Questions tagged [sampling]

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Imbalanced Data Classification

This is my code with which I tried to attack my imbalanced dataset from here. Now I have 2 questions. When I try to split my data into train/test data and try to run the table() function, I always ...
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11 views

Is there a statistical law that can determine quantity of experiment and predict accuracy of results in my experimental context? [migrated]

I designed a biological protocol A which measures a continuous variable qA for each sample I designed a biological protocol B which measures a continuous variable qB for each same sample as above ...
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55 views

SMOTE for regression

I am looking into upsampling an imbalanced dataset for a regression problem (Numerical target variables) in python. I attached paper and R package that implement SMOTE for regression, can anyone ...
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1answer
36 views

Sampling randomly from pd.DataFrame, but ignoring NaN values

I am trying to sample random values from a dataframe where the NaN values should be ignored, without dropping the entire row or column. My sampling function at the moment looks like this: ...
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1answer
17 views

Over/Under Sampling for Multi-classification

I'm trying to apply xgboost and random forest for over and under sampling For imbalanced data: train shape -> (199991, 23) However, reverse my expectation. ...
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12 views

SuperLearner Cross validation with iid time series

I created a number of ML models in R and I aim at combining them to form an ensemble. I learned about SuperLearner library which cross validates many models and returns the weight to each model in ...
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1answer
107 views

Why gradient boosting uses sampling without replacement?

In Random Forest each tree is built selecting a sample with replacement (bootstrap). And I assumed that Gradient Boosting's trees were selected with the same sampling technique. (@BenReiniger ...
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1answer
20 views

How to resample one dataset to conform to the distribution of another dataset?

I have two datasets with 20 features, but with different feature distributions (DS_A and DS_B). How can I sample the DS_A to make its distribution similar to DS_B, with respect to multiple features?? ...
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1answer
23 views

How to compute modulo of a hash?

Let's say that I have a set of users in my database, that have GUIDs as their IDs. I use xxhash to generate fixed-length hashes for each value, so that I can then ...
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1answer
18 views

Can sampling like SMOTE/UP/DOWN applied on Validation set?

I am trying to predict classification problem. For that I have used Ranger, Xgboost and naive bayes. My Response class is imbalance . 92:8 ratio. My positive Response is only 8% of whole data. ...
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30 views

How to estimate the accuracy on a large dataset?

Given that I have a deep learning model(handover from former colleague). For some reason, the train/dev set was missing. In my situation, I want to classify my dataset into 100 categories. The ...
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22 views

Difference between Gibbs sampling and variational Bayes inference

After reading in blogs and books, I came to the conclusion that Gibbs sampling and variation Bayes are methods for estimating or inference of posterior. Below link described but it's difficult to ...
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29 views

Stratified selection based on the y response creates a bias in information (Berkson's bias)?

I have a database which has individuals in different months and a target variable which indicates wheter an event happened or not, let's say: ...
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1answer
31 views

Is there an algorithm for sampling shortest paths?

I have a triangle matrix NxN of distances between vertices (vertex i connected only with vectices j>i) and I'd like to sample path from first to the last and use it as a training sample. Is there an ...
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1answer
33 views

Using Majority Class to Predict Minority Class

Suppose I want to train a binary model in order to predict the probability of who will buy a personal loan and in the dataset only 5 percent of the examples are people who marked as bought a personal ...
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1answer
201 views

How are samples selected from training data in Xgboost

In Random Forest, each tree is not fed with the full batch of training data, only a sample. How does this work for Xgboost? If this sampling happens as well, how does it work for this ML algorithm?
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1answer
21 views

Gaussian Process for Classification: How to do predictions using MCMC methods

Problem I was reading about Gaussian Processes for regression in the "Gaussian Processes for Classification" textbook and in a few other online resources. Everywhere I look people seem to avoid ...
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47 views

undersampling problem in imblanced dataset ValueError: Unknown label type: 'continuous'

I would like to undersampling the data but I encounter the following error? ...
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2answers
97 views

How to perform bootstrap validation?

I am working on a binary classification problem. I ran cross-validation and grid-search on train data. Later I validated the model on my test data as shown below ...
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23 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 ...
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2answers
31 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 ...
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1answer
55 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 ...
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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 ...
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1answer
66 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, ...
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2answers
294 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 ...
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12 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 ...
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50 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 ...
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3answers
76 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 ...
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3answers
216 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 ...
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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 ...
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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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20 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
397 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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28 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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1answer
71 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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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 $\...
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22 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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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 ...
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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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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
24 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
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 ...
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1answer
815 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
59 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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175 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
1k 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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1answer
339 views

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

Consider the following data: ...
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1answer
39 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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1answer
150 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
2k 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 ...