golf club based on data I have gathered. Since the model prediction should be a category, ie. a golf club, I would assume I would have to use a ...
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Fuzzy membership - Kernel SVM in R

I'm trying to perform a binary classification task using SVM with radial basis kernel in R and I want to assign fuzzy memberships to the datapoints. Already existing function in R packages such as ...
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Windowing timeseries classification data

INTRODUCTION: So basically, I have a dataset with 6 columns and around 10k rows. The output column is a label corresponding to every row, with the labels being 0 and 1. The dataset is timeseries based....
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What to do with 99% F1 score in binary classification?

I've been handed a binary classification model to look after. The model uses the F1 score for comparison purposes. The challenge is that the F1 score against the test dataset is very high, like 99%, ...
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Does it make sense to apply Bayesian formula on top on a classification problem output?

In classification tasks we normally get a set of numbers that represent a probability distribution - they sum to 1. For further discussion, suppose we only have two classes: ...
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Are machine learning algorithms flexible enough to learn changing feature importances?

I have a prediction problem where each row/entity contains data over a range of time and features can change in importance over time even for a single entity. I am wondering if machine learning models ...
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how to deal with data leakage in historical data

I have a dataset containing matches from 2000 TO 2018 and I am asked to predict match outcomes for the year 2017 to avoid data leakage I am going to just train my model from 2000 to 2016. in the ...
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2 votes
1 answer
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Why is cross entropy loss better than MSE for multi-class classification? [duplicate]

I know there's a lot of material on this, but I'm still struggling to find a scenario where cross-entropy loss is better than MSE loss for a multi-class classification problem. For example, if we have ...
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Biased logistic regression in pytorch

My model has decently high AUC=90%, but is biased, underestimating the probability $y=1$. This is systematic across some of the input features as well. How can I nudge the bias term, or otherwise ...
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3 votes
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Features are Relevant for Regression but not necessarily for Classification - what to make of this?

I have used the R Boruta package to check for feature relevance in predicting log returns of financial time series, the targets being the log returns themselves (for regression) and the sign of log ...
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Meaning of the sample variance computed from a k-fold CV

Let's consider a k-fold cross-validation to estimate the generalization error of a model. I would like to clarify the relationship between the following quantities: the variance of the CV estimator ...
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