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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285 views

Would the support vectors in SVM algorithm change with scaling of the functional margin?

Would the Support Vectors in the SVM algorithm change every time that I change the functional margin ? The optimization objective in the SVM algorithm is this - The rest of the SVM optimization ...
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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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Minimal dataset for the classifier

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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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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193 views

Clusters as input for classification

I'm currently performing clustering as a batch job and then in real time I'm assigning new points to cluster whose centroid is closest to new arrived point. The other approach that I see is to ...
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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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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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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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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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276 views

Repeating prediction to increase accuracy

I want to predict the outcome of one data row and accuracy of the model is 50%, so if I re-predict it for 3 times in a row then what happens to the accuracy? Will it be 87.5% (50+25+12.5)? Or will ...
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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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19 views

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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754 views

Why One class SVM seperate from the origin

I don't understand what is the intuition behind the idea of finding a hyperplane that separate the training data from the origin if the feature space. To me it would be more intuitive to create a ...
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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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1answer
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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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38 views

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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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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766 views

Multi class classification using Naive Bayes

I have components basically divided into two main categories. AWS and Azure. For eg: ...
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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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What is between regression and ordinal classification (or called ordinal regression)?

There are many articles explaining the difference between regression and ordinal classification, most of them mentioned that regression is for continuous response while ordinal classification is for ...
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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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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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Clustering spatial data [closed]

Suppose a set of approximately 350 elements. Each element is represented by a matrix of 16x16 values, as shown in figure. My aim is to group (cluster) such elements. To give some context: each ...
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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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1answer
319 views

Using multiple TF-IDF matrices on a classification task

I have a dataframe with a certain number of columns of type text. Let's say column A has the name (random names that cannot be used as keys but resemble sentences) and B has its description(a bigger ...
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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
222 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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2answers
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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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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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1answer
375 views

How to combine multiple signal data in my ML model?

I'm doing a task where I need to work with healthcare data from a few different sources. For example, one is an audio signal recording while another is biometric signal reading such as ECG. Both of ...
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2answers
220 views

How does graph classification work with graph neural networks

I am reading the paper The Graph Neural Network Model by Scarselli et al. I understand how node classification works. I am having trouble understanding how graph classification works however. In ...
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2k views

Combining multiple classifiers

I am trying to do a binary classification of text articles into {relevant, non-relevant}. The text articles have following features: [[article text, ...
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1answer
218 views

Classification: training sets different sizes

I'm building a classifier for text analysis sentiment. I have a large training set for positive, neutral and negative mentions. Should the training data sets be similar in size? Currently my ...
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1answer
375 views

machine learning techniques for classifying images with timestamps

I'm doing 2-class image classification (determining whether an object is present or absent in images) with CNNs. The dataset is a bunch of photos with continuous timestamps. And I observed that ...
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2answers
226 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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1answer
68 views

What methods can be used to overcome the tie-breaking when using majority voting in ensemble?

What methods can be used to overcome the tie-breaking when using majority voting in ensemble? I read that Weighted majority voting can help; however, it wasn't effective on the dataset I am using in ...
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1answer
163 views

Conceptual diffiulty in understanding multiclass classification

Given a high dimensional feature vector x in $R^D$, I want to map it to an L bit vector, $L << D$, z = h(x) in $\{0,1\}^L $ using a function h while preserving the neighbors of x in the binary ...
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1answer
23 views

Reproducible results with Keras [closed]

I was trying to classify some images using VGG16 and I realized if I run the same code a second (or third) time I won't get the same results even though random_state in train_test_split is set to 0. ...
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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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When is unbalanced data really a problem in Machine Learning?

We already had multiple questions about unbalanced data when using logistic regression, SVM, decision trees, bagging and a number of other similar questions, what makes it a very popular topic! ...

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