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Questions tagged [machine-learning]

Machine learning algorithms build a model of the training data. The term "machine learning" is vaguely defined; it includes what is also called statistical learning, reinforcement learning, unsupervised learning, etc. ALWAYS ADD A MORE SPECIFIC TAG.

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Why is the threshold term incorporated into the weight vector in linear classifiers?

In the context of linear classifiers, such as the perceptron or logistic regression, I understand that the decision boundary is defined by a linear combination of input features and weights, plus a ...
Narges Ghanbari's user avatar
-1 votes
0 answers
37 views

What are some SOTA algorithms for single hyperparameter optimization while training Deep Neural Networks?

I would like to optimize a single hyper-parameter while training a deep neural network. Let's say it is the learning rate of the network. What algorithms should I use to optimize the process? A ...
desert_ranger's user avatar
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Independance in Bias Variance decomposition [closed]

In the second line of derivation we use independance of $\varepsilon$ and $\hat{f}(x)$, but which hypothesis lead to this independance result ? Is it because all observations are iid ?
amous's user avatar
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1 vote
2 answers
38 views

Defining clinical follow-up: Fixed Period vs. Maximum Duration

We are retrospectively analyzing data of around 1100 patients operated between 2017 and 2023. We analyzed follow-up documentation until 2024. This means that patients operated at a later date will ...
Philipp's user avatar
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0 answers
18 views

Why does the square term get omitted in Gradient derivation of parameter θ-th

I get it that my question may sound a bit sophisticated or overwhelming, but it's pretty straightforward when you read the image below. As you can see, the square ^2 completely dissipates, despite ...
iHunter's user avatar
1 vote
3 answers
53 views

Can I apply data augmentation to the test set?

I'm working with a dataset of 102 rows (tabular data), from which I'm using 91 for training and 11 for testing. I'm using data augmentantion through the addition of gaussian noise for the training set....
Vinicius Maia's user avatar
4 votes
2 answers
274 views

What is the Gold Standard for Evaluating the Posterior of a Bayesian Regression Model?

Let me explain my meaning & the context: I mean evaluating the correctness of the posterior (e.g. for approximate Bayesian inference methods). I care mostly about Bayesian deep learning, I'd like ...
profPlum's user avatar
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2 answers
101 views

How to generate 95% prediction interval around predictions from ML model?

I have predictions from an ML model and would like to generate 95% prediction intervals around each prediction generated from the model such that I can claim that these are the plausible range of ...
JElder's user avatar
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-1 votes
0 answers
11 views

Is Analyzing Post-Fire Vegetation Recovery with NDVI Values Considered an Interrupted Time Series Analysis? [closed]

I'm working on a project that involves analyzing the recovery of vegetation after a wildfire using NDVI (Normalized Difference Vegetation Index) values. My goal is to assess how vegetation regenerates ...
itvcha10969685's user avatar
0 votes
0 answers
36 views

Environmental filtering versus spatial resampling in species distribution modeling

I am building species distribution models using machine learning models based on GBIF data (presence-only data) and working on a very large spatial scale, encompassing all of North America. Before ...
Marine Régis's user avatar
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0 answers
27 views

A faster way to choose the right number of features and the right parameters [closed]

For a very small dataset with less than a hundred samples, gridsearch does not give us the desired results and has overfit. But when I perform hyperparameter tuning of the model manually and also ...
Erfan Mollai's user avatar
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0 answers
21 views

How Random Forest handle missing value in sk-learn? [duplicate]

What is the technic used in Random Forest Regressor from scikit-learn to handle missing value ? First I thought that a Random Forest regressor was able to natively handle missing value during training ...
Maxime Charrière's user avatar
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0 answers
6 views

Using scale pos weight and non 0.5 cut off score for a look-alike model

I'm working on a classification problem where I'm trying to identify look-alikes of Class 1 in Class 0. Class 1 and Class 0 are established based on type of product customers use. Basically, Class 1 ...
user3437212's user avatar
0 votes
0 answers
26 views

Test/validation set

I've been having a discussion with colleagues and wanted to seek your input. If I'm using holdout and cross-validation to build and test my models. In this process, the training set is used to tune ...
John Doe's user avatar
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2 votes
0 answers
23 views

Using whole training set for choosing model

I am working on a classification problem with what I understand as a big dataset. I have first of all splitted it in my "train" dataset and the "test" one. (Actually I am convinced ...
Videgain's user avatar
4 votes
1 answer
174 views

Link between Cross-entropy and MLE

There are numerous material that show the relationship between MLE and cross-entropy. Typically, these are the steps taken to show the relationship for a I.I.D data generating process $D = (X,Y)$: $$ ...
spie227's user avatar
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1 vote
0 answers
26 views

Output from Model A as Training Data into Model B

Not sure this is the right place to ask this question, but I'm having a disagreement with a colleague on this idea. Let's say we have a dataset comprised of "unclean" strings. The end goal ...
setty's user avatar
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0 answers
32 views

Model suggestion to predict a ratio

I have a project where I have a data on companies (one variable being their number of clients and if they have a had a compliance issue.) Im trying to build a model to find a ratio of employees to ...
B_fig's user avatar
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1 vote
0 answers
39 views

How to Estimate GPU Memory for training and inference, Data Requirements, and Training Time for Large Language Models?

Today, I faced this question during an interview for an ML Engineer position. I didn't answer it perfectly at the time. How should I answer it ideally? Assume we have models like Transformer, BERT, ...
maplemaple's user avatar
0 votes
0 answers
13 views

There are statistical models for nonindependent/multilevel data (e.g., Mixed Models). Is there an approach for multilevel data in Machine Learning?

There are many cases of multilevel/nested/hierarchical data: people in schools, schools in counties, trials within a person, web sessions or mobile sessions within a person, etc. In traditional ...
JElder's user avatar
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0 answers
19 views

Bounds on Rademacher complexity of intinite class of bounded functions

Suppose I have a class $\mathcal{F}$ of bounded functions $f : \mathcal{X} \to \mathcal{Y}$ , i.e. there exists $M < \infty$ such that for all $ x \in \mathcal{X}$, for some norm $ \lVert \cdot \...
Donna Schweitzer's user avatar
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0 answers
6 views

Efficient pooling to extract global embedding from local features (for LiDAR point clouds)

Problem: I have 3d point cloud data (autonomous driving setting) and a point cloud encoder (MinkUNet) that extracts local features from them. What are suitable pooling techniques to map those local (...
Hölderlin's user avatar
1 vote
0 answers
27 views

If there are cubic polynomial features, then isn't this a polynomial regression, not a linear regression? [duplicate]

I have the following problem: Consider a Linear Regression problem with two features. Based on your visualisation of these 2D features, $x_1$ and $x_2$, on the training set, you noticed that using ...
The Pointer's user avatar
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0 votes
0 answers
12 views

Meta-learner trained on matched data [closed]

I am trying to estimate the average treatment on the treated. I have used propensity score matching first, to create the control and treatment groups. I end up having quite small group sizes (1500 ...
gummy's user avatar
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1 vote
1 answer
42 views

Weight initialisation for neural networks - should they be different for each observations or the same?

I am implementing myself a Neural Network with feedforward and backpropagation with gradient descent to understand better how things work. After setting up the entire algorithm, I still have a huge ...
umbe1987's user avatar
  • 297
1 vote
1 answer
26 views

ADALINE simple implementation with 2 features bug

I am reading Machine Learning with PyTorch and Ski-kit learn book by Sebastian Raschka While plotting the decision boundary (a line in this case, since the number of features considered = 2) I can't ...
tripma's user avatar
  • 21
1 vote
1 answer
37 views

Predictive Maintenance of factory parts

I'm training a model to perform predictive maintenance of a particular part in a factory. I have performed data cleaning like removing the null, duplicate values, removing the highly correlated ...
Duke2512's user avatar
2 votes
1 answer
27 views

The meaning of linear transformation in a batch norm revisited

I'm reading BatchNorm Wikipedia page, where they explain that BatchNorm. I think the actual formulas are easier than words in this case. The norm statistics are calculated as: $$\large{\displaystyle \...
Mah Neh's user avatar
  • 174
1 vote
1 answer
40 views

How can different models based on different sets of predictors be combined to significantly improve the model performance?

I have two machine learning models for predicting some continuous variable $y$, say $y=f_1(X_1, \theta_1)$ and $y=f_2(X_2, \theta_2)$, and these models are of the same type (ANN). $X_1$ and $X_2$ ...
tunar's user avatar
  • 523
1 vote
0 answers
20 views

Create 2D representative visualisation clusters from 786D clusters

I have about 60'000 vector embedding of size 786. They divide up neatly into about 10 clusters. I have used K-Means to find the clusters. Now I would like to visualize them. The vectors should be ...
user2741831's user avatar
0 votes
0 answers
15 views

Feature selection for logistic regression and random forest (using Orange - no coding)

I’m using Orange to create a prediction model for the Indian liver patient dataset (binary target variable – either has or does not have liver disease – with 580 instances and 10 features). I’m using ...
Jess's user avatar
  • 21
0 votes
0 answers
9 views

What does it mean "length_scale_bounds" in a RBF kernel from a gaussian process?

I'm studying gaussian emulation process, in particular the use of the RBF kernel and I'm not understanding the meaning of length_scale_bounds. Can one helps me?
redbaron's user avatar
0 votes
0 answers
7 views

Inverse Problem: Using LightGBM model to recommend X (feature) ranges to achieve a specific y (target) range

I am trying to build a LightGBM regression model, where in I have aroud 15-20 Input features and my target variable within a range of 20-40. I have used the SHAP beeswarm plot to kind of understand ...
Debadri Dutta's user avatar
2 votes
2 answers
142 views

Mathematical Prediction of Linear Mixed Models Random Intercept

Given data $\{(x_{i,j}, y_{i,j})\} \subset \mathbb{R}^2$, with $i = 1, \ldots, k$ classes and $j = 1, \ldots, n_i$. The linear mixed model is: \begin{equation*} y_{i,j} = a + b x_{i,j} + u_i + \...
温泽海's user avatar
  • 446
0 votes
0 answers
14 views

Suggestion for training various models for classification of different species

Working on developing classification networks for various types harmful algae vs algae/other objects found in the ocean water. We have developed binary networks for some harmful algae vs Ocean. There ...
Dhruvin Naik's user avatar
1 vote
1 answer
24 views

how to train and hypertune a model

As I am new to machine learning, and learning it myself, pardon me if I ask a silly question. My question is: What is the correct approach to building a model for, say, random forest and tuning ...
NEERAJ YADAV 's user avatar
0 votes
1 answer
25 views

How can I learn and remove the linear trend in the residuals against the true response values generated by an ordinary neural network?

I built a neural network using PyTorch to predict y (a continuous variable) based on X consisting of m (=20) features. I found that the residuals (y_predicted – y_true) for the test data set show a ...
tunar's user avatar
  • 523
0 votes
0 answers
20 views

Information coefficient as loss function of XGBoost

I am trying to train an XGBoost regressor for stock price prediction. I want to customize the objective function to be Information Coefficient (IC). The definition of IC is the Pearson correlation ...
atlantic0cean's user avatar
0 votes
0 answers
17 views

How often do we have lipschitz-continuous gradients for the objective function in practice(convergence of SGD)?

When I see proofs for the convergence of stochastic gradient descent then often it is assumed that the objective function is L-smooth(lipschitz-continuous gradients with constant L). Is this a ...
Sen90's user avatar
  • 111
0 votes
0 answers
30 views

Top-N recommender system

Say an intermediary is using a two part recommender model that attempts to facilitate services between its clients and external vendors: Model 1: Predict probability of vendor bidding on a given ...
user416572's user avatar
2 votes
1 answer
72 views

How do I perform a permutation test on a machine learning model to obtain a p-value for its performance?

this is kinda of the same question of this previous post. But since there's no reply, and I'm having a hard time to find some answers, I'd like to ask it again. I'm training a regression model (SVM ...
artvmac's user avatar
  • 63
11 votes
2 answers
416 views

(THEORY) Do Tree models output probabilities?

I have a question purely theoretical about decision trees outputs for classification. I have heard a lot of people say "the output of tree models are not probabilities", and having studied ...
Felipe Araya Olea's user avatar
0 votes
0 answers
32 views

Neutral number in XGBoost algorithm prediction

Does a concept of "neutral number" in machine learning algorithms exist? To make it clearer: suppose we have a logistic regression with only one feature, the "neutral number" is ...
Marco Ballerini's user avatar
2 votes
2 answers
29 views

Does clustering actually reduce the number of rows in a dataset?

I am reading the book "grokking Machine Learning" by Luis G. Serrano and came across the following sentence: "It seems that clustering and dimensionality reduction are nothing like each ...
Leox's user avatar
  • 129
0 votes
0 answers
28 views

What do people in machine learning typically mean when they talk about something being ill conditioned?

RIght now I am reading a paper which deals with some optimization methods for machine learning and the author explains how some of the methods "deal with ill conditioning". I know the term ...
Sen90's user avatar
  • 111
3 votes
2 answers
172 views

Fitting multiple linear regression models to select molecules for which a feature of interest significantly alters concentration

I'm using data from a proteomics platform called Olink. In this case, the data comes from samples of 16 patients and 16 controls. The patients can be further classified into 2 subgroups. The assay ...
maglorismyspiritanimal's user avatar
0 votes
0 answers
9 views

Why learn an embedding before self attention when training transformers?

I understand that self-attention layers learn the "role" of a word in a sentence while embedding layers learn the relationship between the words. But I am not totally convinced that a self-...
Nicolas Johnson's user avatar
0 votes
0 answers
23 views

ML on Vienna Stock Exchange: Predicting hypes for the ATX index and conducting sentiment analysis on the top listed companies [duplicate]

Hello everyone and nice to meet you! :) I am new to this forum... I would like to practice on ML and create a model which predicts stock hypes for ATX index (Austrian Traded Index) and detects the top ...
user415686's user avatar
0 votes
0 answers
11 views

CNN matrices shape for time series data processing

I would like to ask you for advice regarding CNNs for analysing 60000x16 data (single input) - time series records from 16 channels. I did some research on this and my initial idea was to use CNN with ...
kalmary's user avatar
3 votes
0 answers
49 views

How to Automatically Identify Column Headers in New Datasets Using Machine Learning?

I have a dataset with vehicle data that includes headers (example): I receive new datasets with similar vehicle data but no column headers: I need to create an algorithm to recognise and assign ...
user782750's user avatar

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