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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Neural Network not learning [duplicate]

I'm building a Neural Network from scratch, in order to understand them better. Problem is that even if I spent several days on it I can't find a way to have it learn something, not even the XOR ...
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Is there a boundary between statistical and machine learning methods?

I want to better understand what exactly is considered "machine learning" or not. I realize there might not be a categorical answer for this, but that's what I want to understand. ...
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Missing "None" class in outcome variable. Least bad way of handling missingness?

I'm dealing with a preexisting dataset with an outcome variable of suicide which entails the following classes, of which multiple can be selected, but they roughly escalate in severity. Check if ...
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How to fix this ValueError: Shapes (None, None) and (None, 3, 3, 16) are incompatible in VGG16 [closed]

I am currently fine-tuning a VGG16 on a multi-classification problem. The requirement is to add a new 1 Conv block, 1 Maxpool layer, 2 FC layers, and an output layer. I have removed the top layers of ...
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Help! Cluster analysis of curves - grouping already grouped data

I have derived a number curves which illustrate the relationship between two (non-time) variables at different locations. I want to be able to group these curves / functions together, so that ...
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Range for Euclidean distance between two variables [closed]

I have two datasets A and B. I want to compare elements of those datasets to find "matches", i.e., elements from ...
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Will these skills in machine learning be relevant in fall 2023? [closed]

I have a masters in civil engineering but would like to work within IT. From my education I have a mathematical and data analysis background. I'm considering taking a course in machine learning to ...
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Why variable representation plays a role in prediction?

I am working on binary classification using a random forest, where the data have 977 records and 6 columns. The class ratio is 77:23. I have two derived input variables. One variable is called ...
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Precision vs Recall Tradeoff plots 2 separated lines

I'm trying to build a binary classifier with high recall and slightly better precision so as to avoid a lot of False Positives. So far the best scores I have got from all different types of model ...
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Using Inception and FID scores in training?

Is it possible to use the Inception and FID scores in the training of a deep image generation model, i.e. to maximize the scores in a loss function, albeit this is "cheating"? If so, has ...
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Stratification of the continuous y (target) variable in regression setting

Is it wise to stratify the continuous y (target) variable when you split your training and testing data from the total sample in regression setting? Here is the approach in python to do implement ...
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Fisher's exact test when resampling from the population

I am using different ML classifiers for making predictions related to a binary classification. I would like to compare two groups. Let's call them A and B. The already trained and tested classifier ...
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Should I join train and validation sets for final NN model training? If yes, when to stop training the final model?

Normally we divide our dataset into 3 sets: train set, validation set, test set. We use train set to find optimal parameters (weights and biases of NN) and validation set to find optimal NN ...
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is nrmse scale-dependent?

Im trying to evaluate my regression models using a normalised version of the RMSE, nrmse = rmse(y, y_pred)/rmse(y, y_mean) where y_mean is the array of the same len as y filled with the mean value of ...
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Estimating Top n Prices points on a given day for a particular product which would maximize revenue

Problem Statement :- On any given date of the year, get top n prices which would maximize the revenue for that day for a particular platform. Dataset :- Date Price ($p_{i}$) Platform $X_{1,i}$, $X_{...
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What is a differentiable approximation to the indicator function a != b [closed]

I am dealing with an optimization problem where I'd like to regularize two parameters $a$ and $b$. The penalty should be $1$ if the parameters differ and $0$ if they are the same. The motivation is ...
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Using machine learning model trained on standardized data for real world low volume data

I have developed a machine learning model which has been trained on a preprocessed data by scaling and centering using h2o package of R. I am able to use this model ...
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Balancing Multiple Evaluation Metrics for a Model

When evaluating a machine learning (or other statistical model) against multiple evaluation metrics, is there a standardized way to choose the "best" model? As a concrete example, for a two ...
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Which are well-known/popular feature selection methods? [duplicate]

I am trying to test the performance of first reducing the number of features before applying methods such as neural networks for prediction. Due to the fact that the number of observations is not ...
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How do I perform a train-validation split on data with class imbalance such that the class imbalance ratio is preserved?

My data has class imbalance-- that is, some classes have significantly fewer training samples than the others. I want to perform a train-validation split in such as way that the class ratios are ...
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Finding feature values for regression model such that output is more than a given value?

Suppose you have an (online) shop. You have a dataset containing $p$ features (representing customer characteristics) $X = (x_1, \ldots, x_p)$ and a feature $y$, representing how much money a customer ...
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Implications of Violation of Independence Assumption in ML Models

This is a somewhat broad question, but I'm having trouble finding a good answer anywhere. I know many ML models will impose an independence assumption in the data. But I'm having a hard time really ...
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3 votes
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Matrix Factorization and Overfitting

I recently came accross the algorithm of Matrix Factorization for a recommendations system. One of the tutorials I followed can be found here. According to it given the initial matrix $R$ and the ...
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Distance between two clusters after their joining in centroid linkage

For a distance between two clusters A and B of objects given by $d_{AB}=\left \|{m_{A}-m_{B}} \right \|^{2}$ , where $m_{A}$ is the mean of the objects in cluster $A$, show that the formula ...
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How to test the statistical significance of several clustering algorithms? [closed]

I have computed ARI, NMI and Silhouette scores of several clustering algorithms on single cell RNA sequence data. These metrics are computed three times for each algorithm and for each dataset. Now, I ...
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How can I calculate Posterior Distribution, analytically with given information?

The image below shows that the posterior distribution is as follows with given information: I wonder how the posterior has been calculated, analytically.
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High Performance Classification or Similarity Algorithim for Mixed Data Types?

I have a database holding 10-ish features that describe different breeds of dogs. They are mostly categorical features, but some provide ranges for values. Here's a demo representation of the database,...
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Conditioning of join gaussian over a line

I need to compute the conditional probability of bivariate normal distribution over a line. Let's suppose that X and Y both are normal distributions and that they are independent. Let's suppose that ...
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Cluster Validation and Sample Size [closed]

I've recently been using the aweSOM package in R https://cran.r-project.org/web/packages/aweSOM/vignettes/aweSOM.html. The objective is to generate a SOM then impose partitive clustering on top, in ...
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Interpreting learning curves

There is really few examples online regarding interpreting learning curves and they are all of different type.It is quite confusing to me honestly.May I just ask: How should we interpret them?What ...
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Time series forecasting for revenue forecasting?

I am currently working on a project where I have to forecast the revenue for (the duration of) projects within the organisation. The organisation has several departments that occupy themselves with a ...
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How to perform variable/feature selection before random forest in R?

I have a phyloseq object with 4000+ taxa and 300+ samples. What methods/packages can I use to perform feature selection to reduce the number of taxa/OTUs prior to performing random forest? I was ...
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1 answer
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Churn model- how to handle new users without enough historic data?

I'm making a churn model. My observation window (historic data) length is 3 weeks. There are some users that are not been registered to the app that I'm analyzing for three weeks, and as a result, I ...
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BERT MLM - 80% [MASK], 10% random words and 10% same word - how does this work?

I have noticed that (from the original BERT paper) in the MLM training procedure, the authors decide to mask 15% of the words in a sentence. The mask works as following: The masked words are ...
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Handle User Behavior Change when creating a ML classifer

I'm creating a churn model. My first thought was that the bigger the training set, it would be better. However, 2020 was a crazy year because the COVID 19. For example, a user who was sick and ...
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and what if non-linear activation functions give better results than the linear ones?

I had a regression problem with small data set, I solved it with neural networks (MLP, ELM,..) As convention, I used a linear function for output layer, the results were not so good. I tried to change ...
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How to label target dataset based on reduced dimensions of a source dataset?

I have a high-dimensional data matrix with K observations and N variables. To predict the label for each observation, I use some dimensionality reduction method (let's say PCA). Now I have K ...
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Cluster Validation on SOM Codebook

I've recently been using the aweSOM R package for cluster visualisation, https://cran.r-project.org/web/packages/aweSOM/vignettes/aweSOM.html. In particular, the aweSOM package entails using ...
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Variance of the predicted temperature

I am predicting the electrical load and I also use the predicted temperature as one of the input feature. For example, I want to predict the electrical for tomorrow. I use the predicted temperature ...
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Mathematic modelling vs. machine learning

As a biologist with some background in mathematics I am really interested in mathematical modelling of biological processes, like tumor development or therapy responses. However, looking at recent ...
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Interpreting the variance of feature importance results with each random forest run using the same parameters

I noticed that I am getting different feature importance results with each random forest run even though they are using the same parameters. Now, I know that a random forest model takes observations ...
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Separating datasets vs one dataset with extra categorical feature

I have regression/classification problem. Dataset contains data from 4 sensors on 4 positions (1,2,3,4). Processes measured on all 4 positions are equivalent and same label and features describe all 4 ...
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implementing a neural network U-Net with imbalanced classes, implementing the loss function

my problem is : i have a neural network U-Net, but to do the segmentation on my sparse annotation, i need to implement the loss function for the imbalanced classes so the article says, that there is a ...
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3 votes
1 answer
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Why do discrete choice models (such as MNL) not require test set?

The central challenge of Machine learning models is perform well on unseen data. The data is randomly split into train and test set. The test set acts as surrogate for unseen data and is used to ...
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Are tree based algorithms weak at correctly predicting an outcome for a given variable range, if there is sparse data for that variable range?

I am using XGBoost as a classifier, and one of my important variables has values ranging from 500 to 20000. In the training data, there are very few observations where this variable is above 15000 AND ...
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Information Gain Ratio

I tried ranking some variables using WEKA cross-validated information gain ratio. Apart from five of the variables, the other variable average merit came back as 0. is that normal?
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Data Set With Variable Start Dates for Churn Modeling (New customers from business acquisition)

I have dataset representing to specific region of the country. Its purchase data which is how much costumer is placing with associated dates of order, amount etc. I want to do churn modeling for this ...
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2 votes
1 answer
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How to evaluate complementary datasets for ML models?

Evaluating ML models is a fundamental task and subfield of the Machine Learning practice. On the other hand, I was not able to find any existing materials, guides, protocols, papers on how to proceed ...
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Customizing anomalies for different customers

I have built an LSTM autoencoder model to identify anomalies in time series wifi throughput data for over 100 customers. However, the definition of anomalies is very subjective. E.g. Customer A thinks ...
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How to correct for an underestimating (yet consistent) nonparametric step function estimator of survival?

I am working with a specific type of data where the non-parametric step-function estimator of the survival function is underestimating the true survival function for small sample sizes yet I can prove ...
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