Questions tagged [classification]
Statistical classification is the problem of identifying the sub-population to which new observations belong, where the identity of the sub-population is unknown, on the basis of a training set of data containing observations whose sub-population is known. Therefore these classifications will show a variable behavior which can be studied by statistics.
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How to formulate this entity resolution problem?
Problem formulation
Given:
Two sets of elements: S = {s₁, s₂, ..., sₙ} and T = {t₁, t₂, ..., tₘ} (you can think of them as two sets of records)
Each element e ∈ S ∪ T has properties:
A real number v(...
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Should I use ROC curve in my training set after training a Random Forest classification model with k-fold cross validation?
I have a conceptual question: after dividing a dataset into a training and test set (70:30), both are balanced and shuffled, should I use the Confusion Matrix and the ROC curve of a model generated by ...
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Model still overfits after hyperparameter tuning, dataset balancing and convolution layering
I am trying to classify either an image of 25x25 px stacked together as 50x25 px is the same(1) or different(0). I am using keras to create the NN layers. There are 10,000 instances of both 1s and 0s ...
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Using Crossvalidation to evaluate different Mutual Information Threshholds for Feature Selection
I'm currently trying to determine a mutual information threshold value for feature selection in a classification task. My idea was to set different thresholds based on location parameters e.g. median, ...
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How to Compare Binary Classifiers with Similar but Different Labels
I am interested in understanding the best way to compare two binary classifiers with similar but different labeling strategies. Here is the synopsis:
I am building a classifier to predict when a ...
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How to train a neural network to identify multiple features in one image, where the order of predicted features doesn't matter
I recently created a toy dataset for myself, which I call "multi-color MNIST", where multiple digits with different colors appear on a single RGB image. See the image I attached. I am using ...
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High Variance Ratio Signals with Low Denominators in Machine Learning Model
I'm training a model to predict the probability of a website event occurring, based on ~100 signals about clicks and impressions. Most of the signals are ratios; that is, they are of the form ...
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Analyzing Range of Features for Classification
I have developed a classifier with 5 ordinal classes, and I’m looking to do a detailed analysis on the range of values required for each class (1 through 5).
For example, I want to understand how a ...
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Binary predictive classification in R, with predictors consisting of multiple values
I am struggling currently with constructing a binary glm predictive classifier, due to an issue with dimensionality.
I have a dataset of N samples where each sample has values for M entries (genes) ...
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test & train for very very small data
I have just 25 observations. I'm not sure would it possible to test & train the data. For example 15 observations for train and 10 observations for test set. 15 observations is so small for ...
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Using elastic-net only for feature selection [duplicate]
Could we use regularization model instead of feature selection methods and then use the machine learning models to analyze data?
My problem is classification and there is more than 1000 features in ...
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Defining two thresholds in a binary classification model
In the context of binary classification, models usually output probability values ranging from $0$ to $1$, and then use a default threshold of $0.5$ to decide wether to classify an observation in one ...
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Why can a classifier's predicted labels be improved (with respect to the same metric the classifier optimizes) by adjusting classification threshold?
I am hoping to enhance my (and others' perhaps too) understanding of some basic principles, which seem surprisingly elusive.
For a start, I would like to consider imbalanced binary classification, ...
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Is there any way to can deal with missing data without imputation ? model considering NA values [closed]
My question is how to model data with NA values and without imputing. Is there any possibility? and what is the advantage and disadvantage? The problem is classification.
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rpart() decision tree fails to generate splits (decision tree with only one node (the root node))
I'm trying to create a decision tree to predict whether a given loan applicant would default or repay their debt.
I'm using the following dataset
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If I use the regularization method for feature selection and then use machine learning model to predict, am I overlapping?
I used the regularization method such as elastic-net as a feature selection and then I used the random forest and deep learning to predict the target using the selected features. The problem is ...
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Type of model to measure spread between two assets
I am analyzing the price between two assets and the relationship between various features that impact them.
The vast majority of the time, over $90%$, the assets are equally valued, thus the spread is ...
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Would it be possible to generate data from real data in medical research? [closed]
We are trying to develop some predictive models in medical research. We have combination of clinical and RNA-seq data just for 40 patients. The problem is classification. After feature selection, we ...
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Training an image classifier to detect slight differences in line angles
I've been struggling to find success training a Yolov8 classifier to detect slight difference in line angles.
My hunch is that the difference in line angles are so slight that the classifier is ...
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How to increase weight of false negatives in MCC?
I am working on a multi class classification model that is able to predict pre-diabetes and diabetes. I am currently using Matthews Correlation Coefficient (MCC) as my primary metric for evaluation. ...
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Classification rule
In classification, is there any difference between MAP (maximum a posteriori) rule, minimum misclassification rule, and the Bayes decision rule. As far as I know, all of the three rules seem to ...
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Would it be possible to use regularization methods as a feature selection method and then use machine learning models to analyses data?
My data is RNA-seq data with more than 14000 features and the problem is binary classification. Then the total sample is 50 and p>>n. When I use Elasticnet method with train and test data, the ...
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Averaging classification metrics vs Direct infer
Suppose I have tensor X_test with this shape:
(10, 2, 512) Where:
10 is num of ID
2 is num of Channel of every ID, let's say ...
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Does artificially balancing outcomes in regression lead to poor calibration? If so, how to show the poor calibration?
In "classification" problems, it is common for there to be unbalanced-classes. To combat what appears to be a problem (though I would argue that it usually is not a problem), it is common ...
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Coefficient of determination with binary prediction
The coefficient of determination $R^2$ is a popular measure of regression performance that compares the mean squared-deviation of predictions to the variance of the actual data. If instead of ...
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Extremely high logloss in binary classification problem [duplicate]
I have a binary classification problem that I am currently trying to tackle with xgboost. This is a low signal-to-noise ratio situation dealing with time series. Per this answer "Dumb" log-...
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Logloss worse than random guessing with xgboost
I have a binary classification problem that I am currently trying to tackle with xgboost. This is a low signal-to-noise ratio situation dealing with time series. My out of sample AUC is 0.65, which is ...
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How to construct class proportion confidence interval for an LLM classifier with known bias and precision and recall?
Let's say I have a dataset, $D$, with known ground truth labels. I nonetheless use a few-shot LLM classifier on this dataset to predict $k$ classes for each label.
From the LLM results, I get ...
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False Negative vs False Positive for Multiclass classification
Suppose I have three classes 1,2,3.
And there's evaluation like below, where second element is false prediction where model predict class 3 while ground truth is 2.
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Using scale pos weight and non 0.5 cut off score for a look-alike model
I'm working on a classification problem where I'm trying to identify look-alikes of Class 1 in Class 0. Class 1 and Class 0 are established based on type of product customers use. Basically, Class 1 ...
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Why the different default parameters for scikit-learn gradient boosting classifiers? (GradientBoostingClassifier and HistGradientBoostingClassifier)
Why do gradient boosting classifiers (GradientBoostingClassifier) and histogram-based gradient boosting classifiers (HistGradientBoostingClassifier) have significantly different default hyperparameter ...
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How can I explain the difference in accuracies in different ML models?
I have applied various ML models (fundamental and ensemble) to the same dataset for classification problem solving.
AdaBoost, Bagging, and XGBoost classifiers gave the best accuracies. However, they ...
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Mean and Standard Deviation of accuracy for SVM model prediction
I am training a SVM model for binary classification. For this, I have split the train and test datasets in an 80:20 ratio. Then I standardized the training and test data separately and tuned the ...
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How to estimate precision and recall without taking a huge random sample when the positive class is relatively rare
I have a binary text classification model, and I would like to test how well it works, in terms of precision and recall, on a new dataset of 2 million text documents that have not been annotated yet. ...
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What is the best epoch to evaluate the test images?
I created a training, a validation and a test set for an image classification task. Then, I did training using the training and did evaluation on validation set. So, the next step is to evaluate the ...
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What kind of classifiers we shouldn't use for feature selection?
Generally, I see that, for feature-selection, people use PSO as optimizer and inside the cost function, they use less powerful classifiers like SVC, Logistic regression, KNN, etc.
Is there a reason ...
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Object detection for finite dataset
Consider the following scenario
If I want to train a model to detect and count these squares:
These squares will never be different. They will always look exactly the same, and be of exactly the ...
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Best Practices for Splitting Data in a Repeated Measures Classification Problem
I am working on a classification problem involving repeated measures. My objective is to classify positive patients as early as possible. In my practical application scenario, once the target becomes ...
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Suggestion for training various models for classification of different species
Working on developing classification networks for various types harmful algae vs algae/other objects found in the ocean water. We have developed binary networks for some harmful algae vs Ocean. There ...
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One class text classification for open-ended survey questions
I have a specific text analysis problem, and hope someone can point me in the direction of a good approach. I have three corpuses consisting of short documents (open-ended survey questions). One is a &...
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Is it possible to compare the output probabilities of two machine learning models? [closed]
Let's suppose I have two classification machine learning models: $\text{Model}_1$ and $\text{Model}_2$. Each of them are not necessarily the same algorithm, and have not been trained necessarily with ...
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Top-N recommender system
Say an intermediary is using a two part recommender model that attempts to facilitate services between its clients and external vendors:
Model 1: Predict probability of vendor bidding on a given ...
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How to identify the most important features that impact an ordinal score?
I have 40 rows of 5 continuous features and 1 ordinal score. What statistical technique is recommended for me to identify which features have the highest impact on the ordinal score? I have looked ...
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Violated assumption of independence in logistic regression
In order to predict forest fires occurrence, some studies (study 1,study 2)used meteorological data plus vegetation and topographical data. I'm trying to do the same for a different location but I'm ...
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How to draw a ROC curve given estimated probability that a unit is positive and actual observed class? [duplicate]
Assume that a classification model fitted to data available to you has provided for each statistical unit a probability $P(+|x)$ that the unit is positive. The following table shows all available ...
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Evaluating a classifier's performance on different groups of subjects
I have developed a binary classifier that predicts whether a subject is injured or healthy. I am interested to know whether my model performs better on certain groups of subjects than on others. For ...
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What data are used to find the final threshold for a medical diagnostic test?
Suppose I have some blood measurement X whose values correlate with some disease Y (so people with the disease use to have larger values of X). Moreover suppose that the disease is rare, say 1% of a ...
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Taking into account a non-symmetric loss function in a classification problem
Consider a binary classification method that estimates the class probability and where the observation weights can be specified (e.g. Logistic Regression). To accommodate the difference losses from TP ...
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I want to plot the decision boundaries of an SVM model with more than 2 variables
I understand that that is impossible to visualize, so I went in and PCA-transformed the variables. The problem is that I still need more than 2 principal components to get "good" ...
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Precision calculation for Test data
I have a trained multiclassification (4 different labels) ML model for which I calculated Accuracy and Precision using Confusion Matrix . Now for the developed model, I give some test data without ...
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