Questions tagged [sampling]
The sampling tag has no usage guidance.
185
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statistical analysis of two data sets have different number of sampling
I have two datasets from my statistical fault analysis: one with 400 iterations and another with 500 iterations. Both datasets have the same maximum and minimum values, and some data points are ...
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Why is 0.7, in general, the default value of temperature for LLMs?
I have recently read through a lot of documentation and articles about Large Language Models (LLMs), and I have come to the conclusion that 0.7 is, most of the time, the default value for the ...
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Training Biased/Uneven Categorical Data with CatBoost, Unbalanced/Unseen Categories Handling
Summary:
I am training a discount eligibility model where the dataset represents historical data for products where people availed discounts based on simple features like product category, discount ...
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how to evaluate a model on our data when the model is imported from a library and thus not trained by us?
The company I work for has deployed a trained rule-based sentiment analyzer model vader to make predictions on customer's attitude. We import the model from nltk library directly, so we didn't train ...
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Calculating an integral with as few grid points as possible
Suppose I have a function $f\colon [0,1] \to \mathbb{R}$ which is maybe continuous (it's at least in $L^1$).
I have a sample of $N$ points $\{x_i\}$ taken from the domain $[0,1]$ randomly from some ...
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Comparing Models based on two different sample sets of a single data set
I want to compare the performance of two different ML models like M1 and M2. I have a very huge data set and having two different downsampling of this data set, call them S1 and S2. Can I compare the ...
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21
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How to refine sampling of points via averaging?
I am working with a generative model which is generating points that are less accurate than I would like. I have strong reason to believe the errors should average out along a particular axis (to give ...
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14
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Ranking 10^10 random strings
I would like to model a biological process that starts from a pool of 10^10+ random strings (a string can be 6-24 letters long; the alphabet contains 20 letters, say A-T) and I want to order those ...
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15
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Appropriate sample size for prediction algorithm
Our study aims to develop a Random Forest algorithm to predict the incidence of suicidal thoughts, after one year, based on the responses given to four surveys at baseline (time-1). Each survey ...
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29
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Question about collapsing variable and oversampling minority classes
i have imbalanced data consisting of nine classes, and i am planning to collapse them into two classes. i performed stratified (proportionate) sampling between test, validation, and training sets ...
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12
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Normalizing flows with truth values?
I am interested in using normalizing flows to map to a certain distribution of points, but I want to make sure that the distribution is not just of the correct general shape, but also that specific ...
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What is the best way to apply Bootstapped weights to data for statistical analysis?
I'm working with a survey dataset for some statistical analysis. My issue is that there are weight columns, but I have no idea how they did the weighting or what the weights are for. I'm not familiar ...
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Group or find associations and orderings for elements that appear in different samples (analyzing examples of input files for undocumented code)
I'm trying to understand and use a physics simulation code that was written decades ago. It uses input files that have their origins in stacks of punch cards as input. In other words each line is a ...
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22
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Generate new sample with higher variance, covariance from empirical distribution
I am analyzing the joint distribution of three continuous variables. I would like to sample from the joint distribution of these three variables, which is straight forward. However, I also want to ...
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44
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Is Logistic Regression possible using a Convenience Sample?
I've collected some survey data on homeless individuals, surveying their drug use, education level, age, gender etc. I hope to run a logistic regression to see how impactful homelessness (+other ...
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90
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Understanding bootstrapping in bias variance decomposition
I was going through bias and variance tradeoff article and it makes use of bias_variance_decomp function from mlxtend library. ...
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43
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Determining the information loss due to undersampling
I have an image dataset that I need to segment into directories (train, validation and test) using ImageDataGenerator in TensorFlow/Keras. The dataset is highly imbalanced:
For this I have decided to ...
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402
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Creating a dataframe using roll-forward window on multivariate time series
Based on the simplifed sample dataframe
...
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58
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Strategy to choose maximum value from an unknown array of n numbers
Suppose you have an array of n normally distributed numbers whose values are initially unknown(and the probability parameters are unknown too). You must choose one number and you want it to have ...
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64
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class label is less than 1 percent in classification problem
I am working on a ML problem where one class label is very less than even 1 percent. i.e 0.0002%
I have tried undersampling, oversampling, SMOTE but the results are not satisfactory on the model.
I ...
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3
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184
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Timing of applying random oversampling on the dataset
I tried to learn classification using machine learning algorithms. I went through Breast Cancer - EDA, Balancing and ML the notebook. In this notebook ...
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90
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How to subset a 'representative sample'?
Sometimes I need to subsample a dataset for my analysis/modelling. What are the strategies/things to check for to select a subsample that's representative of the entire sample? And in addition, how to ...
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Why we do random sampling when we select the training set?
The usual workflow when building a machine learning model starts with random splitting the data set into training and test set.
What I can't understand is why we do this. For example lets say we have ...
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Data collection after the model is built and deployed
I have built a machine learning model which predicts whether a customer will buy a product or not. The model performs well on cross validation tests. Now, I will deploy it in production to recommend ...
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Scikit Learn Random Forest Classifier Hyperparameter Min Target Sample Size
From reading the docs on Scikit Learn, I haven't been able to find an answer, but does anyone know if there is a way to specify to always include a specific number out of the max sample size of ...
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43
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Interrupted Time Series with Unevenly Distributed Samples
I'm working on causal inference using Interrupted Time Series Design.
I have multiple samples per day and am selecting my analysis bandwidth based on pre-treatment RMSE on leave-on-out cross ...
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21
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How can I distribute samples optimally to fit a model?
I'm trying to fit a model to a low number (~5-10) of data points. I might be able to suggest the optimal distribution of the data points beforehand, knowing a bit about the data and the model I ...
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35
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Recommended number of features for regression problem
In the following link the answer recommends a feauture amount of N/3 for regression (or it is quoted).
Where N corresponds to the sample size:
How many features to sample using Random Forests
Is there ...
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3
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96
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What could go wrong if I sample before classification?
I have a million entries in a table that I can use to train a binary classifier. Only 30 thousand of them are positive. Is there anything fundamentally wrong with selecting around 30 thousand negative ...
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34
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Sampling a data based on average and variance of another data
I have a set of textual datasets that have the following average and variance tokens lengths:
...
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565
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Is it always appropriate to use SMOTE in an imbalanced multiclass dataset?
Is it good practice to always use SMOTE and random undersampling in an imbalanced multiclass dataset or are there exceptions? In context, I am using a traditional machine learning model (SVC) for ...
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32
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Compare standard deviations in different samples?
I have some data which you can group based on different variables.
I know how to test if they have significantly different means. But what the deviation inside the samples?
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197
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How create a representative small subset from a huge dataset, for local development?
I have a time series problem and the dataset I'm using is rather huge. Around 100GB. For local development I'm trying to subset this into a very small batch around 50MB, just to make sure unit tests ...
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Stratified sampling - use of proxy variable
For splitting of the data into train/test/val I use stratified sampling. Is it appropriate to define strata using information extracted from the dataset? E.g. use machine-learning to model proxy ...
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Sampling Technique for mixed data type
I am looking for a very specific sampling technique which pertains to a very large dataset with mixed data type i.e, I have categorical as well as continous variables and want to have a sample that ...
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Discrepency in 25th percentile of standard deviation calculation
For each of the following sample sizes [3,5,7,9], I Need to calculate the 25th percentile of the standard deviation for the values sampled. And the total number of trials will be 10.
DataFrame
...
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Is it right method to remove instances that are hard to predict before train test split?
In a binary classification problem,
I have a slightly unbalanced medical dataset with class distribution:
0:5600, 1:1500
0 without a problem and 1 with a problem.
I tried many pipelines, automls, and ...
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42
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Multiclass data redistribution
I want to redistribute the data in classes according to new proportions and wonder what is the optimal way to do it.
For example I have
...
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499
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Negative sampling for graph representation learning
I was watching a lecture about graph representation learning (from here) and got a little bit confusing about how they define the negative samping procedure.
In the presentation J. Leskovec briefly ...
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Is there a relationship between learning rate and training set size?
I have a large dataset to use for training a Neural Network model. However, I don't have enough resources to do a proper hyperparameters tuning on the whole dataset. Therefore, my idea is to tune the ...
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Reducing (Variance) | the gap between my weights
I have ML ready samples. And each sample has a weight.
The weights distribute between [0-1]
My problem arise because there are a lot of samples which are ...
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785
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Controlling the sampling from Variational AutoEncoder (VAE)
Suppose a Variational Autoencoder (VAE) is trained with mnist data. To sample, one draws from normal distribution. My question is: suppose I am interested in generating only 1s and no other digits. ...
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617
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Subsampling the “right” amout of data to train an ML model
I am training a machine learning model (i.e., a classifier) on a large dataset. I know that I can get the same results using less data (about 30%) but I would like to avoid the trial and error process ...
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46
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Stratification sampling of a json Array [closed]
I have a json array file that i need to create a smaller sample of for testing purposes. A sample of the file looks like:
...
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392
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What is the proper proportion for train and test set for classification system?
I have recently googled the best proportion for training and test set for classifying physiological data between normal and abnormal. Much of the source tells that the proportion should be 70:30 or 80:...
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Undersampling for credit card fraud detection before or after Train/Test Split
I have a credit card dataset with 98% transactions are Non-Fraud and 2% are fraud.
I have been trying to undersample the majotrity class before train and test split and get very good recall and ...
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386
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Sampling n data points from high dimensional data
I have some face images(of a single person), which I ran through an embedding generator to get 128-dimensional embedding.
I have 1000 such embedding (shape of the dataset (1000, 128)). I have a ...
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How to bin a distribution data reported with different frequencies ( salaries ), showing mixed linearity and non-linearity?
I am researching on pay-scales, and wish to receive advise to treat data of salaries.
Objective
My interest is to approximate the salary corresponding to different hierarchical levels in an ...
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Sampling items from a population of subpopulations
I have a population of $n$ items to label and a budget to label only $m$ ($m << n$) of them before training. The population can be partitioned into subpopulations, recursively. In other words, ...
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Recover a integer valued function with *-learning
I have the following problem. From a technical model we have a function $f(n,p)$ approximating its runtime. The function $f$ which maps
$$
f: \mathbb{N} \times \mathbb{P} \to \mathbb{R}_{+}
$$
where $...
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