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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27 views

Classification trees, determining prediction in leaf

My question is how the prediction in the leaf is calculated for classification trees with more than 2 classes. With regression trees its simple, its just the sample mean of the observations ending up ...
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Minimal dataset for the classifier [closed]

I have an older dataset of 43 participants who watched emotionally charged footage. There were three phases, initial rest, video and the rest phase after the video. We recorded electrophysiological ...
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upsampling vs class weights in mini-batch SGD

Let's consider using mini-batch SGD in (neural network) binary classification problem with imbalanced dataset. Let's say that the ratio between the number of examples in each class is positive:...
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35 views

Dealing with label noise (Regression, NLP)

For my school project, my group is tackling this Kaggle challenge (assign reading level based on passage). commonlitreadabilityprize However, it seems there is some label noise (examples below, lower ...
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Inspect each prediction in random forest

When we use elastic-net model for classification, we can check the coefficients and feature observations for each prediction, especially for those misclassified samples. For random forest, what are ...
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What is the definition of margin in a space that is not linearly separable?

When texts introduce SVM they tend to first assume that data is linearly separable, i.e. there is a $w$ and $w_0$ so that $y(x) = w^\top \phi(x) + w_0$ satisfies $y(x_n)\cdot t_n > 0$ for all $n = ...
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Comparing area under ROC for validation and blind set

I used this tool http://vassarstats.net/roc_comp.html to calculate the significance value between the area under the ROC of my validation set and area under the ROC of my blind set. Both were ...
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How can I tell if my classifier is significantly better than chance?

I have a fairly rubbish classifier for a task assigning samples to one of the classes {i, e, l, g}. I would like to know whether it's actually better than random. All the discussion that I can find is ...
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Deep learning for trajectory classification - python

I'm intending to obtain advice or suggestions about a classification problem. I'll attach a brief example of the training data and associated figures below to describe the problem and the information ...
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Improving Random Forest Classifier Design Python

I'm looking to create a Random Forest Classifier to predict NBA standings x years in advance. The goal is to show the chances of a team being one of the five worst teams, the 6-10th worst team, 11-...
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Two types of kNN classifier algorithm

The following is copy-pasted from a lecture note on machine learning... k-NN classification can be realized in two ways: Selecting for the classified sample it's $k$ nearest neighbors for each class ...
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Evaluating a true classifier e.g., pregnancy test

Most alleged "classifiers" give probabilities of class membership. One can use a threshold to map those probabilities to discrete categories, but statisticians are in favor of direct ...
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What is the actual Mechanism of Boosting for building the Models?

I couldn't able to find the proper step by step procedure to understand the Boosting Mechanism, how does it build the models and the data used to build it. So, I have gone through tutorials which led ...
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Loss function for NN classification with subclasses

There is a similar question asked here but with no answers. In short, I have a few classes, each separated into another few subclasses. E.g. ...
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How to print wrong prediction images which is not trained [closed]

I have taken some classes from RVL-CDIP dataset ( Such as Note, Appraisal, Credit report) for experiment. I have used sequential classifier Model (using keras and tensorflow) for classification of ...
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Multi-class optimal threshold selection

What are the methods typically used for optimal threshold selection of multiclass classification problem ? I implemented the detector/classifier and generated the confusion matrix, but would like to ...
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Train score much lower than test score in cross-validation

The task I am working on a binary classification problem using a SVM classifier. The feature matrix X (650x20) holds brain activity features for 12 subjects. The target array y (650x1) carries the ...
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26 views

Determine probability with model accuracy

I was given today with this question: product x represents 2% of the sales volume Binary Classification model, result 1 if the model predicts that a buyer will buy product x, result 0 if the model ...
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Why is my neural network always 100% sure about it's prediction? [closed]

I trained a neural network performing binary classification. Whenever I test it, it is 100% sure about the prediction even if it's wrong. Is this a sign of overfitting? Thanks.
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What does the KNNclassifier do differently from the KNNregressor?

While I understand how KNNregressor will fit a line to data by taking the k nearest neighbors to a point and averaging them, I am having more trouble understanding what KNNclassifier does with respect ...
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Identifying type of graph using Machine Learning [duplicate]

I'm new to Machine Learning. Q: How can I use ML to classify graphs? My goal is to create a machine learning model that can take a function as an input and return a string that classifies the graph as ...
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Help with understanding metrics for imbalanced classification

I am trying to train a neural network to classify chest X-ray scans as my final MSc project. I have a dataset of 13808 image, 3616 labelled COVID, 10192 labelled normal, so the ratio of COVID to ...
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Is it important to know the variance of correctly classified instances in cross-validation?

To create a classification model for the Iris data set, I used the J48 (C4.5) algorithm in Weka. To evaluate the model, I used 10-fold cross-validation. These are the cross-validation results: ...
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1answer
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BIO vs. BIEWO tagging scheme

In one of my classes, the lecturer explains the tagging scheme BIO (begin, inside, outside) which I knew of and I perceive as the standard. Then he introduced BIEWO which additionally has a tag for ...
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Logic of Sklearn Bernoulli Naive Bayes Classifier when the the predictors are not even binary?

I know the mathematics behind the Naive Baye's Bernoulli Classifier Algorithm and it is used to calculate the probabilistic results. As we know the Bernoulli Naive Bayes Classifier uses binary ...
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Do I correctly apply hierarchical clustering and K-means on the resource-selection-function values?

I'm trying to find the best way to classify bivariate point patterns in spatstat according to the relationship between two point species: Point pattern ...
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Point pattern classification with spatstat: how to choose the right bandwidth for the kernel density?

I'm trying to find the best way to classify bivariate point patterns in spatstat according to the relationship between two point species: Point pattern ...
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3answers
364 views

Categorical Variable with too many levels for Decision Tree

I am trying to build a decision tree but the problem is I have too many levels on one of my categorical variable. The variable is 'source' - It indicates the source website where the user came from. I ...
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1answer
223 views

Classifier returning probabilities that dont stretch in the entire [0,1] range

I am building a classifier for imbalanced data (~2%). I am using LightGBM for the time being but I guess the question could apply to all binary classifiers. The returned probabilities do not extend in ...
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In classification algorithms, does the probabilty changes depending on how you define the label? and if is unbalanced?

I have been struggling with this topic for a while, and on this site are multiple answers but none of them answers completely my question. For example in Which class to define as positive in ...
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Is there a way to combine features there is correlated but is important(time series) in a classification problem?

Suppose I have variables indicating access by months, consults by months (and other variables) and I want to predict the digital propension. Digital_acess_mont01 Digital_acess_mont02 ...
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How to tune parameters for novelty detection with only normal dataset

There exists multiple novelty detection methods. I'll discuss two: One-class SVM LOF Both of them have parameters. For example, the SVM has a $\nu$ parameter and if the SVM uses the RBF kernel, it ...
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Form matrix for Churn Rate model: deal with action made by client through time

Assume pseudo case: I have retrospective data about client X (c_id in following dataset) in long table which corresponds to his transactions amount and volume for previous 3 months: ...
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227 views

Which class to define as positive in unbalanced classification

In a classification task, why do we usually choose the minority class as "positive" case for response variable? For example, if there are 1000, 9000 for class A and B respectively, we ...
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Can log loss be an evaluation metric for classification models?

I read several posts online about evaluation metrics for classification models. Only accuracy, precision, recall, F-1 score, ROC, AUC, Confusion matrix are mentioned. However, I found a couple of ...
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1answer
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Would it be wrong if I picked a model with less accuracy in this case?

I have some data containing a list of 2000 questions, some features such as length of question or time needed to solve it, and a score between 0 and 1. The closer to one, the more people solve it ...
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Which features should I choose, fast overfitting ones or slow convergence ones?

I'm doing an audio/speech classification project. For the input features, I used two sets of features to find out which ones are better. Feature set 1 (Normalized spectrogram) ...
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Can I conclude this is overfitting and how can I reduce it?

I try to fit a GAM with mgcv in R for classification. The dependent variabele (WinFlag) is true or false. The independent variables are two continious variabeles (x1 and x2) and one factor variabele (...
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1answer
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How to categorize/classify functional relationships? [R]

I have a dataset of bacterial abundance at specific degrees of temperature. Each line is a different bacterial species and each column is the abundance at a specific temperature. My dataset includes ...
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1answer
33 views

Low MAE, RMSE, RMSLE and MAPE, but also a low R^2

I have a dataframe containing the IDs of 2000 questions, a list of scores representing difficulty, and the following features: how often the question was answered, how often the answer has been ...
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2answers
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Decision boundary between two Gaussians of unequal variance

This question is concerning a similar problem as mentioned in this question. The only difference is that in my case the variances are unequal. To recap, consider a two class scenario. At the decision ...
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What is the relation between Linear Classifier and Linear Decission Boundary (or Non Linear Decision Boundary)?

As we know (Wikipedia Definition): Linear Classifier makes a classification decision based on the linear combination of the feature vectors. Mathematically : $y = f(\sum w_i x_i)$ So , $f$ is our ...
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Is Average Precision for ranking problems computed the same way as in classification?

For classification, we calculate Average Precision (AP) as area under the PR curve. But for ranking problems, we calculate it via the relevance of the items at certain ranked positions. Are these 2 ...
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1answer
178 views

Does my neural network have a normal distributed output?

I have trained a binary classier neural network and I was interested in potentially using some standard statical analysis on it although they are often reliant on if the underlying distribution is ...
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Comparing human scored image classification and neural network image classification

I'm not very experienced with most statistical methods so I was hoping someone could help point my in the right direction. I trained a binary image classifier neural network from which I evaluated on ...
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How to model/classify user's activity based on BEHACOM dataset

I want to classify user's activity from BEHACOM dataset into three types: active, middle and inactive considering keystroke count, move movement average duration and click speed average duration over ...
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24 views

If my p-value is greater than the critical value can I assume both distributions are similar?

I have trained a binary classier and have tested it based on a validation set and confirmed those results on a test set. I was wanting to show that both the validation and test set show similar ...
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1answer
27 views

Is it correct to apply standard techniques for a confidence interval calculation to the result of a neural network?

so I have a binary classier from which I can evaluate on a test set and get a proportion (p) of which the classier has correctly gotten right. I then apply the following function to determine the 95% ...
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A metric in common between a Poisson regression an a binary classifier (Gini for a regressor?)

The general question is how to compare these two models: a regressor for Poisson-distributed count data and a binary classifier? A more specific one: how to compute Gini for the regressor? Background ...
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101 views

What Cross Validation results actually tell about Bias and Variance?

I am trying to get a deeper understanding of the common ML pipelines and I have some doubts regarding Cross Validation, why do we really use it and what does it really tell us about Bias and Variance. ...

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