Questions tagged [sampling]

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15 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: ...
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1answer
29 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: ...
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11 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.
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22 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 ...
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25 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 $\...
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9 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 (...
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52 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 ...
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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 $...
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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 ...
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1answer
16 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 ...
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2answers
209 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 ...
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1answer
274 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, ...
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1answer
23 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 ...
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1answer
54 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?
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1answer
342 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 (...
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40 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 ...
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1answer
43 views

How to do k-folds in python whilst splitting into 3 sets?

Consider the following data: ...
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23 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 ...
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70 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 ...
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1answer
76 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 ...
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1answer
573 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 ...
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109 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 ...
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0answers
12 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 ...
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1answer
266 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 ...
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2answers
59 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 ...
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1answer
18 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 ...
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17 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 ...
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1answer
74 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: ...
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1answer
89 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 ...
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3answers
426 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 ...
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1answer
42 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
136 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?
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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 ...
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1answer
222 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
201 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 ...
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0answers
36 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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1answer
51 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
156 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. ...
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1answer
567 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 ...
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1answer
1k 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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1answer
60 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
75 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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26 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
38 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
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1answer
117 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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139 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
303 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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1answer
3k 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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1answer
110 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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2answers
16k 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 ...