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Share Your Experience: Take the 2024 Developer Survey
5 votes
Accepted

Is it possible that Precision and Recall increase together?

They can increase together if your new classifier is indeed way better than your older one in terms of almost every metric you can imagine including the two scores, together with the F1-score, or even ...
cinch's user avatar
  • 2,277
3 votes
Accepted

Node classification with random labels for GNNs

The Cora dataset is unbalanced (s. here). It's graph consists of 2708 nodes and the label distribution (for labels 1 to 7) is 818, 426, 418, 351, 298, 217, 180, i.e....
Chillston's user avatar
  • 1,748
3 votes

is there a mathematical explanation of precision and recall tradeoff?

The tradeoff between precision and recall occurs because increasing the threshold for classification will result in fewer false positives (increasing precision) but also more false negatives (...
lkahtz's user avatar
  • 131
2 votes
Accepted

is there a mathematical explanation of precision and recall tradeoff?

Your mentioned so called precision and recall tradeoff is a statistical tendency on average, not a mathematical necessity which can be easily shown in below example. Let $TP=10, FP=5, FN=5$ for ...
cinch's user avatar
  • 2,277
2 votes

Temporally Non-Aware RNN

Listen, this is not an answer to your question, but it seems that you are missing the whole point of convolution. Simplified explanation: Convolution is just a weighted sum of the neighbors of a pixel ...
Alberto's user avatar
  • 2,248
2 votes

Noob crafting a simple "Zero-Shot Classifier" Using an API . How can I avoid passing the categories every single request?

There's been a couple ideas thrown around wrt the task of "prompt compression". For example, the gpttrim algorithm, applies some basic NLP preprocessing to reduce unnecessary tokens (e.g., ...
Alexander Wan's user avatar
2 votes
Accepted

What number classes makes a classification problem continuous

What number classes makes a classification problem continuous There is no such number. First you should figure out whether your core problem is regression or classification (or in some cases ...
Neil Slater's user avatar
  • 32.5k
2 votes
Accepted

Is it possible training accuracy never changed while training?

What happened with your model is that it suffered from a Neural Network collapse. This means that your network didn't learn to generalize with the data or that the local minimum found in the gradient ...
Cesar Ruiz's user avatar
2 votes

Is it possible that Precision and Recall increase together?

Often references to the precision-recall trade-off are discussing setting the classification threshold for a probabilistic classifier. A probabilistic classifier is one that returns a probability of ...
Lynn's user avatar
  • 141
2 votes
Accepted

Why would balancing be so helpful when the imbalance is minimal?

I would agree that tha observed results are unexpected. For such a radical change I would actually suspect a bug/omission in data processing for the imbalanced data set. It is not necessarily the ...
Neil Slater's user avatar
  • 32.5k
2 votes
Accepted

Is there an algorithm capable of telling knots and links apart?

This answer assumes that knots are one continuous path and links are multiple linked ;) paths. The part that makes your examples tricky is that the images are 2D representations of a 3D thing, and &...
bogovicj's user avatar
  • 301
1 vote
Accepted

Does a random forest classifier always get 100% accuracy on its own training data?

No, random forest is not guaranteed to get 100% accuracy on the test data. But it also doesn’t mean it’s overfit because relatively high scores on test data are common with random forests. The trees ...
foreverska's user avatar
  • 1,288
1 vote

How to incorporate the probability threshold for binary classification into scikit-learn GridSearchCV?

I don't think that tuning the probability threshold in a binary classification model can help improving the model accuracy. That said, if your best model is "bad" in the sense that it doesn'...
c p's user avatar
  • 111
1 vote

When are Transformers better than LSTMs in time-series tasks such as classification?

Both the architecture have some use cases based on their architecture. Transformer Transformers are good when there are long-range dependencies, in those cases they outperform the LSTMs. Main key ...
Hiren Namera's user avatar
1 vote

Is it possible that Precision and Recall increase together?

PR curves need not be monotonic. Suppose some model, such as a logistic regression, makes predictions p below and that the true outcomes are ...
Dave's user avatar
  • 618
1 vote

How to do image classification with optional metadata?

Train a self-supervised model to generate embeddings of your metadata Train a model to generate embeddings of your images (or use/fine-tune a pretrained image model) Train a prediction model that ...
Karl's user avatar
  • 206
1 vote
Accepted

How to do image classification with optional metadata?

You could use your existing CNN architecture and concatenate the metadata with the flattened last convolutional layer. Add a flag Boolean feature for whether the metadata is present, and if it is not, ...
Neil Slater's user avatar
  • 32.5k
1 vote

What is wrong in reasoning here in classification for defect detection?

Alpha represents the significance level, or the probability that you will make a Type I error by rejecting the null hypothesis when it is actually true - in other words, the probability that you're ...
Nuclear Hoagie's user avatar

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