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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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How can we interprete the results generated by SVM?

I am using SVM for classification purpose. I got results but I am not understanding how to interpret its results and aslo how can I know the contribution of each independent variable in the prediction ...
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Minimising CV standard deviation to increase accuracy

Does minimising the standard deviation of CV folds have any correlation with model accuracy as a theme? I've noticed that changing the order of rows in a training data can change the AUC for each CV ...
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Classification: keeping false positive in training set

I am working on a classifier, with a large number of possibles classes, and also a no class class. My training set is made of the output of a hardcoded logic that ...
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How to explain the decision rationale of time series data classification task using deep learning model

Is there a way to explain which part of the time series data the model is looking at in task that classifying time series data (e.g. video) by deep learning model? When deep learning model using RNN ...
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How should I resample the training and testing set with imbalanced data whilst having meaningful performance metrics?

I have an imbalanced dataset of approx. 200 positive and 800 negative examples. I run nested cross-validation where i=5 and j=5; (i is inner and j is outer). The cross-validation procedure isn't the ...
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Why does pROC roc work with non-probability predictions?

With the pROC package, I can do this: true <- c(1, 1, 1, 0) predicted <- c(0.5, 0.1, 0.6, 0.1) roc(true, predicted) which gives as expected: ...
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Loss function for a risk neutral binary classification

For binary classification task, with samples labeled $y=0$ and $y=1$, a neural network has one output node with sigmoid activation function, producing predictions $\hat{y}\in(0;1)$. Is the following ...
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Can I use logistic regression to calculate the optimal classification threshold between two class based on a single contiguous variable

I have a single contiguous variable, x, and there is two possible classes/labels y for n observations. I need to build a simple binary classifier with a threshold on x so that the number of ...
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Linear Discriminant Analysis vocabulary question

I am doing a descriptive LDA on a dataset with two (known, easily separable) classes and many features (and many more observations). I intend to use the latent variable values as a dimensionally-...
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PySpark ML: What to do when a logistic regression model is not generalizing?

I created a logistic regression model using PySpark ML. My feature set consists of both categorical and continuous features, and I ran the following to pre-process them: Categorical features: All of ...
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1answer
48 views

Best Suitable feature selection method for ordinal logistic regression

I have 33 variables my dataset, I need to omit some less significant features then, which is the "best suitable feature selection method " for the Ordinal Logistic Regression?
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Expected Classification error using indicator functions

I am given three variables (latter two are binary) $A,B$, and $C$, where $A$ = input vector, $B$ = whether data was chosen, and $C$ is the true label. $A$ and $B$ are conditionally independent given $...
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Nested CV with Online Learning

I have a time series binary classification dataset. I am implementing an online learning Logistic Regression algorithm in Sklearn and am cross validating with Sklearn's TimeSeriesSplit method. I am ...
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Probability score for Hierarchical classification models

We've a hierarchical classification system in place; where each level produces predictions with a probability. Here's how the hierarchy is setup Top level: 1 model; ~25 classes Level 1: 25 models(=25 ...
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Setting the Success Class in R [on hold]

This is very basic. I am attempting to set "Against" as the success class using the example in the console where "Light" is the success class. What am I missing as I set up this function? I The ...
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PCA, SMOTE and cross validation - how to combine them together?

I was reading a lot recently about PCA and cross validation and it seems that the majority call it malpractice to do PCA before cross validation. I would also like to perform SMOTE, but there is a ...
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The Importance of Initial Conditions in Autoregressive Modeling

I am developing an algorithm to classify time series by using autoregressive modeling. I have used the following two alternative methods, after fitting an AR(p) model to time series: Method 1: ...
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Soft Margin constraints in SVM

I have understood the constraints of Hard Margin SVM but stuck at Soft Margin SVM. The Objective Function along with constraints of Soft Margin SVM is given below. such that and The slack ...
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Skewness Impact on Classification

I have a dataset with 134 attributes and my goal is to build a binary classification model. While exploring the dataset, I found that there was high skewness present in the attributes. I wanted to ...
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Coefficients in Optimal Separating Hyperplane(SVM)

This question is closely related to Elements of Statistical Learning p.132 - p.134. I want to reproduce the , in p.129 and p.134, respectively. This is a toy example without given any data, so I ...
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Statistical test to measure significant difference between 4 binary classifiers?

I have four versions of a neural network hardware. I am testing the hardwares to classify a single binary input into a binary output. The ANN hardwares are trained using a very simple relation, the ...
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How to reduce false positives when novel negative images are visually similar to training images?

I am noticing that my ResNet model is showing some false positives in cases where novel negative example images are somewhat visually similar to positive examples. In these cases, it's not simply ...
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1answer
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How to do backward subset selection on a random forest model for classification?

I'd like to identify the most predictive features for my classification model. I'm using this data. Here is a sample. This is my code. I'd like to use the predictors to predict loan status. ...
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1answer
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Loss function for binary classification with ordered subclasses [closed]

I have a set of companies that I want to classify as having either >= 250, or < 250, employees (so it's a binary classification), however my training dataset contains more detailed information ...
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GridSearchCV with one-hot y: prediction yields 1-dim array

I run a classification by means of a neural network, thus my y-values are converted to a one-hot matrix: ...
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Linear Discriminant Analyses (LDA) [on hold]

in Linear Discriminant Analyses objective function, what is the effect of Trace? in other word what is the difference between (A'SWA)/(A'SWA) and Tr(A'SWA)/Tr(A'SWA) ??
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Cohen's Kappa - extreme disagreement doesn't register as strongly as extreme agreement

I'm wondering why, for the following data, the Kappa statistic wasn't $\pm 1$ but instead $-0.95$ for complete disagreement and $1$ for complete agreement. I expected them to have the same value. ...
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Multi-label classification with neural networks: Are correlations between class labels taken into account?

I am solving multi-label classification problem (assigning each image 1 to N labels) and want to use neural network (like in this post). Does this approach take correlations between class labels into ...
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2answers
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Which classification model should I choose and Why?

I am working on a research-based assignment where I suppose to build a 3-class (bad, medium, good) classification using SVM. The dataset provided is imbalanced. The train:test splitting ratio is 75:25 ...
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“Hierarchical” Random forests?

Background I am using Random Forest to classify ~900 objects based on a large number (> 80) predictors. I split these 70:30 for training and testing. The overall model does fairly well, giving an ...
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1answer
50 views

How to predict routes using clustering data

I've been working on a ship route prediction algorithm such that given the past and current trajectory of a ship I am able to estimate the future one. The trajectories are represented as a sequence of ...
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QDA number of parameters

If we include to Linear Discriminant Analysis quadratic parameters we get Quadratic Discriminant Analysis classifier. Number of parameters is $(K-1)\times[(p\times(p+3)/2) + 1]$ where K is number of ...
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SVM Classifier with RBF kernel works well with cross validation on training data, but fails on test data. What's going on?

According to the Practical Guide: We propose that beginners try the following procedure first: Transform data to the format of an SVM package Conduct simple scaling on the data Consider the RBF ...
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Unsatisfactory prediction error - is it possible to improve accurancy? [duplicate]

I'm green in ML field and I try to classify user reports to valid/invalid. My dataset contains of Valid - 7355 samples Invalid - 6285 samples So, I devide data into train and test ...
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How to train HMM for ECG beat types generation?

I've read papers on using a regular Markov Model to do this, manually defining the transition matrix. I wanted to use a Hidden Markov Model to more accurately capture these transition probabilities ...
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1answer
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Is this Tensorflow bias vector shaped correctly?

In the text I read the following: I’m confused on the dimensions of the bias vector. How can we add a(m,1) vector to a(1, p) ...
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1answer
27 views

High variance across k-fold CV classification accuracy estimates

I'm tackling a binary classification task, data is class-balanced. I'm training a multi-layer perceptron on this data. To estimate accuracy on unseen samples, I decided to perform $10$-fold cross ...
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1answer
22 views

Train classifiers on a subset and validate on a full dataset

Let's say I want to train a classifier and use it on 20% of "known" data; the rest of the "known" data is reserved for training (60%) and testing (20%). In the end I also want to apply it on "unknown" ...
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LDA (MASS) output in R

I know this question has been asked before, but I still don't entirely understand the answers that have been posted. If I run LDA (from the MASS module), it reports the estimated priors, means, and ...
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Modeling multiple outputs - one model or several

Recently at work I enter an interesting discussion that I thought could continue here and receive your output. I'm trying to model some data that have as an output a categorical variable (let's say X)...
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how to deal with dependent variables as a list of integers in machine learning

In my working project, I'm building machine learning models to predict number of hospital admissions from patient profile. The dependent variable, # of hospital admission are integers from 0 to 8. ...
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Understanding a Classification Tree applied to the iris dataset

I used the iris dataset for a simple regression tree to see how things work. I am generally interested in two aspects: Why does the tree not use the Sepal data to improve the classification What do ...
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1answer
26 views

What are some good robust loss functions for binary classification using LDA?

I am doing a project where I use LDA for binary classification. I want to know how it performs when there are outliers. What are some good robust loss functions for binary classification?
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1answer
25 views

Number of parameters in Bayesian Classifier

Problem Assume we have a Bayesian classifier with the three following features to determine whether a software user is a student, an ...
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19 views

Multivariate time series with a binary dependent variable

I am currently working on a multivariate time series data set with 1 dependent variable (y) and 60 independent variables ($x_1$,$x_2$,....$x_{60}$). The dependent variable is a binary variable (0 or 1)...
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Explanations needed for a univariate classification method based on solving a quadratic equation [on hold]

I read a piece of codes on classifying image hue values into three class with derived thresholds. The thresholds are calculated by simply using a quadratic formula. The related documentations for the ...
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Hierarchical vs multi-class, when to use what?

I am looking for suggestions from machine learning research perspectives about using hierarchical classifier vs multi-class classifier. For example, if I have to classify 3 classes, let's say, 2 ...
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15 views

Suitable performance metric for an unbalanced multi-class classification problem?

I have an unbalanced multi-class classification problem with the following class distributions: Class 0: 17.1% Class 1: 63.2% Class 2: 19.7% I am using scikit-...
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Why should binning be avoided at all costs?

So I've read a few posts about why binning should always be avoided. A popular reference for that claim being this link. The main getaway being that the binning points (or cutpoints) are rather ...
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1answer
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Given two features, one a string and other a categorical, what are the encoding rules?

I have two features in my dataset I'm using to help predict a binary outcome. Based on my features, I'm trying to figure out which I need to drop a dummy to avoid the dummy trap. One feature is a ...