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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Weighting feature-specific reconstruction loss in Seq2Seq VAE
I am working on training a Seq2Seq Variational Autoencoder (VAE) model using healthcare data. In my dataset, I have features that exhibit varying levels of variance across patients. For instance, ...
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Goodness of fit test/index for a regression tree
I have fitted a regression tree on my data and would like to demonstrate that it is a good model. Are there any standard goodness of fit test or index for a regression tree?
I understand that I can ...
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Optimizing Student Test Performance
I am a Secondary Math teacher who is interested in creating effective learning in groups. My desire is to create a system/program that maximizes student Test performance, but I am not exactly sure ...
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Determining the sample size of a very unbalanced machine learning problem
I have a machine learning classification problem where 0.05% of the population (N = 100k) is of the positive class. It is important that I don't misclassify these positives (aka I want to minimize the ...
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Input non-sequential data of arbitrary size to network
I have a case where I want to feed a network with polylines of data. The problem is that the input can be any number of polylines and the polylines can consist of any number of points. If we instead ...
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Is there a better way to self-train on tabular data?
Context:
I'm training a classifier on some fraud data. Only a chunk of data is labeled (~2000) so I'm trying a self-training approach, what I'm doing for now is:
Iteratively training a model then ...
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Need help in random forest time series forecast
I am a beginner in time series forecasting using ML, and I am encountering a strange phenomenon. I have air quality data, in which I have information of various pollutants. The goal is to predict AIR ...
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Using ML for simple rule based to obtain likelihood
Let's say I already have a simple rule which provides me the variables that help determine the output label and relationship between variables and label is rather simple and deterministic one, can I ...
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When does model selection begin to overfit?
Suppose you have a small dataset (perhaps 1000 labels), and you are using cross-validation to train different models and to choose the best one (according to their cross-validation scores).
It seems ...
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What is an appropriate timeseries model to identify macro trends for multiple rows of data per timestamp
How should I approach a univariate timeseries with multiple rows of data per timestamp?
Using stocks as an example, I am attempting to identify a time series model, or suitable alternative, that ...
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Can variables used for rule based labeling be treated as input features?
I am currently working on binary classification problem with imbalanced dataset (n=3419 and 69:31). However, based on the business expertise of the users, they have generated rule-based label based on ...
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Theoretical justification for information loss
Suppose we observe some physical event and we measure multiple time series. So for example we have a time series for a concentration level, a time series for energy consumption, so on and so on.
Now ...
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Do I need to standardize time series data in change point detection?
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I have process data in time series data(0min, 1min, ... 999min). I don't know what does the variables mean. They are just written in X1, X2, ... X52. Each row means the data at the time. At certain ...
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Longitudinal data in ML vs GEE
I'm currently working with pregnant women data. Given that the same woman could have multiple pregnancies over the years, I tend to use GEE to obtain odd ratios of my variables of interest.
Now say I ...
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BorutaPy: All features are classified tentative
I am using boruta_py, Python implementation of the Boruta algorithm, with a random forest estimator.
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What is a Student-t VAE ? and how is it different from Gaussian VAE?
I am currently reading https://www.ijcai.org/proceedings/2018/0374.pdf ,this is a research paper based on Student-t Variational Autoencoder for Robust Density Estimation , In this research paper, they ...
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Best way to encode a variable number of points in a 2D space as features
I am presented with this problem: given a set of points (the number can vary, and their identity is not fixed across observations) distributed in a bounded 2D space (say $x \in [0,1]$, $y \in [0,1]$), ...
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Can input variable be leaking data?
I am currently working on a binary classification problem using imbalanced data. The algorithm that I am using is random forest. The problem is about predicting whether each sales project will meet ...
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Time series prediction problem formatted correctly for LSTM neural networks?
I am new to machine learning, I am trying to find a way to predict voltage waveforms into the future. I have seen examples that successfully predict sinusoids or continuous voltage data based on ...
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Small number of weights in the SVM separator function
The separator function in SVM is:
$f(\mathbf{x})=\sum\limits_{\mathbf{x_i} \in Support}\alpha_i\times K(\mathbf{x}, \mathbf{x_i})-b$
Depending on the kernel, this may correspond to adding any number ...
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Spatial autocorrelation machine learning python
I am trying to build a machine learning model for spatiotemporal data. My predictors are all monthly climate variables, and as such display spatial autocorrelation. The target dataset however has ...
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Figure comparing path length and density (figure 3a) in paper "Isolation-based Anomaly Detection" by Liu, Ting and Zhou
In the paper Isolation-based Anomaly Detection by Liu, Ting and Zhou, figure 3a compares the density and path length for a cluster of anomalies. Figure 3a along with the sub-text has been provided as ...
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Significance of challenges cited, in creating anomaly scores out of path length, in original paper for Isolation Forest
In the original paper on Isolation Forest by Liu, Ting and Zhou, the authors cite some problems in creating anomaly scores out of the path length $h(x)$ of a point $x$ in an iTree. The text reads as ...
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With ReLU activation, are we adding if conditions to a neural network's toolset?
I was reading Reverse Engineering a Neural Network's Clever Solution to Binary Addition. Without repeating the whole article, it seems the network figured out addition was already a part of its ...
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Selecting optimal lag values for Neural Network in univariate time series forecasting - How many lags to use as input variables?
What is the recommended approach for selecting lag values in a univariate time series forecasting problem, specifically for input variables in a feedforward neural network (FFNN)? In my research ...
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L1 vs L2 variance? [closed]
Which regularisation method L2 or L1 gives a lower variance?
$
f(w) = \sum (\hat{y}_i - y_i)^2 + \sum || \beta ||^2 \rightarrow L2
$
$
f(w) = \sum (\hat{y}_i - y_i)^2 + \sum || \beta || \rightarrow L1
...
user387816
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Why does my bi-encoder converge to the mean square of the [0,1] label distribution? [duplicate]
I'm following the Bi-Encoder architecture (see here) in order to build a dense retrieval (search) system. Formally, my network encodes a query q and an item description d based on fixed ...
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Class-Imbalance: How to handle different class distributions in training and held-out test data?
My dataset is high dimensional (sample size is 200 with 300 features) and imbalanced. The imbalance ratio is 80:20 in the training set and 88:12 in the held-out test set (collected at a different time ...
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How to calculate softmax for decreasing values?
I am using a model for a multi-classification / ranking task, however for each choice problem it associates the different options with a number that is in a range with negative numbers with the caveat ...
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Which loss function to use if entries in a prediction span multiple orders of magnitude?
I am modeling the rates of chemical reactions with a ML-model (specifically a neural network with a lowish number of parameters). However, the rates (or technically the formation rates), the ...
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Overfitting models in mlr3
I'm trying to compare multiple learners on my dataset (called "data") in order to predict a target called "lesionResponse", with custom resampling. Since mlr3 package doesn't allow ...
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Why use the "gradient" in gradient boosting? [duplicate]
Let us say we want to predict a one dimensional, real valued $Y$ from $X$. In gradient boosting, the final model $f$ is built by adding the prediction of several trees, essentially $f_1 + \dots + f_M$....
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Interpretation of area under the precision-recall curve
The area under the receiver-operator characteristic curve has a interpretation of how well the predictions of two categories are separated.
This post gives the area under the precision-recall curve as ...
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A way to train a model on data with a very large number of features
I have standard data: where rows are observations, and columns are features.
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How to encode sparse and variable length sequential data
I have 100k historical horse races. The data is sequential in time, so I am wishing to use online learning to train an LSTM (or sequential attention model or something similar...) such that the model ...
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If feature importance is only computed based on training set, does it mean one should never compute shap values on test set?
If feature importance is only calculated from the training set according to here, does it mean one should never compute shap values on test set? What would it mean if I compute shap values from test ...
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Feature Selection Using Bootstrap Resampling, LASSO and Stepwise Regression [migrated]
In the following paper (https://pubmed.ncbi.nlm.nih.gov/33984349/) the authors perform radiomics feature selection for survival prediction by:
Bootstrap resampling the dataset x 1000
Fitting cross-...
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Down sampling the big class in imbalanced data
I’m working with big, imbalanced data set for a binary classification challenge. Big in the sense that it’s hard to digest all at once, and imbalanced in the sense that for every positive example ...
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Customized F1-Score for multi-class classification
Let's consider a multi-class classification problem with 4 classes: 0, 1, 2, and 3
F1-Score 'macro'-averaged is calculated like that:
...
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Normalization or standardization in stock prediction
Currently I watched the videos (links below) that argues using the normalization (max-min scale) is the bad idea when it comes to the stock prediction. In the videos, the editor aruges that people ...
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How to account for data's margins of error in a regression?
Apologies if this is too simple of a question. I work with population data from the census, which provides a margin of error for all their variables. Reviewers have asked that I account for the MOE in ...
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Can I remove correlated features before cross validation?
I am working on a high-imbalanced (80-20 ratio), high-dimensional dataset (200 sample size, 300 features) where all variables are highly correlated. Can I remove the perfectly correlated (using ...
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AdaBoost vs GBM vs XGB
Can someone please summarise the difference between these 3 boosting algorithms: AdaBoost vs GBM vs XGB?
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Regression as classification: advantages?
I have read on many occasions deep learning practitioners recommending to treat regression problems (with continuous variables) as classification problems, by quantizing the output into bins and using ...
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Is bagging less useful in 'big data' settings?
In 'big data' settings where the number of samples $n$ may be very large (for fixed number of features), is bagging less or more effective at reducing variance? I heard the claim that it is less ...
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Proving Perceptron algorithm mistake bound is tight [closed]
How would I prove the Perception mistake bound is tight. Avrim Blum’s lecture notes claim that the upper bound for mistakes is $\frac{R}{\gamma}^2$, but I don’t understand how to prove this is mistake ...
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What is the best model to use with a time-series dataset of uneven data?
I'm working on building/selecting a model to predict the result of a sales lead: whether it's "SOLD" or "NOT SOLD".
My dataset consists of past leads with the following data:
...
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Why do we need nested cross-validation? [duplicate]
I know the principle of nested cross-validation, and it is used for testing model performance. However, when choosing models and selecting hyperparameters we still use normal cross-validation (CV) ...
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Extending efficient PAC learning with classification noise to statistical query model with unlabeled random draws for axis-aligned rectangles in $R^2$
I have proven that if $C$, a concept class, is efficiently learnable from statistical queries using $H$, a representation class over $X$, then $C$ is efficiently PAC learning using $H$ in the presence ...
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Using Generative Adversarial Networks for joint distribution estimation
I am trying to use GAN model to generate N-dimensional samples with joint probability distribution that looks like some training data. I am having trouble getting the probability distribution of the ...
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