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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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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 ...
Roy Phillips's user avatar
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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" ...
maglorismyspiritanimal's user avatar
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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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Convergence of a Bayesian classifier

Background Let $y_k$ be a noisy measurement at time $k$ and let $\{p_{k-1}(i)\}_{i=1}^n$ be (a discrete) prior probability distribution. Using Bayes rule, one can update the prior in function of $y_k$ ...
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Proving the equivalence of two distinct approaches to multiple regression for binary classification

I'm stuck with this peculiar problem that uses multiple linear regression in order to solve a binary classification problem (note: it's not considering the logistic version or any other GLM approach). ...
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Logistic regression only cares about the $E[X|Y]$ for prediction

I was reading this blogpost about how logistic regression and Naive Bayes with gaussian features are equivalent This led me to think that at the end of the day, logistic regression only cares about ...
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Applying PCA Before Training Multiple SVM Binary Classifiers To Reduce Data

I am working on a project which has a goal to determine if a new sample is part of Class A or Class A'. I need multiple of those classifiers. I will have an SVM to classify between: ClassA - ClassA' ...
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How does highly imbalanced test data in certain splits of k-fold time-series cross-validation affect model performance?

I am working on a time-series classification (TSC) problem using k-fold time-series cross-validation (TSCV) to evaluate the performance of my models. My training data for each split is fairly balanced,...
Tov Nephesh's user avatar
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We have sensitivity-specificity space (ROC curves) and precision-recall space (PR curves, $F_1$ score). What work has been done with PPV-NPV space?

Receiver-operator characteristic (ROC) curves display the balance between sensitivity and specificity: how good you are at detecting category $1$ (sensitivity) while not falsely identifying category $...
Dave's user avatar
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Why is my accuracy fluctuating for a while and then stuck? [duplicate]

I am building a cnn classifying model to predict images over 3 classes. The data is balanced, with 10.5k images for train ( 3.5k for each ), 3k validation images ( ...
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How to find a linear decision boundary of a linearly separable problem with unlimited class evaluations?

I have a binary classification problem, where my goal is to find a linear decision boundary (which I assume exists). The context of the problem is that I have an iterative optimization process, where ...
oskar0711's user avatar
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How to interpret this confusion matrix in R?

I've produced a logistic regression model using 70% of my data and tested it using the remaining 30%. These are the results of my confusion matrix: Confusion Matrix and Statistics Confusion Matrix (...
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Interpretation of a decision tree plot

For a paper, I am training different models and using LIME to simplify the blackbox models into a transparent decision tree model that I can visualize with view(tree, "mode", "graph&...
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GridSearchCV performs worse than baseline

I'm working on a binary classification problem using scikit-learn. One of the models I've tested is KNeighborsClassifier, for ...
AndreaTerenz's user avatar
2 votes
1 answer
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Proper usage of K-fold cross validation and finalizing model

I am trying to learn about k-fold cross validation. I am using it on Kaggle dataset of brain tumors MRI trying to classify the images. Kaggle provides two directories Training with 5712 images and ...
Daniel11's user avatar
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"ROC AUC reflects the likelihood that a random positive instance will be located to the right of a random negative instance". How come? [duplicate]

According to this webpage, ROC AUC reflects the likelihood that a random positive (red) instance will be located to the right of a random negative (gray) instance. Would you please explain this ...
Evan Aad's user avatar
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Is the confusion matrix meaning the same within deep learning than within hypothesis testing?

Is the confusion matrix meaning the same within deep learning than within hypothesis testing? I've read what the matrix is, and how to get the values. But in the case of deep learning for ...
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Problems using custom dataset using Minirocket classification [closed]

I'm working on a bigger school project, trying to classify timeseries measurements with Minirocket/Rocket. My trainingdata consists of a 1D matrix containing the measurements, and a seperate 1D matrix ...
Michael's user avatar
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Determining an optimal level of aggregation that balances accuracy and granularity

I am looking for ideas for aggregating prediction outcomes in a way that maximizes the number of classes while minimizing classification error. As a motivating example, say I'm working on a prediction ...
mle_in_paris's user avatar
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Is multinomial naive Bayes classification not naive Bayes classification?

Suppose I am thinking about a classification problem and I have my features $X = (X_1,...,X_n)$ and my classification $C$ (taking values in some finite set of classes $c_1,...,c_k$). The naive Bayes ...
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should the standardisation of numerical variables be carried out before or after the rebalancing technique of the target variable?

I am dealing with a classification task of a binary target variable (company failure prediction yes or no) for a university project. I was wondering, should the standardisation of numerical variables ...
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2 votes
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Re-calculate accuracy, precision and recall after treatment effect in a model

Working in a churn-prediction model where the goal is to detect the players that have a high chance to churn from the site and send those players an offer to keep them in site. In the initial training ...
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2 answers
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Can I use Tanh before softmax?

I am researching the use of neural networks for binary classification tasks of financial data. The output result is two-dimensional, such as [[0.5,0.5], [0.1,09]]. In the case of only 2000 small ...
Mar7's user avatar
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1 vote
1 answer
29 views

How to interpret the results of a classifier when train/test method gives much better results than cross validated one?

I need your help to understand a situation where using train and test set produces perfect results (in terms of accuracy, precision, and recall) but when cross validation is used, the accuracy on ...
letdatado's user avatar
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2 votes
1 answer
38 views

How to deal with extremely small training dataset in machine learning? [duplicate]

I've around 100 rows of data with labels ...
zZzZ's user avatar
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1 vote
1 answer
138 views

How to deal with extremely small training data? [closed]

I've around 100 rows of data with labels ...
zZzZ's user avatar
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4 votes
2 answers
524 views

Overfitting in randomForest model in R, WHY?

I am trying to train a Random Forest model in R for sentiment analysis. The model works with tf-idf matrix and learns from it how to classify a review, in positive or negative. Positive ones are ...
Anisa's user avatar
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An error occurred when using the xgboost as a classifier for hiclass [closed]

Bellow it's my example when using the xgboost classifier for hiclass. My question is specifically directed to the hiClass Python package for hierarchical classification. I would like to model the ...
Ramzy's user avatar
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Combining classification models and regression (Hierarchical? or MoE modeling?)

Assume, there is a dataset of human poses captured from different angles. For each sample there are 2 level of labels - pose type (standing, sitting, etc) and pose magnitute(continuous). Defined tasks,...
PatrickHellman's user avatar
4 votes
2 answers
100 views

How to split and sample "Panel Data" when training a Logistic Regression to predict future outcomes

Introduction I have panel data where customer behavior is observed over time. For each customer at a given reference date, I have a lookback window of 12 months for generating features, and a look ...
Esben Eickhardt's user avatar
6 votes
1 answer
35 views

What is happening behind the scenes when we use CalibratedClassifierCV without prefit?

From what I understood by reading sklearn Probability Calibration, when we run CalibratedClassifierCV we will fit "a regressor (called a calibrator) that maps the output of the classifier (as ...
andy mot's user avatar
1 vote
2 answers
34 views

KNN K = 1 Training on itself vs K > 1

When training a $KNN$ algorithm, why is that with $K = 1$, the model trains using the "1 nearest observation to each training point" and treats this as itself resulting in a training error ...
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Should I interprete data as noise or not

I am tackling a classification problem with 3 classes. Here is what those classes look like on the Two first principal axes. I fine-tuned a SVM model and the best performance achievable was 50%. By ...
Yann's user avatar
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2 votes
1 answer
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How does a logistic ordinal regression model learn the interaction between the features?

I have a dataset of informations about students and the last column is the target variable which is the final note. My goal is to make logistic regression and ordinal regression models to see whether ...
Moez Daly's user avatar
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26 views

Do you need to convert all your input numerical variables to categorical if you want to change the target from regression to classification?

Title. I did some research on the StachExchange/data science/cross validation, etc, and found that "converting numerical variables' numerical regression problem into categorical variables' ...
Yuuya's user avatar
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what are the consequences of re-encoding with 0 or -1?

I have a dataset of information about students and the last column is the target variable which is the final note. My goal is to make logistic regression and ordinal regression models to see whether ...
Moez Daly's user avatar
0 votes
1 answer
35 views

About the use of Bayes' rule for continuous valued random variables

I am currently studying the book "An introduction to statistical learning with application in Python" and I am currently at the part 4 of chapter 4 where they explain the general framework ...
Vincent's user avatar
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0 answers
10 views

Combining both regression training and classification Evaluation for a single task

I was given a task which involved video summarization using AI. My initial thought was to use a basic classification model based on image features. However, after reading papers and conducting some ...
moha tech's user avatar
3 votes
1 answer
46 views

Which is the denominator of the Brier score for joint multiple variables predictions?

Brier score can be computed for joint predictions of multiple variables, each with multiple categories. Let's say we have 4 variables with 3 possible classes each. In that case, the denominator of the ...
Antonello's user avatar
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0 answers
16 views

Improving a logistic regression where multiple signals separately yield the same accuracy, and combining them does not improve the model

I have a logistic regression that estimates the probability of an event occurring. There are roughly 10,000 data-points, and I have roughly 20 model features. The model features are each quite ...
olives's user avatar
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1 vote
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Image classification metrics

I have been working on an image classification task using CNNs and getting some puzzling results. My training, validation and test loss keep going down with epochs and are comparable. So this might ...
Nithin's user avatar
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1 answer
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using MSE loss paired with F-score in a classification model

for a video summarization project i use the features of each frame as input to predict if some of these frames are included in the summary or not. one of the famous implementations i found had treated ...
moha tech's user avatar
1 vote
1 answer
100 views

Choosing the correct evaluation metric between F1-score and Area under the Precision-Recall Curve (AUPRC)

We're currently working on detecting specific objects (e.g. poultry farms, hospitals) from satellite images. We've modeled the problem as a binary image classification task (i.e. classifying images ...
meraxes's user avatar
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1 vote
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Balancing when combining multiple datasets (balancing in a metastudy?)

There have been some detailed discussion of balancing the datasets in this community: Are unbalanced datasets problematic, and (how) does oversampling (purport to) help? Why is accuracy not the best ...
Roger V.'s user avatar
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1 vote
0 answers
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Why does GAP at the end of FCN for MTSC work?

I have a binary MTSC (Multivariate Time Series Classification) problem where i train a CNN, namely a FCN (or Fully Convolutional Network) to predict class 0 or class 1 based on a multivariate time ...
davva's user avatar
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0 votes
1 answer
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Standardise features for a tree based model or logistic regression

I am trying to understand if one should standardise features for all models and when does it make sense to do so. Is the below statement true? If yes, could you give a bit of explanation please. ...
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