Questions tagged [sampling]
The sampling tag has no usage guidance.
103
questions
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0answers
14 views
Bayesian network in Python: both construction and sampling
For a project, I need to create synthetic categorical data containing specific dependencies between the attributes. This can be done by sampling from a pre-defined Bayesian Network. After some ...
3
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2answers
27 views
Does Sampling size matters in Multi classification Model
I am working on a multi class classification model where few of the class are with less data compare to other classes.
I used random sampling technique to create a sample from the population keeping ...
1
vote
1answer
16 views
Adjust predicted probability after smote
i have an imbalance data set and I used smote to oversample the minority class and undersample the majority class. now, I want to check the test AUC using predict_proba of the model.
I have two ...
0
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0answers
10 views
What are accepted methods for sampling data that will be sampled?
I have a very large (TBs) dataset that I need to sample to a smaller more manageable size. However, I know that the sample will also be sampled to get statistics after it leaves my hands. Are there ...
0
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1answer
35 views
Correctly evaluate model with oversampling and cross-validation
I'm dealing with a classic case of dataset with binary imbalanced target (event 3%, non event 97%). My idea is to apply some sort of sampling (over/under, SMOTE etc.) to address the issue.
As I see, ...
0
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0answers
12 views
How to get a sample/summarised data from a table for data analysis?
I am currently working on EHR data analysis. I have a measurement table which has information lab test information of the patients. However the record count is huge. These are table count in my EHR ...
0
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2answers
65 views
How to find whether a dataset is blanced or imbalanced?
I have few dataset to experiment classification(Multi-class). These datasets are about 400GB. I wanted to know whether the dataset is balanced or imbalanced. How to know that dataset is balance or ...
0
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0answers
10 views
How to compare two 3d vectors, which differ by sampling start time
I have some curve, assume it is given by some function f(x(t),y(t)).
This 3d curve is sampled twice:
t=0..99, no noise. This ...
1
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0answers
20 views
Training data requirements for NLP models
Are there general guidelines for how much data is required for natural language processing (NLP) classification models? I understand this may depend on the text quality, text length, how accurate the ...
2
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3answers
46 views
Highly Imbalanced dataset fro classes more than 200
I have a text dataset where I need to train a classifier to classify the titles into categories. The dataset shape is more than 575000. There are 256 target classes here. The problem is the dataset is ...
3
votes
3answers
104 views
Why did sampling boost the performance of my model?
I have an imbalanced dataset with 88 positive samples and 128575 negative samples. I was reluctant to over/undersample the data since it's a biological dataset and I didn't want to introduce synthetic ...
0
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0answers
14 views
How to compare two samples taken from the same dataset?
I have data from almost 10 different sensors and was trying to downsample the data. I tried the reservoir algorithm and the uniform sampling and the random sampling techniques. I was wondering how I ...
1
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0answers
14 views
Sampling trying to keep as much multivariate variance as possible
We can use PCA for dimensionality reduction, but at the cost of getting "uninterpretable" variables.
I was thinking if anyone considered a sampling technique that would try to aim keeping as much of ...
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0answers
17 views
Sampling user IDs from a large data set for retention analysis
Let's say I have a data set with user identities and their respective orders, of the form:
...
0
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1answer
138 views
train_test_split ValueError: Input contains NaN
I tried to do a stratified sampling by way of train_test_split in order to save myself some trouble later. So I wrote the following lines:
...
0
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0answers
23 views
Can we have a sampled sigmoid instead of softmax?
Thh solution proposed here: is for softmax negative sampling. How do we do a sigmoid negative sampling? I couldnt find a corresponding 'tf.nn.sampled_sigmoid_loss' function.
0
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1answer
36 views
Random forest with zero precision for unbalanced test data
Apologies if this is a basic question.
I have a very unbalanced dataset in which the records are labelled by one of two classes, class1 (negative class) and class2 (positive class):
class 1: 1.5 ...
1
vote
0answers
31 views
Which machine learning methods can be used to address MonteCarlo sampling problems?
I would like to pick the brains of machine learning experts here to point me to a branch of machine learning technique that is best suited for my context. I would like to estimate the average value $\...
0
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0answers
13 views
how to read all my audio file from one directory and store the outputs for all the files in one variable (matrix)
I would like to run the program over all the samples of audio file and store the output in a single variable. For example, if my folder is "audio" inside that I have 10 audio files (...
1
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0answers
56 views
Variable Importance changes with oversampling
I am currently using Xgboost for a binary classification problem with highly imbalanced data in R.
I have used oversampling to train the model. This worked well, now however it comes to measuring ...
0
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0answers
6 views
Balanced sampling of a dataset on 2 or more regression targets
I'm given a dataset with 2 regression labels, $y_1$ and $y_2$ in the range $[0, 1]$. My goal is to sample from this dataset such that I come as close as possible to a uniform distribution for both $...
0
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0answers
10 views
How to deal with features of majorly different sampling rates in neural networks?
Consider you have neural network with two different time series as input features. The first is sampled once every second. The second is sampled once every minute. I want the network to perform ...
0
votes
1answer
22 views
Forcing class imbalance to mirror the target data
I'm trying to do binary classification on some data, my source data has a class split of 40% A / 60% B while my target data has a split of 70% A / 30% B.
Is it a worthwhile strategy to use SMOTE to ...
3
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2answers
215 views
Is it OK to use the testing sample to compare algorithms?
I'm working on a little project where my dataset have 6k lines and around 300 features, with a simple binary outcome.
Since I'm still learning ML, I want to try all the algorithms I can manage to ...
3
votes
1answer
545 views
SVM SMOTE fit_resample() function runs forever with no result
Problem
fit_resample(X,y)
is taking too long to complete execution for 2million rows.
Dataset specifications
I have a labeled dataset about network features, ...
0
votes
1answer
41 views
Sampling Big Data for Predictive Analytics in Python
In practice, how does one go about sampling a from big data set (eg. +/- 50 million distinct observations) to perform ML using Python?
Most non-parametric models (e.g., SVM, ensemble models) start to ...
0
votes
1answer
98 views
Choosing sample from large dataset?
How to choose sample from a large dataset such that each unique row from the dataset is selected at least once in the sample? Is there a way of doing this in python?
1
vote
1answer
795 views
Using SMOTE for Synthetic Data generation to improve performance on unbalanced data
I presently have a dataset with 21392 samples, of which, 16948 belong to the majority class (class A) and the remaining 4444 belong to the minority class (class B). I am presently using SMOTE (...
0
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0answers
45 views
Cross-validation and out-of-bag bootstrap applications
I have a question regarding steps on which a specific resample method should be used in general.
As far as I know:
out-of-bag bootstrap is the resample method with replacement, which has lower ...
1
vote
1answer
97 views
3
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0answers
26 views
Sub-sampling so that sample statistics match population statistics
I want to investigate the impact of various testing strategies on a product. Let's say chairs. I start with 500 random chairs that I've picked up from garage/yard sales. They come in all shapes and ...
0
votes
0answers
89 views
Why am I getting such a low precision after performing oversampling and undersampling?
I am performing fraud analysis on credit card fraud committed dataset.
I am performing oversampling by .sample(oversampled_class_size) and undersampling by .sample(undersampled_class_size).
I am ...
2
votes
1answer
103 views
In Machine Learning, what is the point of using stratified sampling in selecting test set data?
I am currently learning machine learning via this book "Hands-On Machine Learning with Sci-kit learn and Tensorflow" by Aurelien Geron.
In page 76 and 77, the author talks about using stratified ...
6
votes
1answer
1k views
Cross validation for highly imbalanced data with undersampling
In my problem, I am dealing with a highly imbalanced data set, say for every positive class there are 10000 negative one. A normal starting method to train a model is to undersample the data. In this ...
1
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0answers
135 views
Relation between using stratify and class weights for imbalanced classes
I'm working on a multi-class classification problem where the classes are imbalanced (70:25:5).
Train-Test Split
...
0
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0answers
13 views
sample n unique items from dataset
I have a dataset that has N of different unique items and each item appears Ai times (every item appears different times).
This is mean that I have the ...
2
votes
1answer
390 views
Oversampling before Cross-Validation, is it a problem?
I have a multi-class classification problem to solve which is highly imbalanced. Obviously I'm doing oversampling, but I'm doing cross-validation with the over-sampled dataset, as a result of which I ...
0
votes
2answers
67 views
Disadvantages of hyperparameter tuning on a random sample of dataset
I often work with very large datasets where it would be impractical to check all relevant combinations of hyperparameters when constructing a machine learning model. I'm considering randomly sampling ...
0
votes
1answer
20 views
Optimal proportion between the amount of Class = 1 and the amount of Class = 0?
I am quite new machine learning methods, so I may not write proper technical formulas.
My question is about the optimal proportion between sample size in Class = 1 ...
1
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0answers
19 views
SmoteBoost: Should SMOTE be ran individually for each iteration/tree in the boosting?
As per the paper on SmoteBoost, SMOTE is ran for each iteration of the boosting, generating N samples, which are further added to the original training data and the weight distribution of the ...
0
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1answer
94 views
How can I extract bootstrap generated datasets into individual dataframes?
I am having a bit of trouble understanding Bootstrapping and what/how I can manipulate the bootstrap created dataset.
This is all in R
My original dataset is structured like this:
...
1
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1answer
90 views
A few questions to understand a random forest blog [closed]
I'm trying to understand a nice blog on the trade-off between sensitivity versus specificity with the random forest and logistic regression models. I have a few questions:
1) The blog used a 10 fold ...
0
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3answers
663 views
Downsampling and class ratios
My target variable is whether an application is accepted or not. It is a highly imbalanced target with 98.5% of applications accepted. I am unclear about the concept of downsampling. If I were to ...
0
votes
1answer
53 views
Generating a set of different scenarios based on some initial observations
I have a in my hands 3 different time series which model 3 different scenarios (base, downside, upside). Every of this time-series depends on a set of 11 different attributes, which take values for ...
1
vote
1answer
316 views
Difference between bagging and boosting
Can anyone explain me the basic difference between bagging and boosting and which technique can be used in which scenario?
2
votes
1answer
2k views
How to correctly perform data sampling for train/test split in multi-label dataset?
Problem statement
I have a text multi-label classification dataset, and I've found a problem with the dataset sampling.
I'm facing two different strategies. The first one consists in preprocessing ...
0
votes
1answer
282 views
Downsampling the dataset to create balanced dataset for neural models
I have a classification dataset with 10k instances and 4 classes and it is unbalanced. 7000 of it belongs to first class, 2000 of it belongs to second 800 of it belongs to third class and remaining ...
1
vote
0answers
252 views
Sample size equation for multi-class distribution
I have a large (k>15) number of potential classes involved in a text classification problem, and don't know the true distribution of these classes in the ...
1
vote
0answers
51 views
Downsample GPS track
I am working with GPS track files (list of X and Y coordinates). I have tracks with a high sampling rate and want to downsample the track for easier handling.
The obvious way would be to create a new ...
1
vote
1answer
56 views
I have limited samples for one class, unlimited samples for the other class. Need to balance?
I want my machine learning algorithm to learn the difference between two classes, actually picture of X or ...
