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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Calculating Shannon Information of Data Augmentation Strategies

I recently caught Andrew Ng's 2021 talk on MLOps (MLOps: From Model-centric to Data-centric AI). At 26:40, he talks about calculating the effectiveness of cleaning your data (training examples) vs. ...
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Neural networks - what is the point of having sigmoid activation function $\sigma(.)$ AND sigmoid g(t)?

Just so we're all on the same page, this is the classic neural network set up as I understand it: $$z_j=\sigma(\alpha_0j+\alpha_j^T x)$$ $$t=\beta_0+\Sigma^k_{j=1}\beta_j z_j)=\beta_0+\beta^Tx $$ $$ y=...
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Using Variance of time series as input feature for time series clustering

I have a time series dataset, it is a data frame with 2000 rows and 1000 columns. Each rows is for one specific id and has a unique pattern. I want to clustering this data into multiple classes. Let ...
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Hyper parameters to tune in Bayesian network?

In tree based models and neural networks, we can optimised the models by tuning the hyper parameters(such as: learning rate, number of neutrons.. etc). Is there a hyper parameters to tune in Bayesian ...
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RFM Customer segmentation - Why Avg monetary value instead of total monetary value?

I am trying to segment our customers based on their purchase data. And I came to know about the RFM technique (Recency, Frequency and Monetary) through these posts here, here etc. Recency - How ...
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Modeling demand distribution with selling constrained by stock

I'm working as a Data Scientist on a project where we are supposed to determine how many pieces of stock a certain retailer should have in each of its physical stores. The stock should be set on the ...
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1 vote
1 answer
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Can I convert a typical reinforcement learning problem to a supervised learning problem?

I'm not sure if I've understood correctly the whole point of reinforcement learning. In my point of view, the whole goal of RL is learning a policy that maps states to actions. Let us suppose that I ...
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How does attention add expressive power to encoder-decoders?

I am learning about the attention mechanism for the first time, and the context in which it has been introduced (watching Lecture 8 of Stanford's CS224N) is that of language translation using seq2seq ...
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Multiclass classification on top of given probability distribution from previous model

I have a multiclass classification problem that has multiple steps. Firstly, I am given a probability distribution over the classes by a base model for each sample. The task is to create a new model ...
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Neural networks - calculating output manually if $x_1=x_2=0$ . Should this be easy to do?

This is a problem question I'm trying to make sure I understand from a past paper (with no solutions). The R output is below. ...
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Interpreting Shapley Values on Breast Cancer

I was analyzing Shapley Values on the Wisconsin breast cancer data set (binary classification). I applied it on Random Forest and on Ridge and Lasso Regression. However the summary plot seems to be ...
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2 votes
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1D cluster - Jenks optimization - Finding optimal number

I have a sample data variable shown below score 10, 11, 12, 90, 95, 97, 38, 37, 35 Instead of applying/binning data based on ...
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Assessing importance of interactions between categorical features

The issues with using feature_importance of models such as XGBoost, or even using packages like SHAP or ELI5, is that the results are displayed in a way that doesn'...
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2 votes
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Does using grid search for hyperparemeters make test set redundant?

The purpose of train, validate and test data splits addresses the issue of data leakage when tuning for the model's hyperparameters. Does Grid Search then eliminates the need for test set? Because ...
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"Consensus" on Analyzing Mixed Continuous and Categorical Data in the field of Statistics? [closed]

I have been trying to determine the popular "consensus" as to how mixed continuous and categorical data (e.g. a dataset that has variables on income and gender) is generally analyzed in the ...
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How can I train Mixture Density Neural Network? [closed]

I am learning Mixture Density Neural Network but it looks different from the usual neural network for regression problem. As far as I have understood from what I have read on the Internet, it gives ...
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Implementing kernel alignment for SVM algorithms

I am trying to understand and re-implement the results from Table 2 in the first Kernel-Target Alignment paper. The task that is being done is a simple classification task using an SVM with RBF ...
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How do you approach a CNN problem?

I wanna create a machine learning model based on a region based CNN architecture (either RCNN, Fast RCNN or Faster RCNN). As an framework I wanna use Pytorch. I made a image containing apples and ...
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How to manage out of sample data in the long run?

For example, you are interested in testing an investment strategy and there is data from 1950 to 2022. So you split it into a train and test set, say 1950-2000 and 2000-2022. Then you build your model ...
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2 votes
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Is it possible to deal with datasets of graphs with different number of nodes in graph nural networks?

I'm dealing with a graph classification problem. In my dataset, each graph has som specific number of nodes. The number of nodes has a range of 1-1000 nodes. At inference time (after training), the ...
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Is there a way of comparing/ evaluating a machine learning model with a recurrent model with a statistical significance test?

Comparing two different machine learning models (to assess if the difference between the mean performance is real or not using P-value and t-Statistic) is possible and strait forward in Python. ...
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1 vote
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Preforming backward pass in network with batch normalization

if we have a network model like this: input_layer (linear) [0] hidden_layer (linear) [1] batchnorm1d() [2] output_layer(linear) [3] When preforming a backward pass would you calculate $$\delta^3$$ ...
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2 votes
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XGBoost interpretation of the plot in R

I am applying XGBoost implementation in R on the data with 9 columns. After training the model, I tried to plot the "multiple-in-one" tree using the xgb.plot.multi.trees() function with the ...
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Advarsarial autoencoder loss function - Using MSE and BCE both

I came across this implementation of AAE on financial data to detect anomalies https://github.com/GitiHubi/deepAD/blob/master/KDD_2019_Lab.ipynb. In here for the VAE part of AAE, the author is using ...
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3 votes
1 answer
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What is meaning of such notations in general?

I read a notation on a paper about statistics and machine learning ("High-dimensional Asymptotics of Langevin Dynamics in Spiked Matrix Models" by Tengyuan Liang, Subhabrata Sen, and Pragya ...
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As alpha increases with a tree model, what happens to its flexibility? [closed]

Suppose there is a tree model with $\alpha = 0$ and another with $\alpha = 1$. What happens to the flexibility as the value increases from $0$ to $1$? I suspect that it will have lower flexibility and ...
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Is there a way of comparing/ evaluating a machine learning model with a recurrent model like LSTM with a statistical significance test? [closed]

Comparing two different machine learning models (to assess if the difference between mean performance is real or not using P-value and t-Statistic) is possible and strait forward in Python. Generally, ...
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0 votes
1 answer
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how to generate data from beta-Liouville distribution?

I want to test my model following the beta liouville distribution, so as a synthetic data, I need generate data from this distribution. can anyone mathematically tell me how to calculate it? this is ...
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Text Extraction and Text recognition

Starting from text I'd like to be able to identify specific informations. Example : Input texts : "The invoice number is 18", "Inv : 75", "Inv N. : 84" Identified invoice ...
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Improve the perfomance of the deep learning model based on the train and validate loss curve [duplicate]

I have a deep learning model and the following is the loss on the train and validate data. The prediction for my model is not good. Do you know what I should do for my model to have a better results? ...
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Combining text and numerical in ML model [closed]

Have anyone implemented this approach? https://towardsdatascience.com/combining-numerical-and-text-features-in-deep-neural-networks-e91f0237eea4. If so, mind sharing? I don't understand it. Thanks.
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YOLO v2 loss function

I'm trying to understand (and implement) the YOLOv2 loss function, which is not given explicitly in the original paper. There are several posts on this topic, but quite a few seem to confuse the ...
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Post analysis using raw data or SHAP values in Machine learning

Let's say I have SHAP value returned in dataframe for input variables like below ...
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3 votes
1 answer
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Usefulness of KS tests and other similar distribution comparing tests

I am working on a machine learning binary classification problem. I have an outcome variable status called as loan paid and <...
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R squared of subgroups

I am trying to predict a value using a linear regression, and I get an R squared of 0.63. My data is composed of 5 different groups (each with different ...
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Should we care about correlations between model performances while we search for the best model?

When we fit (train) a regression model, we usually pick the best performing model (for example the one which gives the smallest RMSE). By doing this we do not take into account the correlation between ...
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Dependent variable standardization in neural networks

I am using a multilayer perceptron model to predict urban temperatures. I have standardized the independent variables before training the model. However, I have not standardized the dependent variable....
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Can a kernel be constructed from any arbitrary bivariate function?

For a kernel $\kappa(x_i, x_j)$ to be considered valid, it must be symmetric and have a positive semidefinite gram matrix for any set of points $\{x_1,...,x_n\}$ (ref: page 4 of http://www.cs.berkeley....
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1 vote
1 answer
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Significance test for comparing different 10-fold cross-validated Machine Learning Regressions

Is there a recommended significance test for comparing different 10-fold cross validated regressions? For instance, I want to compare the performance of LASSO against Random Forest for my dataset. ...
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Why PR score is down when balanced accuracy is good?

I just read this discussion here and here. I have a dataset of 977 records where class proportion is 77:23. My balanced accuracy is 75.5, ...
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How can I get the Binary Cross Entropy from the Cross Entropy function for GANs

I got the definition of log-likelihood by Goodfellow's Deep Learning book: \begin{equation} \label{eq:loglikelihood} \theta_{ML} = {argmax}\sum_{i=1}^{m} \log p_{model}(x_i; \theta). \end{...
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2 votes
2 answers
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How is AUC helpful when we only need one threshold of a classifier

AUC is a summation of performance at different thresholds, but do we only care about a good performance at one threshold? Imagine a classifier with a low ROC but shots up at point of a low FP and high ...
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1 vote
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Predicting Value in multi-dimension data

I need help with my data, I mix 2 different powder materials to get one powder with a specific quality number. So, this resulted in quality numbers depending on 4 features, the quality of materials 1 ...
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Feature preprocessing (standardize and normalize) and variable independence

I can't find clarity on this question so here goes: Suppose I have 3 features, x, y, z. I know x and ...
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Statistical test when comparing oversampling to no oversampling on ANN

I use 70% of the dataset for training and 30% for testing. I use oversampling on the training dataset with an ANN. I use the test dataset on my ANN and look at the performance of oversampling against ...
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Area Under Precision-Recall and Area Under ROC curve for different amount of observations

I am doing a research and thus comparing some algorithms for binary classification. Worth to mention that, the data set is highly imbalanced i.e., the minority class is only 0.2%. Notation: Area Under ...
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Can somebody help me understand the sentences in more readable expresions?

I was reading a paper about "bayesSimIG" and I have problem in understand the following paragraph.I have read it many times and did a lot of research for it and have understood what each ...
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95% confidence interval for the goodness of fit scores in regression

I see the following computation available online in classification setting. ...
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2 votes
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Are there smooth analytical penalty on leaves sizes for decision trees?

In a decision tree, when we search for an optimal split, we usually minimize root mean square deviation (RMSE). In addition to that we might forbid splits that give too small leaves (for example a ...
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2 votes
1 answer
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How to force splits in decision tree to be distributed uniformelly in case of no dependency on feature?

I have targets ordered by a feature. I want to find a single split that minimizes a squared deviation (RMSE). For example, I have 100 values (targets) and it might be the case that, if I split them as ...
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