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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13 views

best hypothesis for data with zero mean noise is the one which assumes no noise at all?

In an ml-class, they introduced overfitting with an example: Say, we have $x$ picked from $Uniform(0,1)$ $v$ random noise, picked indepidently of $x$ from $Uniform(-0.3,0.3)$ and $\mathbb{E}[v] = 0$ $...
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Some Confusions Regarding Variable Importance Extraction of Several Machine Learning Models

I'm trying to apply several machine learning algorithms in R using caret (decision trees, ensemble methods (bagging, boosting, ...
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RBF Network for classification

I would like to know how it is calculated the outcomes (i.e. the output layer output) of a RBF Network for a classification problem. My code fits the hidden->output weights with linear regression ...
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How does Adaboost increase the weight of the Data Instance in case of Regression

I know how the weights of the data Instances increases for all the data which were wrongly predicted in case of AdaboostClassifier, however i did not understand how the Weight of the data point ...
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Model adjustment during cross-validation

I have an imbalanced dataset, with the following stats: Value Count Percent 0 133412 97.62% 1 3247 2.38% I have created a ...
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28 views

AUC ROC and Varying Thresholds?

I understand that the ROC curve will plot the sensitivity vs FPR for varying thresholds. For my SVM ML model, I desire a good sensitivity score so I have decreased the threshold to make a positive ...
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Does my validation curve indicate over fitting from train and test?

I carried out a grid search on my xgboost and varied the parameters below. I noticed in my grid search results that the train score is very high, e.g. 0.99999 and my test scores are more modest around ...
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Is it possible to classify a rather messy image dataset using CNN?

I am trying to classify a rather messy dataset of images. The images are taken using mobile phones and scanner scans from various sources so there is some variation in the dataset. Its a dataset of ...
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How to averaging classification predictions across different CNNs?

In the paper "ImageNet Classification with Deep Convolutional Neural Networks" ,the 6th section ("Results"), said: "Averaging the predictions of five similar CNNs gives an ...
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Analyze misclassifications

I've trained a classification model (XGBoost) but I'm having some issues with misclassification (especially false positive). I want to analyze the misclassified observations to see if the model misses ...
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The effect of multiplying the weighs of a trained neural net with a scalar

Can somebody give us some intuition on why multiplying the weights of a trained neural net with a scalar $s \geq 1$ doesn't changes the accuracy, but multiplying the weights by an scalar $s < 1$ ...
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Anomaly detection using Mahalanobis distance

I am using Mahalanobis distance to identify outliers. I am training using kind of one class classification,by training only on positive samples and trying to predict negative samples using distance ...
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Confidence/Prediction Interval on binary/multiclass problems

I've recently fine-tuned a deep learning framework/model BERT for a sentiment classification task. I'd like to look at the confidence/prediction interval of the predicted sentiment scores (class 1 and ...
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Sample size vs F1-score, which is more important (small sample size)

I work in a filed were there are many publications based on classifiers trained on small samples sizes (but large amount of features). In most cases the sample size can only be increased by a few ...
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What kind of prediction/ classification is this?

Suppose I have a data frame like the following (and I want to do machine learning): ...
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29 views

P-values for sensitivity/specificity when only improvement is possible

Imagine two diagnostic tests in a single population at risk of the disease, one of which is always "more strict" than the other, meaning that if test 1 is positive, test 2 will be positive ...
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Building AUC/ROC curve without probabilities, only with actual/predicted labels

If we dont have access to model and have just actual and predicted labels without probabilities, is it still be possible to plot AUC/ROC curve. For example can we have the curve from the following ...
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Prediction of group of values

I want to predict belonging to the range of values (e.g. age group) based on numerical labels (e.g. exact age). Is, in general, regression or classification more accurate to this problem?
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How to train a neural network *not* to give a certain output?

I have a neural network with a softmax layer as final layer. The loss function I use is the categorical crossentropy loss. I want to classify an input to belong to exactly one of N classes. But for ...
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How to select a classifier from the set of classifiers resulted from different random undersamplings with different accuracies?

I have a labeled two-class training set and a test set. My training set data is imbalanced. I want to train a classifier using the training set and evaluate it's accuracy using the test set. Before ...
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How to tune specific hyperparameters in H2O AutoML

Is it possible to say to the H2O AutoML to tune an additional hyperparameter (eg. the class_balancing param that has to be specified)? I would like that the AutoML ...
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Empirical risk minimization on a mixture distribution and misclassification error inequality on individual components

My question is about establishing a one-sided inequality between population error and expected training error for a model trained with Empirical Risk Minimization (ERM). The setting is described as ...
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21 views

Is there a way to classify curves into two or three groups?

I conducted an experiment, in which participants (n=44) experienced different tones ranging from low to high (x-axis) and rated their perceived liking from 1- low to 10 - high(y-axis). I created line ...
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1answer
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Decile Analysis for model comparison

I am working on a simple classification problem (bank marketing response data) and trying various evaluation methods for multiple models to compare and understand the output. And I was trying to do a ...
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Where to find pre-trained models for transfer learning - for tabular data to solve the classification of cardiovascular disease or related

Most transfer learning models suggested are for image classification, where can I find models to use as a weight initialization scheme to predict Cardiovascular disease? The data is tabular, ...
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Does it make more logical sense to model the discrete FFT output as a categorical variable or a numerical variable?

I am training a time-series data classifier and some of my features are the output of CT FFT. The results are of course discrete frequencies. I understand that they are in numerical order and higher ...
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Metrics for Multilabel Classification

From what I've read, F1-score is a commonly used metric to assess the performance of a multilabel classification problem. However, I recently came across mAP@K and mAR@K as metrics used for ...
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Statistically compare two mutli label classifiers [closed]

I am trying to classify two different multi-label classifiers. Sample data: ...
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Assign more importance to recent records during training

I have a large dataset with information from the last year. I have to build a classification model in order to predict if a customer will buy a product or not (binary classification). Since in the ...
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Why applying Linear Regression to a classification problem often isn't a great idea? [duplicate]

I got to know two reasons why we don't use Linear Regression for Classification: For example- We want to predict a tumour is Malignant or Benign using one feature-x: Tumor size and we want to predict ...
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Is a “decision boundary” incompatible with proper scoring rules?

Having a decision boundary in a binary classification problem tells me that if the point lies on one side of the boundary, classify as $0$; if the point lies on the other side, classify as $1$. What ...
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Is this overfitting?

The Use Case: We are given three unique, 'ground truth' binary training patterns (not patterns with 'noise'). A machine is to be trained with these three vectors. The requirement is that once trained, ...
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Logistic generalized Additive Model (GAM)

How are the smoothers fitted in case of Logistic GAMS? For Gaussian response variable, many smoothers are defined such as splines and local regression etc. But how are they used in case of Logistic ...
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Predicting if some variable is $\geq C$?

Say you're given a dataset where the response $y$ is continuous. The only prediction you're after is whether $y \geq C$, i.e., where the response is greater than some value. In this case, would it be ...
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Transforming topics into text data

I was reading some articles on topic classification, in which some algorithm uses snippets of text as input and tries to classify them in topics, and I thought of implementing this technique in my ...
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35 views

Uniqueness of SVM solutions in term of l1 and l2 losses

I have been reading several articles to find reasonable answers for the difference between L1 (hinge loss)and L2 (squared hinge loss) in solving the primal and dual SVM problems. I need help to find ...
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Pretrained models for sexual harassment detection [closed]

I'm looking for pretrained ready-to-use models for sexual harassment detection in chat messages.. Preferably (but not necessarily) free of charge, open source, and allowing for domain specific fine-...
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Imbalanced classification or Regression? What is the best approach to my A/B testing related problem?

The context of the problem is A/B testing of two new versions of a game. I have a structured dataset (50000 rows x 22 columns) from the game designers that represents data with respect to two versions ...
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How can I improve a classification algorithm for dogs and cats?

The following code is a ML algorithm trained to classify between dogs and cats, the database is composed by 25000 images (evenly split) and can be obtained at this Link (if you click it will ...
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2answers
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Understanding cross entropy loss [closed]

The formula for cross entropy loss is this: $$-\sum_iy_i \ln\left(\hat{y}_i\right).$$ My question is, what is the minimum and maximum value for cross entropy loss, given that there is a negative sign ...
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The Bayes' Theorem Components of the Probability Output of a Classifier

Let's give a simple setup. I have $500$ photos of dogs and $500$ photos of cats, all labeled. From these, I want to build a classifier of photos. For each photo, the classifier outputs a probability ...
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Authenticity of text dataset according to its labels using K-Fold cross validation [duplicate]

I have multilabeled text data and I want to ensure the authenticity of the labels by using K-Fold corss validation. If I attain consistent classification results based on data splitting by K-Fold, ...
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In Logistic Regression models, should classification cutoff always be approximately equal to the prior $p = P(Y = 1)$?

Mathematically speaking, when using a logistic regression model for binary classification, the output of the model $\hat{y}_i$ for any instance $x_i$ not only can be interpreted as, but is defined as ...
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product categorization by short strings and one or more powerful factors - a climbers problem

I am using webscraping data to classify products related to the sport of climbing. For all shops i get a product name (which is kind of a short description) plus a category-string as how the product ...
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Decision Boundary & Vectors - Discriminative Classification

I'm struggling with understanding the underlying vector multiplication that are used to define a decision boundary for a binary classification. According to my textbook, this line may be expressed as: ...
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How to create an ROC model using three classes

I am trying to create a ROC plot based on response variable with three classes. I believe that is possible based on the answer to this question Here. In the answer provide in this question though, ...
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1answer
68 views

Sampling uncertainty of posterior probability distribution

I'm working on a problem with 3 possible outcomes and a bunch of features. I have a regression model that outputs probabilities for each category and I'd like to extend these probabilities to ...
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2answers
27 views

Should I run a machine learning model many times?

I am comparing different classification models and for a given model (let's say logistic regression), everytime I run it, it obviously produces a slightly different value for the Accuracy and for the ...
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Does Discretization improve Classifier Performance?

I am trying to understand the basics of how and when is it ok discretize a variable. Below are some papers that support Supervised Discretization: Improving Classification Performance with ...
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1answer
33 views

Binary Classification with almost no positives

I have a dataset with 121 features and 7176 data points. Only 11 of these are positive, the rest is negative. If I want to train a SVM on this data set, what would be the best strategy to do this? ...

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