Questions tagged [labeling]
The labeling tag has no usage guidance.
79
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When to use Label encoding
All the articles I read, it is clear that, Label Encoding should be avoided for the ordinal data. But, in one of my ML tutorial video of Artificial Neural Network, ...
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1
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15
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Correction of labelling bias using the labeler identity as a feature
Suppose I have a dataset labeled by multiple analysts.
I assume that each analyst has some bias in his labeling.
Is there any literature on reducing the bias effect on the general model by using the ...
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22
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Proven approaches for labeling audio data for training a speech-to-text model
To train a deep learning model, for converting speech to text, we need labeled data. How should this data be arranged?
I can think of several approaches, and I don't know which approach has already ...
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Node classification with random labels for GNNs
I decided to train GCN on the Cora dataset for the node classification task, however, with the random labels, i.e., applying np.random.shuffle(labels). For the ...
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How can I utilise known label consistency in classification
This is a problem that I have experienced in multiple domains but most recently in identifying bird species from audio data. I have 300, 5 minute audio files with corresponding species labels (10 ...
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72
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What is the correct Kappa coefficient to use when more than two labelers are present?
I have a dataset consisting of m rows and 2 columns. The m rows denote a patient and the columns denote the label assigned to them. The label size can be arbitrarily long, with labelers given the ...
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50
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Evaluating classifier with 2 labels and 'unknown' label
The classifier I'm using has 3 possible label outputs - POSITIVE, NEGATIVE or UNKNOWN. For training data, the labels are only POSITIVE and NEGATIVE.
What is the best way to handle evaluating the ...
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830
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How to compare labels from clustering analysis and original ones?
I was asked to run a clustering analysis to assess the validity of labels for a manually labelled dataset.
I can simply save the actual labels (4 classes: 0, 1, 2, 3) and run clustering analysis (let'...
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Improving model accuracy by manually labelling more data?
I'm working on a binary classification problem with a small dataset n < 2000 that predominantly uses text data. Let's say the model tends to misclassify observations where a certain categorical ...
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1
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What are some best practices for labeling data that exists in a continuum?
I am building computer vision models on data that exists in a continuum. For example, imagine I'm trying to do semantic segmentation on cars. Some of the labels are distinct, like "chipped paint&...
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Is it a good idea to have a category and its subcategories in the training set of an object segmentation model?
I hope you are doing great!
I am currently training an object segmentation model (detectron2 : mask rcnn)
The objective is to detect materials like wood, plastic, glass etc...
wood is one of the ...
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76
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For semi-supervised learning, is more pseudolabels always better than less pseudolabels?
Let's say I have a labeled dataset $L$ and unlabeled dataset $U$, where $U \gg L$. Suppose I focus on a subset of $U$ called $u$ and generate a subset of $u$ I'll call $u_L$ that consists of ...
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13
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Bulding a labelling dataset and modelling categorical features
Summary: How do I ensure that sample I create for labelling is representative enough and would be appropriate for modelling, given I cannot include all feature combinations in it.
I have a tabular ...
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24
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What is the mathematical term for a real world categorical function that yields several categories for the same inputs?
Background and Color
Contextually, this is pertaining to machine learning and natural language processing. Specifically, it has to do with the labeling of real world data for partitioning by a machine ...
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Chicken and egg problem in machine learning [closed]
Recently, I went through an ICLR paper SELF-LABELLING VIA SIMULTANEOUS CLUSTERING
AND REPRESENTATION LEARNING. In the paper, authors discussed simultaneously labeling the images and training a network ...
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36
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Rule based label - For attrition risk
I have 3 domains of supplier data (Jan 2017 to Jan 2022) and they are as follows
a) Purchase data - Contains all the purchase (of product) data made by the suppliers with us. It contains columns such ...
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1
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97
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Logistic Regression applied to biased dataset
I have collected a binary classification dataset in a somewhat biased way:
I have thousands of unlabeled samples.
A small percentage of these samples belong to the positive class.
I know for a fact ...
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131
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binary classification with labels of varying quality
I have a binary classification problem, where half my development data is labeled by a reliable source, and the other half by a less reliable one. Note that each instance gets a label from only one of ...
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Estimate sample size from a variable population
Context:
I want to measure accuracy, precision, and recall for individual raters. Each rater completes a variable amount of labels, for ex. rater A may complete 500 in a given time period while rater ...
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1
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554
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Use Naive Bayes to label unlabeled data
I have an Excel file that includes all product information (web scraped from Zalando) of 10k dresses. So for each dress/line I have multiple features available (brand, color, neckline, length...)
I ...
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111
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Labeling unlabeled data (expectation maximization)
Say I have a database (Excel) consisting of 10k different dresses and accompanying attributes (column names) for each dress (sleeve length, color, pattern, ...). I would like to label each of these ...
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43
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How to do sentiment analysis in financial news?
I already have financial news that I got from financial news sites. Now I want to apply sentiment analysis to classify news as positive, neutral, or negative. I do not know what to do. I know some ...
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43
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How to combine two manual classifications
We are manually classifying texts as positive (+1), neutral (0) or negative (-1). The purpose is to train a sentiment analysis classifier.
We are two people, and we have both classified the same ...
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43
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Question on labels in machine-learning
Suppose that $X$ and $T$ are both random variables. Where $T$ is a label.
I just want to ask is tha finding probabilities on the label $P(T=t)$ or conditional probabilities $P(X=x|T=t)$ meaningful or ...
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113
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Correct or most common term for altering a loss function to ignore unlabelled pixels?
In my experience it is quite commonplace to alter the loss function used when training a neural network for segmentation to ignore the contribution to the loss of unlabelled pixels. There are a few ...
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9
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label encode date [closed]
I am working in R. I want to set initial date(6-2018) as 0 and last date as(12-2020) whatever the last value is. I have around 19 unique dates. I tried to Label encode them but it changes to 0,1,1,1,1,...
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How should I construct a binary classifier for small set of positive data and million of unlabeled data?
Does anyone have suggestions for specific algorithm or implementation for labeled data of only one class and unlabeled data that can be from either classes? And I'm unsure what is the proportion of ...
- 11
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31
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Comparing quality of automatically labeled data
I have two algorithms for automatically labeling data for a computer vision task. The dataset is large, and it's not feasible to inspect visually.
Can I determine which labeling is higher quality ...
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1
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39
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How to make a decision - when there is a tie and no human expert
We have two algorithms (simple rule-based) working on labeling the dataset as "Yes" and "No" for a disease. There is no ML involved in this task.
For ex: If Algo 1 says subject 1 ...
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For binary classification learning problems, how should I label instances where I'm only 60% sure?
I've come across a few binary classification problems lately where the labelling was challenging even for an expert. I'm wondering what I should do with this. Here are some of my suggestions to get ...
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218
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Label-encoding nominal variables
I am aware of the practice that label encoding is preferred for ordinal variables while ...
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60
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How to formulate a machine learning problem?
Virtually no books talk about how to formulate a ML problem, especially the process of creating the right label. Here I use an online hotel search portal as an example to explain my thought process ...
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462
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Conflict vs Overlap in Snorkel Label Analyzer
I am currently working on a weakly supervised approach to labeling training data. Snorkel helps make the process a lot easier by providing methods to facilitate labeling, modeling, etc. Snorkel ...
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1
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22
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Principled Method for Reweighting Labels Based on Labeler Consistency?
I have a large dataset where each datum has been labeled by multiple raters, and each rater has labeled multiple datums. Rather than assigning to each datum the naive average of all of the labels ...
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mislabeled data Event Detection, neural language processing
I'm doing some experiment with sequence labeling problems, more specifically is Event detection problem. But I'm encountering with the mislabeled data issue.
In my dataset, there are many tokens that ...
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Active learning with a unlabelled pool - standard references & model-based labelling of the pool?
I'm looking into active learning for a multi-class classification problem, where there is a large pool of unlabelled data. I start out with a small set of labelled data and can labelled some more of ...
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How to deal with incorrect labels in classification?
I have a dataset with 2 classes: A and B. The problem is that 20% to 30% of the samples of class B are mislabeled (labeled as B but the right label is A) and I am not able to identify those mistakes.
...
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705
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LabelEncoder with a Multi-Layer Perceptron?
So we're working on a machine learning project at work and it's the first time I'm working with an actual team on this. I got pretty good results with a model that uses the following SKLearn pipeline:
...
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Does mislabeling due to adversarial noise in features count as adversarial machine learning?
According to the traditional definition, Adversarial machine learning is a technique employed in the field of machine learning which attempts to fool models through malicious input. However, I have ...
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248
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Using label encoder on a categorical feature that we want to embed
I have a dataset with feature that have very high cardinality, doing one-hot encoding is not an option because of memory limitations, so I am currently label encoding this feature and then I feed that ...
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2
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172
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Directly using Inaccurate Labels vs. Transfer Learning
I have a two ML models model_a and model_b that optimize on an event, label_a.
I have a small volume of labels for model_a and a large volume of labels for model_b.
The features used in these models ...
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How to you label (caption) the table title? (for example:Tests of Between-Subjects Effects for One-way ANOVA)
How do you lable/caption a table under One-way ANOVA?
Tests of Between-Subjects Effects for One-way ANOVA
OR
...
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331
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Binary least squares classification labeling {0,1} vs {1,-1) [closed]
I am trying to find the best possible line which discriminates my data with least squares. I have 2 points, $\pmb{x}_0=\{1,2\}$ which belongs to the class $y=+1$ and $\pmb{x}_1=\{2,1\}$ which belongs ...
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Do ordinal variables require one hot encoding?
For categorical variables, one hot encoding is a must if the variable is non-binary . But what about ordinals? These variables are ordered but are mutually exclusive. Do they require the same ...
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55
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Labels in Out-of-time validation
I have a binary classification problem and I use out-of-time validation to validate my models. My question is regarding the label. There is a lag in identifying the correct label.
Simplified example:
...
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174
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Elastic net/LASSO with soft labels
Sometimes you do not have firm Y/N labels, but e.g. 80% probability of Y as a label. E.g. this happens, if you train a model on a small amount of labelled data, predict for a large amount of ...
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161
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Meaning of sparse annotation for images?
what is sparse annotation? Is it pixel-wise labeling for images. what are the other types of annotation?
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is it scientifically correct to label data by model built using golden data?
I am trying to find a labeled dataset for users profiles pictures with their personality traits scores. Unfortunately, I did not find any and therefore, I decided to crawl twitter for public users ...
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2
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163
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When is it okay to label data yourself? (And semi-supervised learning)
i'm pretty new to machine learning so i think this might be a realy basic question.
Let's imagine the following scenario: I want to classify projects as either active or inactive. Projects can be ...
4
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2
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301
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Regression algorithm on [0,1] with lots of mislabeled data
I have a training set mapping some Likert-scale variables (integers between 1 and 7, rescaled to real numbers between 0 and 1) to predict a continuous variable between 0 and 1. The data set is ...
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