Questions tagged [machine-learning]

Machine Learning is a subfield of computer science that draws on elements from algorithmic analysis, computational statistics, mathematics, optimization, etc. It is mainly concerned with the use of data to construct models that have high predictive/forecasting ability. Topics include modeling building, applications, theory, etc.

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Line items matching with PO

How can I match two products from an unstructured invoice (in .pdf format) with the PO (purchase order)? How to deal with the cases when there are differences between what's in the invoice and what's ...
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How to find the best optimized combination on generalized assignment problem and multi-dimensional knapsack problem?

Let say that company [X] has these following features product: A, B, C total target production a year: 100 target for each product: A: 30 B: 30 C: 40 total employees: 10 To achieve the target, ...
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In Gradient descent, Why the gradient of cost function do not have to be normalized into unit vector

From my background, I understand that the purpose of having a learning rate (α) is to normalize the magnitude of gradient (▽J), so the step size can properly converge the local minima Since α is ...
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Newbie in ML - Using error traces to predict issues

We have trace data from Jaeger which shows end-to-end information about requests/transactions/error codes. Jaeger UI/APIs are useful in debugging issues after they have happened. The requirement is to ...
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Why is a neural network not doing better than multivariate linear regressions?

I am making neural networks of multiple targets, all using same training data. For some of these targets, multivariate linear regressions do a very good job, i.e. a strong linear relation exists ...
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How to fine tune n_components parameter in UMAP?

I am using UMAP for clustering. However I can't find any information about methods to fine tune n_components parameter (which is very important). As good as I understand I can't use explained variance ...
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Train a unique model over multiple time series

I'm currently working in a project involving time series. I have nearly 100 univariate time series (representing the performance of an engine of cars between 2018 and 2022). My goal is to forecast the ...
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SciKitLearn - Powerlifting Placing Predictor Recommended Models?

I am new to data science and working on a project utilizing the openpowerlifting database to create a machine learning model to predict what someone would place in a local powerlifting competition, ...
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How to test the confidence for a rule based system?

I have a multi-class dataset and I generated based on it rules. That is, if certain features are seen then it must be a certain class. I chose only rules with precision 1 (with respect to the whole ...
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Feature engineering for DateTime column

I am trying to train a Model that predicts the solar power generation of my roof. This is my current dataset: https://pastebin.com/gtZcGi2m. It is built using some weather api and the actual power ...
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Why is ROC-AUC usually shown in GNN papers

In various graph neural network (GNNs) papers, the ROC-AUC metric is usually shown alone without considering F1 or Accuracy. Is there a reason for that? What does it say about two models 1 and 2 with ...
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MLOps for beginner

I am 1 year old in ML and have been using jupyter notebook to build static models all these days, do some analysis and present my results to the bosses as it was all POC. Now, we would like to scale ...
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How to understand to what maximum size you can reduce the dimension of data and avoid the curse of the dimensionality?

i have a question, maybe someone could help me. I use t-sne (also tried umap) to reduce the dimensionality of the text embeddings dataset (size of embedding 300). after that I will cluster using ...
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Can I use different dataset when performing model stacking?

Let's say I want to detect new species of fish. I have several models, each trained to recognize a different characteristic, e.g., the speed of known fish, the size of known fish, their known shapes, ...
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Outputs from models (trained on different data) as inputs to another model?

Let's say I want to detect new species of fish. I have several models, each trained to recognize a different characteristic, e.g., the speed of known fish, the size of known fish, their known shapes, ...
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Score ties in decision trees and ensemble trees

While implementing decision trees and ensemble of trees I found two places where a decision must be made and we can have potentially ties: for ensemble trees we can vote multiple weak learner ...
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What does the log-likelihood say and how is it to be interpreted?

Intro I am new to AI and I started with modeling a bayesian network for my AI agent. I learned the parameters using the EM algorithm. Besides the computed conditional probabilities distributions, the ...
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Best Neural Networks for Models with If Statements

If I am trying to use a neural network to learn/improve/replace part of an existing computer model, and that has if statements in it which seemingly make it more difficult to predict than other parts ...
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Relation between batch size, number of steps, and learning rate

Taking alphazero training setup as a reference: 700k total steps batch-size of 4096 initial LR of 0.2 What would be an equivalent setup for a batch-size of 1024? ...
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How to reduce the size of Bert model(checkpoint/model_state.bin) using pytorch

I used torch.quantization.quantize_dynamic to reduce the model size but it is reducing my prediction Accuracy score. I'm using that model file inside the Flask and ...
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How do machine learning applications communicate with one another?

My understanding is that if a company creates a machine learning application, they use an API to ensure that applications talk to one another, which is what I found in my initial research (https://www....
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How to handle tags/lists with CatBoost?

I have database like this: Id, type, category1, category2, tags 1, ‘cosmetics’, 123, 456, [446, 354] 2, ‘electronics’, 234, 213, [55, 978, 12] … And I want to predict some value with ...
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Selecting Right data and amount of data

Multiple Data Sources are needed to make Credit or Fraud Decision on (loan/credit card) applications. But each data pull costs money to buy. So how do we optimize a) Number of data sources to pull ...
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How to avoid numerous Hyperparameter tuning in ML?

Suppose I have developed a dynamic system for forecasting the future of some specific stocks. As time passes, the train set will change dynamically. For a better understanding, consider this example: ...
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Grid Searching seed in randomized machine learning

I was wondering if tuning a seed with cross-validation in order to maximize the performance of an algorithm heavily based on a randomness factor is a good idea or not. I have created an Extra Tree ...
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How can I implement lambda-mart with lightgbm?

I have a learning to rank task at hand and I want to use the lightgbm implementation of LambdaMART. I'm also following this notebook. ...
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Find closest item from ALS model using KNN

I have a dataset like: ...
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How to Scale target feature

How should I scale target feature? Should I use scaler as fit_transform on y_train, and just fit on y_test to avoid leaking data?
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Newly discovered learning rule

Does anyone know how this algorithm performs the learning process for neural networks? I've stumbled over this solution. It works, but I don't know how and why. It's neuron-local and works without ...
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On-Device Football Detection Model not performing up to the par ; misdetections

I have trained a football detection model. I have so far trained the models using RCNN, SSD (backbone MobileNet), CenterNet and others. SSD and Centernet, so far have been the best in terms of speed ...
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How to exercise Quality Assurance Engineering principles to Artificial Intelligence systems?

In deterministic (software) systems we have a set of business requirements and ideally, given enough resources, such a system can be fully defined of which are the expected outputs for each inputs or ...
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Loss decreases, but Validation Loss just fluctuates

I've been trying to implement object detection using a CNN architecture like this: ...
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What what will happen if all the layers of a MLP or any DL architecture are set as same in the beginning?

Setting the initial weights as all zeros will have the output dependent on the bias and setting the weights of all the neurons of a layer as same, will update the gradients in same way thus removing ...
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Using the dnn model on data with a small amount attribute

I have made some Medical information classification model using tensorflow and keras. I make some classification model that have two input. That are the time series data[like signal data] and two of ...
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Why we do random sampling when we select the training set?

The usual workflow when building a machine learning model starts with random splitting the data set into training and test set. What I can't understand is why we do this. For example lets say we have ...
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How to do Feature clustering?

I have different datasets and I want to find out the features that are similar among the datasets. The datasets are of varying sizes. example: dataset1 has columns a,b,c,d,e dataset2 has columns m,n,o,...
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What configuration of output neurons to use for detecting bias

I am trying to make a deep learning model that detects political bias in media articles for my local community. There are two political parties here and I have a dataset of biased articles from both. ...
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MAE divided by median metric

I have a regression task for which my best models has a Mean Absolute Error (MAE) of approximately 15,000. The median value of the target variable is approximately 150,000. I want to report that the ...
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Choosing Right Optimiser and Data Scaling

The choice of optimiser and how data is scaled are both very important things in machine learning, yet they are not hyperparameters (as far as I am aware). It is also not necessarily obvious which ...
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What is the correct way to compute hit rate in recommender systems?

I'm working with the famous Movielens 1M dataset and implemented some simple recommender algorithms. While computing the hit rate, I found that it's very low $(\approx 0.008)$ but the papers seem to ...
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Different results between hyperparameter optimisation and actual training/val values

If I want to do a hyperparameter optimisation on a dataset using e.g. hyperband or random search, I note that some of the models being randomly chosen seem to have rather good R2 scores, MSE etc. I ...
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Keyword suggestion rule learner question

I have a mock exam question as the following: Q: You have a set of documents D = {di}, where each document is assigned to an arbitrary number of keywords from a fixed set of keywords. There is no ...
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Why would an affine forward layer take unflattened inputs?

I'm doing Stanford's CS 231n on my own. I'm tasked with implementing the forward function for an affine forward layer. Here's the doc comment: ...
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Mix of time-dependent and constant features for a transformer

I'm using the transformer architecture to predict future time-points from previous time-points. Each item of the input sequence is a vector of ...
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How to calculate dataset and feature sparsity/density

I have a dataset with 8 features and 30,000 samples but which is probably a sparse sampling. I would like to quantify how sparse or dense the dataset and individual features are, as described in the ...
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Are there examples of quantization aware neural networks

I'm looking for examples of Machine Learning / Neural Networks examples that work with quantized weights, activation functions,.... The simple approach of training with floating point parameters and ...
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Images get rotated during training

I am trying to train a ssd_mobilenet_v2_keras for object detection on a dataset of more or less 6000 images. The problem is that images are rotated randomly during training (or at least, this is what ...
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How to select relevant columns from a dataset with many features

I have a dataset with a large number of potential features (>100) and I am interested in finding a relatively small subset of these (maybe on the order of 5, or 20) features which is best suited to ...
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How can we determine if the model does require an image or not while predicting?

I've this sentiment analysis task, where the dataset comprises an image and a comment for prediction. I want to determine if the image is really necessary for the task or not, is there any way I can ...
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Is Proximal Gradient Descent exactly same with coordinate descent for LASSO

We have the lose function of LASSO: $$L(\theta)=F(\theta) + \lambda||\theta||,\quad F(\theta) = \sum\limits_i(y^{(i)}-x^{(i)}\cdot\theta)^2.$$ And the minimal ...
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