Questions tagged [classification]
For questions related to the placement of individual cases into categories, such as is essential in fraud detection, spam detection, quality control, prediction of user or market responses, automated organizing or indexing, assigning objects in view to types of obstacles or risks, writing or typing recognition, phonic recognition, .
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When are Transformers better than LSTMs in time-series tasks such as classification?
I’m working on a time-series classification problem and trying to decide whether to use a Transformer or an LSTM.
From what I’ve learned, Transformers are better suited for capturing long-range ...
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Is it possible that Precision and Recall increase together?
Usually, it is said in ML that there is a trade-off between Precision and Recall.
I wonder if it is possible that Precision and Recall can increase together?
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is there a mathematical explanation of precision and recall tradeoff?
Is there a mathematical explanation of precision and recall tradeoff? Ie, is it possible
mathematically to see that as you increase one, the other decreases?
$\text{Precision}=\frac{TP}{P^*}=\frac{TP}{...
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Meaning of "error on the test point x" in optimal classifier for binary classification
Let f(x) be optimal classifier for binary classification where output is modelled noisy.
What does it mean "f(x) makes a mistake only if there is an error on the test point x"? Basically,
...
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Can mAP(mean average precision) use at classification problem?
I have some questions about mAP(mean Average Precision).
In object detection problem, mAP use to measure performance broadly.
But, Is it possible to use mAP in multi-class classification problem?
I ...
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Why my deep learning model (FCNN/ 1DCNN) fails to learn when training on medical dataset?
I am working on a project to predict the severity of the disease, Hemophilia using a deep learning model(FCNN or 1DCNN). I am working based on the information provided in this article: https://www....
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Why is the Hinge Loss defined this way?
I have a question regarding the Hinge Loss function used for classifiers and in general the "max-margin" types of classifiers, it is defined as $$max(0,1-t*y)$$ where $t$ is the intended ...
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How to create meaningful features that allow unsupervised image classification?
I would train features that can later be correlated with categories, once I have some examples for the categories. Let's say one has a set of training images sorted by artist, and wants to create some ...
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Classes definition for detecting impervious surfaces on aerial photographies
My project is to use deep learning, essentially a UNET segmentation model, to detect impervious surfaces on high resolution aerial photographies.
I wonder if it's better to train the model with many ...
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What number classes makes a classification problem continuous
I am working on a classification problem, where I have sequences of images and I want to train a model to predict the index of the image with some wanted property.
The target classes would obviously ...
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Simultaneous forecasting and classification
I'm working on a project where I need to perform both forecasting (regression) and classification using time series data. The dataset is labelled.
I've been exploring LSTM networks due to their ...
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Unclear points in scaled Euclidean distance
The following is from a machine learning book.
I did not understand the explanation given in the figure caption. Could some expert make it clear? Why is the stretching class-dependent for the center ...
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Is it possible training accuracy never changed while training?
Question summary
What informations can get from this epoch_accuracy graph?
Is it possible training accuracy never changed like after 10 epoch in graph while training?
Body
I do some experiments with ...
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Does the order of iteration affect the answer returned by FIND-S?
This paragraph is from the book Machine Learning by Tom M.Mitchell (Page 26):
Initialize $h$ to the most specific hypothesis in $H$
For each positive training instance $x$
$\;\;\;\;\;\;$.For each ...
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Predicting next 2D location from sparse 2D inputs which are received sequentially
Problem: You toss a coin on a 2D table with known dimension. There are certain regions on the table where the probability of get heads is high. At the maximum you can toss N=20 times at an arbitrary ...
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How to do image classification with optional metadata?
I have a vanilla image classification problem. The image may optionally have some numerical metadata associated with it. We don't assume uniform availability of this metadata, i.e., the model should ...
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Search recall optimization - what appropriate loss function to use?
I am studying machine learning and wanted to work on a project of my own so that I have better chances after graduating college. I'm studying the application of ML to improve searches using a toy ...
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Approaches for multi-label classification with over 1,000,000 labels
I have billions of rows in some dataset and each row can be in any subset of about 1 million binary labels. So the number of overall classes would be $\sim 2^{1,000,000}$, if I were to think about it ...
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What is wrong in reasoning here in classification for defect detection?
Consider the following hypotheses:
$H_0$: a given coin is fair
$H_1$: a given coin is unfair
Let
$\alpha$ = P(Classify as $H_1$|Sample actually from $H_0$)
We know the statistics for a fair coin, ...
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ROC curve for multiclassification - results sound not correct
I'm working on a multiclassification task using LSTM algorithm, i generated my roc curve plots but they give scores like 1 , 0.99, 0.97 however i have an accuracy of 0.97, Precision 0.65, Sensitivity/...
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Implement perceptron given decision boundary function
Suppose we have a linear classifier for the classes ω1 and ω2 with characteristics vectors
Xa=[a a]^T and
Xb=[-a -a]^T
correspondingly. Also suppose that the decision boundary that is defined by ...
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Temporally Non-Aware RNN
I am trying to classify whether or not a specific object is in panoramic photos. The issue is, a panoramic photo can be any width, so the input to my neural network can't be fixed in that dimension.
I'...
user55176
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Linear Discriminant Analysis on a transformed space
Let $S$ be a finite subset of a $\mathbb{R}^k$ partitioned into $N$ subsets $S_1, \ldots, S_N$ and let $n_j = |S_j|$. The between-groups sum of squares of the partition is defined as
$$bSS(S_1,\ldots, ...
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Noob crafting a simple "Zero-Shot Classifier" Using an API . How can I avoid passing the categories every single request? [closed]
I have a collection of 700 categories, all potential classifications for articles. My current need is to create a system that can dynamically categorize short texts or articles according to these 700 ...
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Handling Feature Selection Discrepancy in Image Classification Model
I have developed an image classification model that categorizes images into two classes (we'll say good and bad for the sake of example) based on a set of tags. To improve the model's performance, I ...
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1
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Effect of large activations of hidden layers
The example is trying to predict wether coffe is well roasted or badly. 1 is good roasted and 0 is bad. The architecture is:
Now I try to visualize the model.
Unit 1 has higher values when the ...
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What is the main difference between class label prediction through Machine Learning and Conditional Label generation that can be done on raw data?
I have understood that machine learning helps us to classify various instances using the labeled data, but why do we need machine learning when we obtain the same results through generating labels by ...
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What are the most common fault prediction algorithms?
I have to predict a fault (automotive related) as much in advance as possible.
Right now I have found a solution that is somewhat satisfactory (a good number of true positives and a low number of ...
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Early binary classification of timeseries
I'm trying to figure out how to solve this problem that I'll try to explain in the next few lines. I have a timeseries of length ~200k values and every 700 points I have a label that indicates the ...
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Loss is negative- DQN with BCE Loss function
I am writing a code with DQN, using BCE as a loss function for the classification of a sequential time series. But while training, the loss value goes in negative. Also, accuracy and binary accuracy ...
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Node classification with random labels for GNNs
I decided to train GCN on the Cora dataset for the node classification task, however, with the random labels, i.e., applying np.random.shuffle(labels). For the ...
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Classify sequence of flags
I am not able to find an answer to how I should classify a varying number of sequence of binary flags + other features. My data looks like this (these are events, so the order is important and I may ...
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How do I choose a good treshold for classification (using cosine similarity scores)?
I am using openai's text-embedding-ada-002 embeddings model to do a semantic search on a database of articles to find articles that are most related to a given ...
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Should I downsample because of overrepresentation of geographic locations in time series data
I am in the start of working with a project where I am hoping to be able to classify activities based time series data. I have historic data; lat/long/speed/(..) as well as the activity.
The challenge ...
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How does a Machine Learning model predict this classification problem?
Let’s imagine we want to create a simple Sentiment Analysis model using Machine Learning not Deep Learning algorithms, so we need to have a set of handcrafted features for this classification problem.
...
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Can AI be used for submarine detection?
Recently a lot of work has been done in connection to using AI in the search for extraterestrial life, the SETI project.
Could the same methodology be used by the military, in order to detect ...
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Is it feasible to perform facial recognition on hundreds of thousands of individuals?
I came across a video with the title "you can buy things with your face in China". in the video, a woman scanned her face into a vending machine to buy a drink with only her face and without ...
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Do deep ensembles and regular ensembles coincide for classification tasks?
The deep ensemble paper https://arxiv.org/pdf/1612.01474.pdf introduces proper scoring rules for ensembles of NNs. Turns out that the likelihood is always a proper scoring rule.
For regression tasks, ...
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How to approach a toy classification problem using a neural network?
The toy problem:
50 unique numbers are randomly selected from number 0 to 99.
If number 1 appears in the selection but number 2 doesn't, the selection is labelled as "1".
If number 2 ...
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Using MSE or RMSE instead of CrossEntropy in Question Answering NLP problems. What are the problems if we used?
When you predict Start Index, end Index in Question Answering NLP task (SQUAD Data), you use CrossEntropy as a loss function. ...
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How can I not only classify an intent, but also identify slots and values in it?
I've been working on text -> intent -> command execution for a particular application and while I've found many papers and code that work well for intent classification (1, 2, etc.), they stop ...
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Large Language Models vs Tabular Data
Problem:
Let's say we want to predict insurance frauds. Whenever we obtain an insurance claim, we are provided with a free-form description detailing the loss and a substantial amount of data on the ...
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Which loss / activation function with 2 classes that do not occur often and do not sum to one?
I have a neural network that predicts 2 classes of a time series (bottom and top). Currenlty my Y labels are size 2: [1 0] for bottom and [0 1] for top. The NN has 2 output nodes.
Of course not every ...
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Does splitting the classes in my dataset into sub classes improve classification accuracy?
My problem is basically classifying ok / not ok. But I do have additional information on the error cases for the "not ok" class. Should I just train on the classes that I need for my output, ...
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What reinforcement learning algorithm should I use for the following problem?
Environment
I have a static timeseries environment meaning the environment is the same.
This problem is a multi armed bandit problem.
Time
t0
t1
t2
State
s0
s1
s2
Score
10
0.1
0.2
Class
1
0
0
...
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Training a neural network to produce a one-hot encoding vector out of a single feature
I would like to build a neural network that takes a natural number and generates a one-hot encoding vector corresponding to that number.
Example: $2 \rightarrow (0,0,1,0,\dots)$
More formally, I ...
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Best feature engineering approach for interest-based age classification
I have a dataset which has users (rows) with the list of their interests (IABs), which looks like this
...
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Higher accuracy in the test set than in the training set
Hi I'm trying to train an ANN model to classify images containing these characters: 0,1,2,3,4,T,X,S
eg.
etc...
so something like the classification of records of the MNIST dataset but using my ...
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predict a one-hot vector which sum is 2 (so not really a one-hot vector)
So I basically have a $n$ classes. I have an input. My data is organised in the following way: each input has a label, this label is 2 classes. It can be twice the same class, or two different classes....
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How does FaceNet (or similar) bootstrap new faces?
In a metric learning system the system can be trained on known examples such that common classes (faces) are clustered together and separated from each other as much as possible. If triplet loss is ...
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