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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What are good configs for running UNet3DConditionModel on 8 GB VRAM? (64x64x64 inputs)
What are good configs for running UNet3DConditionModel on 8 GB VRAM? (64x64x64 inputs)
More specifically for this project I'm looking to use HuggingFace's UNet3DConditionModel on my home PC on a RTX ...
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Linear warmup always result in a significant drop in accuracy
I'm at a machine learning task where I used linear warmup for a machine learning task. However, I observed that the test accuracy always drops significantly after the warmup, sometimes even during ...
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Downsampling an image in the right way
I was not shure where to ask this question, but SE Data Science seems to be the best place for it.
So I tried to build a CNN based super resolution model. Unfortunately I have only high-res images but ...
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Resources for Integrating Good Machine Learning Techniques (CoursEra preferred)
I've plateaued as a Python programmer. I understand the ins and outs of developing:
Cross Validated ML Scripts for a variety of models (Random Forest, SVM, SVR, ...)
Multivariate tools (Lasso, ...
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How to create Radial Basis Function Network for Regression problem in python using Tensorflow (keras)
I am working on a non-linear regression problem using a dataset with over 100 sensor data columns and a single output column containing real numbers ranging from 0 to 10,000. I have implemented a ...
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Class imbalance for binary classification tasks
I am looking to train a binary classifier. Most of my experience so far has been with generative models, not classifiers, so I am wondering with respect to training data, what is a good ratio of 0 and ...
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Latest Tree-based models
What are the latest Tree-based models that are used in machine learning? Tell the new models except the old ones such as the Decision tree, Random Forest, Gradient Boosting, LightGBM, XGBoost, and ...
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Reinforcement learning with Q learning doesn't seem to be learning
I'm encountering an issue with my PyTorch-based Q-learning model. Despite implementing the reinforcement learning algorithm, the model seems to be stuck at the same balance level without showing any ...
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Multi-class Classification with Categorical and Time-series Features
I have a problem where I want to classify certain entries into clusters, based on their categorical features (such as Country, Category, as dataframe columns), and also their selling pattern (time ...
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Fixing class imbalance vs Over-detecting in test data
In my experiences, binary classifiers tend do better in terms of F1 scores when the class imbalance is at least reduced. However, this leads to over-predicting in the test data.
(Thought) Example: If ...
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How to build a recommendation system Based on user infos and without ratings?
I would like to build a recommendation system based only on user informations(age,sex,zipcode,and some quiz answers),based on those features i want to recommend assurance products, but i am confused ...
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Invalid Input shape for input tensor on Multimodal CNN
Im trying to build an image classification model with multimodality, it takes SAR and optical images, both types of images have FITS format.
The optical images have shape (None, 512, 512, 3), while ...
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How can I fit sklearn.svm.SVC with three features, given that the features are actually arrays of lengths 128, 12 and 40?
To clarify, each instance of feature_1 is a 128 item long array, each instance of feature_2 is a 12 item long array, and each instance of feature_3 is a 40 item long array. I am currently simply doing ...
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the features in cora dataset
Cora dataset contains a citation gragh which also contains feature vectors for each paper. The size of the feature vectors is 1433 (corresponding to unique words). Feature vectors contain binary ...
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Dears i run it to ValueError: Found input variables with inconsistent numbers of samples: [372478, 18583]
rs i run it to Value Error: Found input variables with inconsistent numbers of samples: [372478, 18583] i test my model(X1), which is differetnt CSV file than i used on training trained my model(X) ...
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Linear Discriminant Analysis (LDA) determining the class for test data after transforming with eigenvectors
Suppose we are given two classes class-1 and class-2 and the mean of the two classes are $μ_1$ and $μ_2$ respectively before projection. Both of their variance-covariance matrix is also provided ...
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Outputting handwritten digits with a Neural Network
I know that you can use a neural Network to recognize handwritten digits. How would you then use that same neural network to output handwritten digits in the unique style of that network? In other ...
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Interpreting Centered 2-Way PDP
I have recently used scikit-learn's PartialDependenceDisplay.from_estimator function to display cICE plots in combination with PDPs using the ...
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How contextual embeddings learned during training a transformer are applied to the input sequence at inference time
I'm trying to understand contextual word embeddings better, and how they are applied at inference time.
While training a transformer, embeddings are learned as parameters during training. Are the ...
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The best algorithm(s) for finding the best hyperparameters (special case)
I would like to ask for help with the following.
Given the following dataset, which I have split into train and test sets:
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Estimating the Increase in Rademacher Complexity after Feature Selection
I'm trying to estimate how much the Rademacher complexity (or empirical Rademacher complexity) increases when performing feature selection using methods like Sequential Forward Selection or Genetic ...
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Implementing L2 Regularization in pure NumPy
I was implementing L2 Regularization in pure NumPy as an exercise to figure out the inner workings of the mathematics of an ML model.
I'm unsure if it's done well, I don't really have a reference to ...
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does DALL-E api use Microsoft account info while generating responses?
I have read some work regarding occupational gender bias in AI image generation and it seemed. According to my research, tools like DALL-E generated more images of men when trying out for images with ...
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Theoretical Limitations of Achieving 100% Accuracy in Modeling Non-linear Relationships with Neural Networks
I am working on a project where I need to model a specific non-linear relationship using a neural network. The relationship is given by $y = 3x_1^2x_2^3 $. The approach involves:
Preprocessing the ...
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Why do Shapley value solutions remain consistent when the value function of the empty set changes in the ML context?
Hey there data science stack exchange - question about SHAP.
In the original Shapley value formulation from Lloyd, one assumption is that the value function of the empty set equals zero, $v(\emptyset) ...
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How to choose segment in Grouped AUC metric?
Background
In Binary Classification, AUC is a common metric. However, Group-AUC performs better in some scenario, such as we use AUC grouped by user in recommendation systems.
In the below examples, I ...
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Is DataSpell super slow training models?
I am working on a project where I have to train a couple of models, from Random Forest Regressor to Hybrid CNN-LSTM models.
I was using DataSpell for this, also using the GPU of my laptop for the ...
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ML for lot sizing task
I am solving the problem of automatic lot sizing, which consist of different products. I receive data about a certain product and I have to decide which products to combine into lots. And then ...
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Does f1 score evaluate only the model or does it also enable us to observe and evaluate the data?
I have a dataset. This dataset consists of the data that the actual picture that needs to be drawn, that is, the 100-point graded paper, and the similarity between 100 and 0 points graded pictures ...
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How to explain missing dates to a model?
I have this dataset that I'm trying to train a neural network on.
The problem is that since weekend dates are not available, I am not confident in whether the model is able to account for that. ...
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Feature Engineering a Recency feature
I have a customer scoring problem I'm working on specifically on predicting conversion and coming up with a probability score on conversion (using xgboost classifier atm). There's a feature I want to ...
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Improving GPU Utilization in LLM Inference System
I´m trying to build a distributed LLM inference platform with Huggingface support. The implementation involves utilizing Python for model processing and Java for interfacing with external systems. ...
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Fuzzy Name Matching with Machine Learning. Input data encoding
I have a huge dataset: Last name, first name, date of birth of Indian residents and I need to match them for similarity.
The matching is fuzzy, the data looks like this (names are fictitious for the ...
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Test Error is extremely higher than Training error after gridsearch and crossvalidation
I'm currently working on a machine learning project. It's a supervised learning problem. My goal is to predict for given data of an animal(keeping,size,weight,...) ingredients(energy,vitamine etc..). ...
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How to measure different models' feature importance using a generic and common standard?
I want to measure the feature importance of a series of models after training them. Most models have some built-in APIs that allow me to access their feature importance, but as far as I know, these ...
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Can anyone help me understand this problem in my data?
I tried making a model using the autoTS library but the thing is in the result it gives me the following results. I checked everything there is no missing data but the original data had a missing ...
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recognition of names, surnames and patronymics
is there an example of neural networks on Github or Kaggle that perform the task of recognizing identical surnames, first names and patronymics?
I'm just learning neural networks so it's interesting ...
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Does Factorization Machines accept continuous variables?
Most of the implementations I have seen of FM rely on an Embedding lookup matrix, restricting the variables that can be used to some categorical variable. Is there a way to use FM with both ...
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Diffusion Models: Conditioning on Time vs. Noise Level
I am new to SE-Data Science, therefore I hope this is the right place to ask this rather theoretical question.
In diffusion models we usually have a time variable which determines the noise schedule (...
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Using SMOTE Train Model and Optimal Cutoff on Unbalanced Test Data
My original dataset has a binary dependent variable with 3% of the values being one. First, I split the original dataset into training and testing sets using an 80-20 split. Since it includes both ...
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Is it possible to train a neural network to feed into a Random Forest Classifier or any other type of classifier like XGBoost or Decision Tree?
I want to create a model architecture to predict future stock price movement as such:
The Goal of this model is to predict if the price will go UP or DOWN within the next 3 months.
I have tried a few ...
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Training VAE in Latent Diffusion Models
When working with Latent Diffusion Models (LDMs), is it common practice to only train the U-Net component while leaving the VAE untrained? Additionally, does this approach apply when fine-tuning an ...
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How do I interpret probability results in conjunction with my known precision/accuracy/recall scores?
I have a Random Forest Classifier (trained with sklearn) modeling a binary data set. Here's what the configuration looks like (I've tuned it for precision intentionally):
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Modeling spatial data
I have the following dataset. For every time point (at a frequency of 1 hour), we can construct a graph consisting of 20 nodes representing countries. Each country (node) is characterized by 5 ...
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Leveraging Extra Data to Enhance Text Clustering
I want to cluster thousands of text data (called corpus A) and find a label for each cluster. Accuary of clustering is significantly important, because I want to use the texts and their labels for ...
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How to implement CP tensor completion with extra calculations?
I am new to tensor decomposition. I want to know from a practical point of view, how to use an already known tool/library to compute CP factorization for tensor completion. Specifically, I want to ...
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Using training data that requires manual interpretation
I have a dataset that comprises several data streams that are measured on objects (>10k objects). The data is essentially time series data (0.5 second intervals). Typically, an expert interpreter ...
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Determining the threshold value for the neural network
I have a dataset with last name, first name, middle name of people participating in sporting events. I need to train a neural network that will match similar surnames, first names and patronymics. But ...
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Optimized input data structure for ML model training
I have a large dataset (20M+ rows) of user interactions which I want to use to predict the probability of a customer purchasing an item in one-, three- and six months time. However since the ...
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How good are LSTMs in generalizing when learning curves?
I'm interested in the following scenario: I want to learn a mapping that maps a function to another function, i.e. I want to approximate a functional operator. If one is unfimiliar with operators one ...
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