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Questions tagged [sampling]

The tag has no usage guidance.

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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 ...
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0answers
21 views

Address Class-Imbalance Dataset using Sampling Techniques on Train, Test Dataset or Both?

I am dealing with an unbalanced dataset and I'm really confused if I should apply sampling techniques, like downsampling, smote, upsampling etc., on the train, test dataset or both? The minority ...
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0answers
29 views

R package used for Sampling techinque such as over,Under, both, SMOTE to balance multi-class target variable

given below is an example of my data frame. Target Class 0 0 0 0 0 0 0 1 1 2 3 3 4 1 3 4 Now my target class ...
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0answers
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Survey results analysis for a social media app - stratified sampling?

I found this question on Glassdoor and was hoping to get some input on it... A user satisfaction survey was conducted for two groups for a social media platform. Assume large sample sizes. Group 1 ...
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0answers
19 views

Resampling to get equal predictive power per observation

This is probably a thing I am just not searching for correctly, but essentially my idea is this: given some machine learning classification $C$ based on an input dataset $D$, certain observations in $...
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1answer
25 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 ...
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1answer
19 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
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1answer
106 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
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1answer
27 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 ...
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0answers
25 views

Discard overlapping data sets in classifications

I think I have overlapping in the training data sets. Because if I use the training set that of one label as the test documents, it will output other labels as classifications. I'm thinking of ...
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0answers
32 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
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0answers
13 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 ...
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2answers
26 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 ...
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0answers
15 views

How to assign prior probabilities while using Gaussian Process bandits?

I implemented the work based on Srinivas, "Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design" and it looks like my code is working. My problem is that when the ...
2
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0answers
62 views

Oversampling for multi-class neural net

Does this make sense or do I have no idea what I'm doing? I want to train a model that takes a sentence and outputs a binary multi-class vector of size $K$ where each dimension is a question class. ...
2
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1answer
140 views

Gumbel Softmax vs Vanilla Softmax for GAN training

When training a GAN for text generation, i have seen many people feeding the gumbel-softmax from the generator output and feed into the discriminator. This is to bypass the problem of having to sample ...
2
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1answer
235 views

Why is sampling useful in machine learning?

I have met that question online and I wanted to know where sampling can simulate complex processes and why? Why is sampling useful in machine learning? Sampling can increase the accuracy of the ...
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0answers
16 views

Active learning methods and insight extraction

I am looking for active learning strategies, I have a data set, I train over it, and now need to use the model on the usage site itself, there I would like to have a clever strategy for selecting ...
1
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1answer
38 views

Overfitted model produces similar AUC on test set, so which model do I go with?

I was trying to compare the effect of running GridSearchCV on a dataset which was oversampled prior and oversampled after the training folds are selected. The oversampling approach I used was random ...
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1answer
29 views

Generating ordinal data

I would like to generate synthetic data which are ordinal, i.e. ordered, in Python. But how would I do this? What are the differences in generating ordinal data vs categorical data? I'm reading the ...
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0answers
20 views

Search Query Sample Size Determination for validation set

While designing a search system, which searches in N identifiable categories, how many search queries does one need in each category to validate the target metric (DCG) scores accurately (balanced ...
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1answer
33 views

R programming (Jackknife) [closed]

Hi I would like to ask how to sample out 50 instances from 150 instances of iris data by using Jackknife. Is it possible? Thanks in advance
1
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1answer
78 views

Right ML mode and metric to minimize FN and FP on imbalanced dataset

So I have a dataset in which I have to predict class binary label (1 or 0), the problem, out of 120k data points, only 200 have the label '1'. the aim is to minimize FN and FP. Which ML model should ...
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0answers
73 views

Orange: Group samples by a “splitting” feature for cross-validation?

I need to split my datasets based on my own feature column in order to hold together certain data rows (e.g. from one patient, or compound) for cross-validation (CV). In Orange 3.11, Test&Score ...
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2answers
142 views

Working with audio data with different sample rates in Tensorflow

I am trying to implement (as a toy project) some aspects of speech recognition in Tensorflow. The audio files I want to use as training and test data have different sample rates (16, 20, 44 and 44.1 ...
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0answers
75 views

RBM, Gibbs Sampling, and Real-Valued Data

I am admittedly very new to Restricted Boltzmann Machines (RBM) and have been toying with the idea of using RBM to generate samples from the underlying distribution of ...
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0answers
24 views

Importance Sampling for Minibatches

I have a question regarding to importance sampling of subsets. Given a Dataset of 943 by 1682, I need to create subsets of the data and provide each subset with a weight v_i. Hence, my first question ...
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1answer
1k views

Keras negative sampling with custom layer

I am trying to implement negative sampling in Keras. I wrote the following code that just compute the loss and I plan to add an additional output for the logits once I get it up and running. Here is ...
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0answers
9 views

What is the process from Data to samples/vectors as Distributions to the NN

I have downloaded the citeulike dataset and extracted 5 features for the same. Creating text information from the tags and training data using the user,item data. I want to know the next steps for ...
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0answers
27 views

How to Compare resampling methods (Dolan-More Curve)

I read about comparing data resampling methods in this publication: https://arxiv.org/abs/1707.03905 In it, they used the Dolan-More Curve. I have been trying to read more about it but can't find ...
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1answer
99 views

Exploration vs exploitation tradeoff to find a price that maximizes revenue

Is there a practical strategy that can learn to price a product optimally? Right now I have the following arbitrary hill-climbing algorithm: Run an experiment at starting price ...
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0answers
15 views

How to bootstrap CI for predictions for a particular value in a Lasso regression

I was working on this for a project. The data set is basically a 65000x13 matrix, so there are around 13 predictors to choose from. The first part of the question involves creating a LASSO regression. ...
3
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2answers
9k views

SMOTE and multi class oversampling

I have read that the SMOTE package is implemented for binary classification. In the case of n classes, it creates additional examples for the smallest class. Can I balance all the classes by running ...
2
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1answer
33 views

Stratified Sampling Variable Choice

I am trying to do stratified sampling in R to sample from my data and one of the parameters is group, which takes variable names to sample from keeping same initial distribution of the data set. Is ...
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2answers
783 views

why we need to handle data imbalance?

I need to know why we need to deal with data imbalance. I know how to deal with it and different methods to solve the issue which is by up sampling or down sampling or by using Smote. For example, if ...
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1answer
3k views

How many features to sample using Random Forests

The Wikipedia page which quotes "The Elements of Statistical Learning" says: Typically, for a classification problem with $p$ features, $\lfloor \sqrt{p}\rfloor$ features are used in each split. ...
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0answers
24 views

Resampling a normally distributed dataset for regression problems?

I have a dataset from an operating process having 5 measurements and 1 outcome. All values are normally distributed. When I train a regression model on the dataset it performs good on the majority of ...
2
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1answer
1k views

Which is better: Out of Bag (OOB) or Cross-Validation (CV) error estimates?

I have seen other posts in this forum but didn't find any convincing answer. Random Forest has an another way of tuning hyperparameter via OOB by design. OOB and CV are not the same as OOB error is ...
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2answers
21k views

train_test_split() error: Found input variables with inconsistent numbers of samples

Fairly new to Python but building out my first RF model based on some classification data. I've converted all of the labels into int64 numerical data and loaded into X and Y as a numpy array, but I am ...
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1answer
2k views

K-Fold Cross validation confusion?

I am using K-Fold cross validation to test my trained model.But i was amazed that for every K-fold the accuracy is different.For instance if use 5-K fold ,every fold has different accuracy.So which ...
2
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0answers
58 views

How to randomly sample crops from plain image with points only if crop contains n points inside?

Lets assume I have an image which only has white background and black points, all same size. I need to randomly sample crops with a hardcoded size. The condition is that all the crops need to contain ...
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1answer
93 views

How to Choose a Sample for Multiply Classifiers

I've got a dataset of 1.5 million and am looking to train 7 different classifiers -- for each classifier I have up to 10 classes to predict. The total sample has 20K text features (more if I include ...
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0answers
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Calculate accuracy of crowdsourced responses in realtime

I have a total dataset of, lets say, 1000 Items. Each item will get a response from the crowd. After I get all the 1000 responses, I can sample the dataset to calculate accuracy. Sampling would ...
2
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2answers
141 views

Strategy for dealing with giant sample size

There's a not well-known fact in statistics. That as your sample size increases, more p-values become significant. I'm working with a massive sample size consisting of 3 million samples (~ 1% of the ...
3
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2answers
784 views

Imbalanced dataset: how to deal with test data?

I plan to use many methods to solve the imbalanced dataset problem on the training set. But I couldn't find any paper that describes how they dealt with the test dataset? I assume that they just ...
12
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1answer
7k views

Is stratified sampling necessary (random forest, Python)?

I use Python to run a random forest model on my imbalanced dataset (the target variable was a binary class). When splitting the training and testing dataset, I struggled whether to used stratified ...
3
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2answers
2k views

Cross validation plus oversampling?

I am quite new to machine learning and python as well. I faced an imbalanced dataset and wanna use cross validation and oversamopling like the figure shown. I realised the Python function below ...
0
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1answer
420 views

How to chose training sample for neural network in fraud detection?

I am working on a personal project sponsored by a data scientists, he offered some data sets to me. And I have built a neural network system based on my own theory. But when I tried to train it, I ...
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1answer
47 views

Imbalance in observable data

I am studying the performance, over 10 years, of high school students that enrolled in a school district. My Objective is to make inferences about factors leading to poor performance in school exams. ...
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1answer
85 views

Sample selection through clustering

I have a biased set of samples going into a binary classification sklearn pipeline, white and black samples. It is easy for me to fetch as many black samples as required, while whites are a bit ...