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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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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Why is the PPO agent in RL giving negative rewards after each iteration during the training process and what are the possible hyperparameter values?

I am using the mujoco simulator as my training environment. I loaded Ant-v3 for the agent to train on. It is persistently producing negative rewards after each iteration performed.
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What is meant by averaging inhibits it in the paper 'Attention is All You Need'?

Could anyone explain to me about the sentence below? What is meant by averaging inhibits it? Multi-head attention allows the model to jointly attend to information from different representation ...
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Why would we add regularization loss to the gradient itself in an SVM?

I'm doing CS 231n on my own. I'm looking at this solution to a question that implements a SVM. Relevant code: ...
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How to measure similarities between two datasets with same features?

I have multiple datasets with the same features, a few numerical and a few categorical. The only difference is that they are market behavior for different countries. I wanted to know if there is a way ...
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1 answer
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Automated feature selection - Best practice to avoid data leakage?

This question relates generally to all automated feature selection approaches. In my particular scenario, we have a python package called tsfresh and multiclass classification. What has been done so ...
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Hashtag-based Tweet similarity

I have a big dataset consisting of tweets including hashtags and I want to build a hashtag-based similarity engine to get the most similar tweets given a set of hashtags. In the end I would like to ...
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Why isn't my backprop matching autograd?

Am attempting to implement backpropagation for a deep learning course but my backprop gradients don't seem to be matching the gradients you get from autograd. Here's the code: Is my math incorrect ...
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Advice on vision ML classifier pipeline

I'm a neuroscientist and I've become quite good at using ML to predict a variety of variables of interest, however have no experience with vision ML. My aim in 2022-2023 is to learn vision ML so that ...
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Near duplicate detection algorithms for a near real time system

I'm looking for near-duplicate detection algorithms or techniques for a near-real-time system with large document volumes. I know LSH is the most popular industry-standard algorithm for syntactical ...
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5 votes
4 answers
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Calculating Potential Usefulness of Acquiring Additional Data [closed]

Imagine Anne has a labeled training dataset for a machine learning prediction problem. There is an opportunity to acquire more data from an agent, at a cost. However, before she decides to acquire ...
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1 answer
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Model to implement Question Answering System over structured data

I need to write a program(like a chatbot) that retrieves an answer from a CSV datafile based on a question user asks. So for example if the CSV stores list of products and its specifications in 5-10 ...
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Are neural networks(or any other machine learning tools) capable of predicting the next element in "1 1 0 1 0 0 1 0 0 0 ..." sequence?

Is neural network can solve tasks that involving some type of counting, for example this sequence ...
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1 vote
1 answer
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Calculate RMSE based on R squared and vice versa

If for example I have the value of RMSE can I calculate the $R^2$? And vice versa if I have the value of $R^2$ can I calculate the value of RMSE? I have all predictions, dataset, training set, and ...
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Has anyone been able to run sklearn_evaluation.plot.grid_search with the "kind" parameter as "line"?

I am trying to optimize the hyperparameters (one of which is hidden_layer_sizes) of the ML learner: MLPRegressor, appealing to a visual representation to guide me ...
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Fakebert implementaion

I am trying to implement this architecture of fake bert for fake news detection, but I don't know how to feed the word embedding from Bert. Help, please.
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Stacking ensembles in meta learning with only one base algorithm

I'm learning stacking ensembles in meta learning and , there is an example where thy used only lightgbm as base model and linear regression as meta model, they first Split thé dataset into 50 samples ...
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1 vote
1 answer
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Time series model hardly fitting well

I'm trying to forecast Google's stock prices. I've made two models one with LSTM and another one that's Bidirectional LSTM, but the forecasted values don't converge quite well with the test values. I'...
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Linear regression returning negative values for house price prediction

I am trying to do a prediction of real estate (prices are in millions). The mean price for the dataset is 4 million. I do not have any negative values in my dataset,...
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Using ML to find ideal racing lines given a map of a track

I'm currently interested in a project where I use ML to find an optimal racing line given a track map. The project essentially takes a map of a race track and draws an optimal line through the track. ...
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1 vote
1 answer
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Why can Random Forest "handle missing values and cardinality well compared to linear regression"?

I've read a question comparing Linear Regression and Random Forest Regression. I was supposed to choose between then and solve a problem (=predict prices). The question mentioned that "Random ...
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GAN - discriminator loss remains at a constant value while the generator loss decreases?

I am building my first GAN network, and I noticed that sometimes the discriminator loss remains at a constant value while the generator loss decreases. I couldn't find an explanation - if the ...
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3 votes
1 answer
132 views

Performing a text classification based on a dictionary

I have been given a kind of dictionary which maps a category with a set of certain strings. A sample of the dictionary is given below: This is all I have, there is no other data. There are around 46 ...
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2 votes
1 answer
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Threshold tuning with one-vs-rest for multi classification python

I’m currently using a One vs Rest Random forest algorithm for multi class classification problem using Python, and I want to find the optimal threshold for each class, How can I do this with OVR (One-...
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How do I handle known trend inconsistency in data?

I have a dataset of house prices where during a certain period there was recession in the housing market (late 2008 to early 2012) and prices fell. The problem is the data during this period makes up ...
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1 vote
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ML techniques for mathematical inverse approximations

I have some inputs and outputs of a set of functions, and I want to be able to find/approximate any given input vector from its corresponding output vector (In other words learn the inverses of these ...
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1 answer
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Best Python Library for Spiking Neural Networks?

I'm trying to experiment with Spiking Neural Networks and I'm wanting to use a high-level programming language like Python. I've come across some libraries, i.e., ...
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Capacity planning and modelling

I have a business case in which I am going to model how many devices are required given the predicted workload in a series of monthly cohorts in the next ten years. The work could come from multiple ...
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Are these ANN training graph and validation graphs incorrect?

I have trained an ANN using Keras (Python3). However, I do not understand the training and validation loss graph. There's a big difference between the first and second training point. Is the graph of ...
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How to draw a ROC curve for each Fold in cross validation in R

I am evaluating my model using K fold cross validation and I would like to draw a ROC curve for each of the folds and show them ALL TOGETHER. I'm using the R programming language and I'm going to ...
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1 answer
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Biometrics identification with embeddings comparison and "unknown"/"other" class/label

This is a general or more conceptual questions about biometric classification models, based on deep learning neural networks. The goal of the system is to take a set of features (e.g. voice recording, ...
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2 votes
2 answers
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Why do we don't write units with MAE or RSME for regression problem ? If I wish to write the units when how do I identify the units for them?

I have referred many research paper but no one is talking about the units of the metrics. Do MAE , RMSE etc have some units ?
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Deploy ML model on java web server or on python web application

Newbie here and I only have experience in training machine learning models on Jupyter notebook along with test/validate the model accuracy. I would like to move on and learn how I can deploy machine ...
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how and what parameter to choose for a random forest classifier?

I am building a random forest classifier for DoS/DDoS attack detection, it is a ...
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masked image and language modelling

I was coding this piece of code which heavily relies on the demo of visual question answering, and I'm masking inputs while feeding it to the bert using [MASK] token, and providing a label which ...
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Using scipy.optimize.curve_fit for Non-Linear, Multi-Variate Models

Warning: ML Noob. I have a 3D dataset (data at the bottom) with 2 feature variables and 1 target variable. Polynomial Regression produced unsatisfactory results and it seems that the relationship of ...
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1 vote
1 answer
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Get result from log transformed variable

I can't find some documentation. I had right-skewed target (sale price) variable and also some skewed features at the same way. I did log transformation and fit the regression model and it doing well. ...
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XGboost Classifier predicits different results for same samples depending on test dataset size

I train a simple xgboost classifier model with the following lines. ...
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1 answer
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What is the right way of training Regression model having various categories involved?

I am working on one regression problem statement and it involves multiple categories into it. I am not sure how to proceed with it, hence looking for your guidance/suggestions over it. Suppose there ...
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