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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5 views

Effect of multi colinearity on machine learning problems other than regression

High colinearity between variables/ features is a problem when peforming regression analysis. But does the same stands true for Classification, ...
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Oversampling for imbalanced time series classification

I'm doing multivariate time series classification (two classes) with GRU/LSTM models. Each observation is a multivariate time series with one label (0 or 1). But the two classes are highly imbalanced. ...
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36 views

What are w and b parameters in SVM?

I've read almost every article on the web, every question regarding SVM here, but I still don't get how to calculate w and b, how did they appear in formula, what is weight and what is bias: $$\vec{w}...
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21 views

Rain overflow modeling: Categorical variables or separated models?

I'm working on a project where I have to predict rain overflow due to rain for 5 sewer locations. I have a file which tells me if there is a rain overflow (=1) at a given date for a given sewer or no (...
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19 views

Classification Model with multiple records per user

I have a dataset where each record is a purchase, with user, datetime, low-cost/high-cost option chosen (y, boolean), quality, and opportunity price (the lowest priced option) as variables. The goal ...
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How to use neural network for regression

I am new in the field of machine learning, and I am sorry if my question may seem naive to most of you. I am following the "Machine Learning" course by Andrew Ng on Coursera. At this moment, I am ...
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What is the difference between Feature selection techniques for Classification versus Regression?

Is there any difference between feature selection techniques and methods for Classification, clustering, regression? For example, features with high colinearity are never preferred in Regression. Is ...
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1answer
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What are the important Factors for Feature Selection in Classification Problems? [closed]

While doing a classification I have to choose from the ocean of choices at every step like model selection, performance criteria selection and all. But the important two things I get confused most of ...
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Does the order of the columns in an CSV file affect the performance of my neural network?

It's a classification problem. I have a big dataset ( CSV file) of flights, where each flight is depicted as a set of variables ( ...
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Wide feature matrix but few examples

I have a data set of 125, with only about 25 (20%) positive cases. The features, lets call them Feature1, Feature2 up to Feature250, can be easily grouped (since they all describe responses to ...
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Is K = 1 is good for KNN, when error is min, accuracy is max and even AUROC is Max for that value of K?

I am getting highest Accuracy for K =1 in KNN, Max AUROC, and lowest Error. however, I was taught that when K = 1, then its always going to be over-fitting mode, and hence I am asking the question is ...
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What is threshold in ROC curve? [duplicate]

Whenever I read about ROC, people say that it is graphical representation of True Positive Rate value and False Positive Rate at various threshold. Whenever I read in detail, people explain that ...
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38 views

An interesting task on machine learning

There are 5 programs. Each program is a binary classifier, which classifies letters - "Spam" and "Not spam." All classifiers determine the category correctly in 60% of cases, regardless of other ...
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What can I do when the values for my attributes is mostly 0

I was trying to do classification for census-income (KDD Data Set) (https://archive.ics.uci.edu/ml/datasets/Census-Income+%28KDD%29). The aim is to classify people who gain income >50 k and <50k. ...
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26 views

The precision recall AUCs calculated by two different packages are different?

I used the dataset cars as an example ...
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Tuning threshold from multiclass ROC for Gradient Boosting Classifier?

I have created a ROC curve based on the output of a multiclass Gradient Boosting Classifier (See Figure below implemented from Yellowbrick ROCAUC: https://www.scikit-yb.org/en/latest/api/classifier/...
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ROC curve under diagonal?

I trained an SVM to classify images based on some extracted features (using the ISIC dataset). The resulting ROC curve produced by sklearn looks like this: I have don't quite understand the line for ...
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Fitting a linear model on XOR [closed]

On the XOR problem, there is no linear decision boundary (linear in $x_1$ and $x_2$) that will be able to perfectly classify all 4 points. The weights and bias of a decision boundary that classifies ...
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Derivation of Bayes classifier in Murphy's book

I am reading Kevin Murphy's Machine Learning book (MLAPP, 1st printing) and want to know how he got the expression for the Bayes classifier using minimization of the posterior expected loss. He wrote ...
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Consistency on SVM results

I have a conversation in my office regarding the results on SVM classification. As far as I understand it SVM does not contain any random initialization that could produce a different result on ...
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How to estimate the marginal distribution of binary dependent variable with respect to one predictor using a classifier?

I have a dataset with a binary dependent variable $y \in \{0,1\}$ and a set of predictors $x1,x2,..,t$. Here, $t$ is the time in minutes (in 24 hrs, that is $t \in (0,1440)$). I want to estimate the ...
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39 views

Using data from multiple sources with same features in classification problem

Suppose that I am doing a classification problem where I classify people into two categories as bullied or not. In such type of ...
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28 views

What can be done to improve generalisation ability in a classification system?

I know the importance of the generalisation ability in supervised learning but what approaches can be taken to improve the generalisation ability in a classification system?
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Worse model after removing duplicates - Multiclass classification

I am currently evaluating a set of models trained on product names (149k obs) to infer their category. Products are divided into 3 hierarchycal levels (which I've collapsed into 198 unique ones to ...
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Missing name in confusion matrix?

In a confusion matrix, what's the name of the percentage of cases I predict as positive out of the total population? I am in the position of having to use this metric for my project, but I can't find ...
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Preprocessing during a Kfold cross validation

I have noticed from various sites online that preprocessing and feature selection when dealing with Kfold Cross Validation suggest that the preprocessing and feature selection should be done on the ...
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33 views

How to change the distribution of classes?

I have two classes [0,1] and I want to evaluate algorithm on different distributions of classes. I did label flipping incrementally such as 0%, 10%,20%,....,90%,100%. Does label flipping change the ...
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Maximum level of label noise for binary classification so that dataset is “Learnable”?

Assume we have an imbalanced dataset (minority label frequency 1-20%), where subset of samples have their labels randomly flipped. Now, of all samples with positive label (the minority class) in this ...
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31 views

What is the purpose of the generalization error bound?

I could not understand what is the purpose of the generalization error bound, why do we need to calculate it?!. How does the generalization error bound work with 1-nearest neighbour algorithm ?. Does ...
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1answer
100 views

Bayes Optimal Classifier for multinomial classification

I understand the meaning and how to deduce a Bayes optimal classifier in binary classification, but I am not sure how to derive this in the context of multinomial classification. Do we use the naive ...
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Assumptions of Logistic Regression and Naive Bayes Classification Problems

Am trying to understand the difference between assumptions to follow for Logistic Regression and Naive Bayes. As per my knowledge both Naive Bayes and Logistic Regression should have features that go ...
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About learning curve

I have intended to find overfitting or underfitting cases. I have used MLP classifier and Logistic regression of scikit-learn. How do I know which is a good fit? Or Which one underfitting or ...
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1answer
19 views

Model evaluation when training set has class labels but test set does not have class labels

Training set and test set are separated in 2 files. The training set has class label and Random forest, svm, and KNN can fit. However, the test set does not have class labels. How do you evaluate the ...
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What statistical classifiers can use unlabelled data to enhance their performance similar to a transductive support vector machine?

I was wondering if statistical machine learning methods like tree based methods, ANNs, logistic regression can make use of unlabelled data to enhance their performance similar to the way a ...
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Why does the formulation of the SVM problem has the bias (something we try to optimize) as a part of the constraint?

The common formulation of the SVM problem is $$\min_{\theta, \theta_0}\frac{1}{2}||\theta||^2$$ $$\text{ subject to: } y^{(t)}(\theta \cdot x^{(t)} + \theta_0) \geq 1, \ t=1,...,n,$$ However, it ...
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How much is the Class Imbalance Problem rates?

I'm working on a data set and wanted to know is there a standard rate about Class Imbalance problem or not? I have 47 samples in Class A and 150 Sample in class B , should I use Class Imbalance ...
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Cosine Similarity for Classification to EM Cluster?

Perhaps my question sounds naive, uncovering the very little knowledge that I have in the field of Statistics, but is very urgent to get a solid answer or trigger for further insights for my concerns. ...
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Passive Combination of two Classifiers at the level of Class Labels

I have three classifiers - 1. Vision based classifier trained to detect class labels such as pedestrian and cars 2. Radar based classifier to detect same class labels 3. Lidar based classifiers to ...
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1answer
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Common practice for validating classifiers in medical statistics

We are writing a paper, where we suggest a new classifier. We have a data base with about 1400 medical cases. Would it be sufficient just to divide the dataset into training 70% and test 30%? Or ...
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1answer
40 views

Classification methods for univariate time series

Our team wants to develop a machine learning algorithm for classification of univariate data. Our data is a live feed from a position sensor placed in an injection molding machine. We want to be able ...
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Avoiding bias when labelling to construct a test/train data set

I have a multiclass classification problem, where I have a massive database of text documents and the objective is to predict what class a document belongs to. There are many classes (>10) but the ...
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1answer
19 views

In ordinal classification, how can adjacent accuracy be expressed mathematically?

Say that we have an ordinal classification problem where we have an ordered set of classes $\mathbb{C} = \{ C_0, C_1, \ldots, C_{K-1} \}$. We have $N$ samples, where the true and predicted classes of ...
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1answer
41 views

What are different time-series types?

I am having a time-series classification problem. I tried to using movingaverage and firstdifferences as two models to perform ...
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Identifying optimal values for a continuous variable using recursive partitioning analysis

I would like to identify the optimal values of a continuous variable based on the hazard ratio using recursive partitioning as shown in the diagram. In the diagram shows hazard ratio of overall ...
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11 views

Imbalance class data resample gets results in overfitting Random Forest

I am working with a very imbalanced dataset (16k lines, 4% in the minority class), using random forest to for a binary classification. I’m using the Python Sklearn implementation of ...
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After applying SMOTE, the class distribution doesn't match the real world. Is this a problem? [duplicate]

I have an extremely unbalanced dataset with two classes: 1: 1,800 # class 1 0: 40,000 # class 0 This is real world customer data of churned/not churned If I ...
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1answer
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Cropping input images Neural Networks

I'm creating a simple neural network for image classification,I had some doubts about the input images. Let's suppose i'm trying to classify (for example) a bear and i have an input image like this: ...
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2answers
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How to create training data for CNN using remote sensing imagery

Before I start with the issue I would like to touch base with some background information. I had been working with Random Forest for classification of Remote Sensing data, here the classification ...
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Gaussian RBF vs KNN explanation

I was studying SVM ML alghorythm and I was wondering about solution for non-linear cases. As I understand it for know, SVM tries to find hyperplane or object in defined n-dimensional space, which ...
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Validity of PU learning when using character-level encoding and CNNs for text data

I'm trying to classify a large set of documents (~100M) as valid or invalid, based upon a small given set of labeled valid documents (~3k). I'd like to know if the PU learning approach described in ...