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Binary Classification [Text] based on Embedding Distance?

The are two levels to your question: Conceptual - Yes, you can perform an approximate nearest neighbor search on text documents that have been embedded. What you call binary classification is more ...
1 vote

How to calculate accuracy of a logistic regression?

You need a threshold value $t$ to assign a class based on the probability, so that if $p < t$ you assign it to class 0, and if $p >= t$ you assign it to class 1. Then, you can compute the ...
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Classification of sequential data

The definition of your problem is univariate timeseries classification. Univariate because there is only one variable evolving through a time-axis, whereas multivariate is the case when you have more-...
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What method should I use to see whether my categories are well-encoded by my variable?

Why don't you split your data into train, validation and test sets and then train a model to predict the category? The model could be an LSTM or a transformer encoder. It would receive the words and, ...
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1 vote
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How to present a statistical justification for the choice of models with approximate accuracies?

A widely accepted approach for your problem is using the Critical Difference Diagram. At this time, the original paper has 12274 citations (and counting). Implementations: Python; Julia; R.
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1 vote

Probability distribution of probabilities

The landscape of the output probabilities depends entirely on the training data. If the data itself is sampled from a normal distribution, then the learned probabilities will reflect that. Otherwise, ...
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Classify E-commerce URLs into predefined classes

There is not enough information for a comprehensive answer, but I will try to assist you. You can try to hard code it without using any machine learning algorithms. Try looking at specific parts, ...
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Can a recommendation system be used as a binary classifier?

Please do not build a recommendation system in order to solve a binary classification problem. If you like the idea with the ratings, you can always create a new feature that is the ranking. Consider ...
1 vote
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How to perform a classification experiment with data augmentation?

Data augmentation must be done after splitting your data into training and testing sets to avoid data leakage.
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1 vote

How to increase retention?

You can get inspiration from reading the following papers: 1, 2, 3, 4, 5 In any case, I recommend you investigate more on your data. In particular, you should be able to tell a story before employing ...
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2 votes

How to automatically classify a sentence or text based on its context?

If you don't have training data, "classification" may not be the best name. But it could be if you consider this task as like in zero-shot learning. There are some pretrained models in ...
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Applying the model on validation data achieves higher performance than on test set. Is this possible?

Yes, it is unlikely, but it is possible. If we assume that you have no mistakes in the code, this simply means that the unseen data is easier for the model to classify compared to the test data. This ...
2 votes

How to automatically classify a sentence or text based on its context?

Extending Solomon Ucko's comment, I would propose trying to label a few thousand examples and fine tune one of the available large language models, for example, the Google's T5 or BERT using your ...
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Is it possible to reverse the layers of a convolutional neural network?

Neural networks in general are not bijective. This means that inputs and outputs do not map onto each other in a 1-to-1 fashion and you cannot simply reverse an output to obtain the input it was ...
12 votes

How to automatically classify a sentence or text based on its context?

Me: Please give 2 semantic tags for the sentence "The area of a circle is pi time the radius squared" ChatGPT: 1. Mathematics. 2. Geometry I'm not sure it's a robust and scalable solution ...
9 votes
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How to automatically classify a sentence or text based on its context?

To my knowledge, there is no such library or pre-trained model. Imho there is an important issue in the task as defined in the question, more exactly in the example: these tags seem natural for a ...
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Combining results from classifiers trained on different test/train splits results in higher accuracy

A number of observations as per your use case: Please use a k-fold validation scheme, for more reliable numbers of accuracy. The model or data need some more hyper-parameter tuning since ...
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Using human created small groups to identify entirely new small groups

Welcome to the Data Science World. Note: I am not 100% sure about your project and details are not known. So I will only point you in some directions. As I understand your question, your problem is to ...
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Best way to compare classification output between different locations

Reducing 0.25 to something like 0.15 would help you find the similar predictions. Similar prediction arrays have high correlation. If you can get a set of arrays with correlation just above 0.15 (for ...
2 votes

What can be done about mislabeled data points in the training set of a binary classification model?

A good method for identifying mislabeled data is Confident Learning. It can use predictions from any trained classifier to automatically identify which data is incorrectly labeled. Since Confident ...
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Dataset with some mislabeled data (around 1%)

In case it's useful, I recently published a paper on this very topic: Identifying Incorrect Annotations in Multi-Label Classification Data TLDR: what you are doing is not ideal because your model ...
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In DBSCAN, can the distance between a Noise Point and Border Point be less than Epsilon?

Yes you are correct. I just gave an example check this image
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How to use 5 different datasets to re-train & test your ML Classification models multiple times

Your code appears correct for this project. Additionally, the performance of the model appears acceptable for the nature of the project. It is very difficult to predict stock returns based on ...

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