Questions tagged [classification]

An instance of supervised learning that identifies the category or categories which a new instance of dataset belongs.

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How to build a classifier for determining if two pairs are a match?

I'm trying to build a classifier that can determine if two addresses are a match or non-match. Let's assume I have a data set of address pairs that have a match or non-match label. I'm new to ML so ...
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Supervised family classificcation with HMM

I have seen that HMM can be use as supervised family classification problem by train one HMM model per each classhttps://stats.stackexchange.com/questions/91290/how-do-i-train-hmms-for-classification. ...
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The most informative curve for imbalance datasets

For the imbalanced datasets: Can we say the Precision-Recall curve is more informative, thus accurate, than ROC curve? Can we rely on F1-score to evaluate the skillfulness of the resulted model in ...
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How to measure model success in production

I have a model running on a productive system. The model predicts if some lead will become a sale. How would you develop a check, which checks the success and the accuracy of the model? There is a ...
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How to deal with class imbalance in a neural network?

Suppose we have a game and its action space contains two possible actions: A and B. We have a labelled dataset of state-action ...
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How to deal with imbalanced text data

I am working on a problem where I have to classify products into multiple classes (more than one) based on product descriptions. For instance: "Tresemme shampoo and conditioner - sulfate-free" = ...
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Logistic regression for classification?

I have a dataset with most columns having Boolean values and categorical values. A sample of it is: ...
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Negative value in information gain calculation through gini index

I am trying to determine the root node for the decision tree on given data annual income target variable has been renamed as ...
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How to train a simple Machine learning model in batches?

I have a dataset for multiclass classification of text data. The number of samples in training data are 1,20,000. If I extract features using TF-IDF vectoriser of the sklearn library it gives about 80,...
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How to use spectral clustering to predict?

In an academic paper, they talk about using a nearest neighbour algorithm to predict the cluster of a new point. And how the number of nearest neighbours is set to 10 in their example. What do they ...
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Positive or negative impact of features in prediction with Random Forest

In classification, when we want to get the importance of each variable in the random forest algorithm we usually use Mean Decrease in Gini or Mean Decrease in Accuracy metrics. Now is there a metric ...
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What's the best way to do classification basing on two given datasets (annual data and daily data)?

I want to do binary-classification basing on two given dataset, one is annual statistical data of a company and has the label I should be able to predict like this: ...
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What are the strategies can be considered while designing a model to avoid the finite-context length and time-stretching issue?

What are the strategies that can be considered while designing a model to avoid the finite-context length and time-stretching issue?
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Which of the scikit learn classification algorithms accept Sparse matrices?

If I have to use scikit-learn(sklearn) library for classification and the feature matrix is a sparse matrix then which of the classification algorithms of this library can be used by me?
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Classification of OLS regression coefficients

A variable $A$ (reaction time) is log-normally distributed, i.e. $\log(A) \sim \mathcal{N}(0,\sigma^2)$ and is linearly dependent of $n$ variables $X = (X_1,\ldots,X_n)$, i.e. \begin{align} A &= \...
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Predict best score on unlabelled test set

Data I have one dataset with $1500$ data points, each with $\sim 23 000$ features (gene expression data, if that matters). However, I've split this dataset into a labelled training set of size 1000, ...
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Methods of working with unbalanced dataset

I've got problem where I need to classify the images for about 400 classes and to do this I'm using model with neural network. In my dataset (about 300k images) there are classes represented by about ...
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Classification - get some label value to check how close to another class (Python)

I am doing text classification in python with 3 alghoritms: kNN, Naive Bayes and SVM. I have 3 classes - easy, medium and hard. The accuracy is quite fine. Is there a way to check for new text its ...
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Advantages and disadvantages of using classification tree

I was working on a project and was trying to validate my decisions. I wondered why would I want to use a decision tree over more powerful algorithms like random forest or Gradient boosting machine ...
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Keras LSTM predicts every signal in the same category

I'm working on a project that involves signal classification. I'm trying different models of ANN using keras to see which one is better, for now focusing in simple networks but I'm struggling with the ...
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will increasing threshold always increase precision?

here precision at threshold 0.85 > precision at threshold 0.90. shouldnt it be the other way round? increasing threshold will reduce False positive and precision will be greater than before?
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Classification algorithm poor results, tips needed

I'm new to classification and I'd like to have some tips from someone more experienced. I have a dataframe of used car (from this link: Dataframe) and I'm trying to apply some classification algorithm ...
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How to implement SVM from scratch?

I am trying to build a SVM from scrath and I would like to maximize this Lagrarian expression: I know what variables means but I would like to know how this maximization is implemeted. Should I start ...
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1answer
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When is z-normalization not needed when using DTW?

I'm hoping to get some answers to a question I have regarding normalization of DTW datasets, in particular datasets in which two time-series shapes with similar shapes but differences in magnitude are ...
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How to obtain a typical object from each class of my neural network classifier?

I have trained a deep neural network to classify vectors of a given dimension over 4 classes. Is there a way to "reverse" this neural network in order to obtain what my network considers a typical ...
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Inconsistency on A. Graves' original Connectionist Temporal Classification (CTC) paper?

Here's the link to the paper. For the forward-backward algorithm, they introduce $\alpha_t(s)$ as a definition in eq. (5). Then they give a recursive formula for it (eq. (6)), and its initialization. ...
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Pruning in Decision trees

Following is what I learned about the process followed during building and pruning a decision tree, mathematically (from Introduction to Machine Learning by Gareth James et al.): Use recursive binary ...
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K-Nearest neighbor in transformed space

When googling "weighted KNN", the results appear to be focused on weighting the nearest neighbor values after those neighbors have been determined. I'm looking for something that assigns a level of ...
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How to identify corresponding record of a car from a semi-structured string?

I am trying to build an application that can take a record of a car from different websites, compare it to data i have in a CSV file and return me the matching row. Each website will present and ...
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1answer
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WEKA Random Forest and numerical attributes

I am working with Random Forests in Weka. I thought the ID3 algorithm is used to find the best split attribute at each level. But after reading a bit I noticed that ID3 can not handle numerical ...
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What does the classification report interpret? Class 1 indicates abnormal data

How to interpret the report and How is precision, recall values are calculated for individual class labels. What is the significance of macro avg ? Does this report signify a good predictions by the ...
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1answer
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Which ML classifier is appropriate for me if all of my features are categorical?

My dataset contains four features. All of the features are categorical. There are 150 categories in the value of 1st and 2nd features. There are 8 categories in the value of 3rd and 4th feature. I ...
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Classifier for DBSCAN [closed]

I have written a code that uses DBSCAN and tries to find the most appropriate eps for my dataset, trying to include most of the data inside a cluster. The problem is that DBSCAN is not a classifier ...
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1answer
302 views

Overfitting with text classification using Transformers

I am trying to make a binary text classification model by using the encoder part of the transformer and then using its output to feed into an LSTM network. However, I am not able to achieve good ...
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Ways to increase recall in SVM

I am training an SVM on UCI's Bank Marketing Data Set, the bank additional-full.csv. As the data is skewed I am also interested in recall. I am getting accuracy of about 87.95% but my recall is around ...
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Gender identification task on instance or user level?

I'm working on a task which is gender identification. Given a user account (e.g. Twitter account) with its documents (e.g. 100 tweets), the user should be classified as a male or a female. The ...
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Automated taxonomic identification of insects – where can I find some good enough software or code?

Short context: my colleagues study plant-pollinator networks. Insect (pollinator) identification is a task that requires a lot of effort. I would like to know if there are already trained or ...
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Training a classifier with text and numerical features - what is the state of the art?

I'm trying to build a binary classifier where the features are mostly numerical (about 20) and there are a couple of unstructured short text fields as well. What is currently considered the state of ...
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When optimizing the MSE, the correlation between prediction and target increases?

After optimizing the MSE (mean squared error) in a regression task, how is the change in Pearson correlation coeficient between target vector and the prediction? Is any behaviour possible? Or is sure ...
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classifying a part a robot makes from its power draw

I work for a company that has a robot that makes parts. I have a IOT device connected to the robot that measures and stores how much power the robot draws. The power data gives a clear pattern of when ...
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1answer
58 views

Binary Classification Comparing two time series of variable length

Is there a machine learning model (something like LSTM or 1D-CNN) that takes two time series of variable length as input and ...
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Platt Scaling vs Isotonic Regression for reliability curve

I am learning classifier probability calibrations and have calibrated an eleastic net model using both Platt scaling and isotonic regression. As you can see in the attached image Platt scaling (on the ...
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1answer
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LSTM Time-series classification - derived feature

I have a time-series dataset and I want to derive a new feature based on a date column which I believe might improve my predictive model. The feature is if it's weekday or weekend. I am not sure how ...
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What is the input dimension for this keras model?

I'm doing a tutorial, where I have to evaluate the sentiment of IMDB reviews, positive or negative. I first created an index for each word and then replaced every word in each review for each ...
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How to decide who to market? Clustering or Decision Tree?

I am working with a dataset that has enough observations and ~ 10 variables, half of the variables are numeric another half of the variables are categorical with 2-3 levels (demographics) one ID ...
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1answer
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Why is oversampling outperforming class weight?

I have a dataset that is highly imbalanced. One class has 412 (class 0) samples while the other has 67215 (class 1) samples. For its classification, I am using MLP. When I use class weight of 165 for ...
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SGDClassifier on Big Data

I am trying actually to train a SGDClassifier with over 4,000,000 samples of data without any positive results. X vector has 6 features and looks like : [ 2 , 4 , 56431555 , 1 , 0 , 33] Y vector has ...
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Measuring performance of classifiers with different/extra classes

Disclosure - This is also on cross-validated, but has no comments or answers. Then I found this forum and thought it may be best suited here. I'm not sure where to post this, or how best to explain, ...
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Preferred approaches for imbalanced data

I am building a binary classification model with imbalanced target variable (13% Class 1 vs 87% class 0). I am considering the following three options to handle the data imbalance Option1: Create a ...
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How could a neural network classifer for multilclass problem classify only in one class when a decision tree is more balanced and accurate?

I want to create a classifier for a data frame that has four classes. Each line can only have one class. I have two predictive models: a neural network and a tree classifier. But they put everyone in ...

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