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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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Maximizing Spearman coefficient correlation under ML algorithm

I have anonymus date (with unknown variable names) X (the amount of data is very large). The task is to predict the class, which is only 5. The main problem is that Spirman’s correlation coefficient ...
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How Does Weighted KNN Work?

I am reading notes on using weights for KNN and I came across an example that I don't really understand. Suppose we have K = 7 and we obtain the following: Decision set = {A, A, A, A, B, B, B} If ...
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Best way to narrow down a list and rank based on attributes?

I have a mortgage/credit data set that contains a list of customers (600k rows) and has a 100 columns inclusive of the customer's general info (address, city, zipcode, etc), income, fico scores, ...
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Not sure if over-fitting

I trained the data this way : There are four classes , the data distributed evenly (same amount of labels). Used min_max_scaler Used train_test_split(X,y,test_size=0.3,random_state=42,stratify=y) ...
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Analyzing results of fragile automated test suite

I'm not a data scientist. Please be gentle. I have a suite of about 1,000 automated "system" / "integration" tests that are run on each new version of software to check for regressions. The tests are ...
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Classify members of a dataset, given a known group of members

Problem I have a large CSV file with lots of lines. It has 4 columns and each column contains a label and three integers. I know the first 10,000 lines belong to the same group. Now I need to ...
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segmentation of brain tumor in MRI images

I have a dataset of brain tumours images. and I have to build a model to classify the malignancy grade of these tumours. The size of the tumours varies from small to large. The ROI are already ...
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Doubt to use accuracy or macro f1 measure in an unbalanced classification task

I have a multi-class classification task where the organizers said that the final results will be using the Accuracy measure. The provided data is unbalanced, and I don't have an idea about the test ...
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Understanding model performance (AUC and F1)

[ EDIT: I originally asked this at https://stats.stackexchange.com/q/379586/116913, but have not gotten any replies, hence posting here ] I built a multiclass (and very imbalanced classes) classifier....
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Can I classify a healthy population into clusters found in an injured population?

Just a bit of context: We are applying Machine Learning algorithms in the field of Human Biomechanics. In a previous project, my colleagues identified three different subgroups in an injured ...
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1answer
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human level performance on ImageNet, top-1 or top-5?

Anyone have pointers to where the human level performance on ImageNet comes from? I found a reference to 5.1% accuracy (top-1? or top-5?) from here.
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Supervised learning for timeseries classification problem

I'm trying to use a supervised classification algorithm on a timeseries problem and my model is performing to well i think. It's a time to failure problem. I have 1000 sensors and have to predict if ...
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Understanding the Gini/AUC metric as out-of-development performance metric

Assume we develop a model for a binary classification task that reaches a certain Gini/AUROC estimate on the validation ( or training ) sample, among others. This is an overall good metric, often used ...
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37 views

Multiple classification algorithms are predicting always exactly with the same scores. Is that normal? If not, what should I suspect?

I have been working on a multilabel classification problem. I am using Python machine learning libraries to implement the classification algorithms. For the cross-validation, I am using repeated K-...
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which algorithm to use for classification

I am a student and for this question is for my project. I have a dataset with 45000 rows, 16 variables + class variable. The classification is a binary classification with Yes and No. the problem is ...
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is max_depth except terminal node in decision tree?

I made the dicision tree with parameter max_depth = 5, max_leaf_node=15 but i get the model with 7 layer, which include root node, terminal node. I think I will get 6 layer because root_node + ...
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Designing a loss function for coordinated multi-object classification

I would like to train a neural network to identify two MNIST digits in an image. The one run of the network outputs two vectors, each with one-hot-encoded identity of one of the two digits. Obviously ...
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3answers
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In a binary classification, should the test dataset be balanced?

I have a dataset with 4519 samples labeled as "1", and 18921 samples labeled as "0" in a binary classification exercise. I am well aware that during the training phase of a classification algorithm (...
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Modeling data for Machine Learning

I am a beginner in machine learning and i struggle with understanding of how can i structure my data or get useful data to solve my problem or if it's even possible or needed with machine learning. ...
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Minimum numbers of support vectors

I'm trying to understand the concept of SVM. Consider linearly separable data $\{(x_i , y_i )\}_{i=1}^n , x_i \in \mathbb R^d , y_i \in \{−1, 1\}. \text{Let}\ \ \{x | w^T x + b = 0\}$ be the margin-...
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data pre-processing before image classification

I'm working on a machine learning project, Images classification (shape: 100 x 100)-> (vector of 10000), I did some pre-processing before applying decision trees algorithm , I got an accuracy of 55 % ...
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2answers
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Why Decision trees performs better than logistic regression [closed]

I'm working on a machine learning project, a classification of (100 x 100) Images (every pixel contains 0 or 255), my training set contains 10000 examples (which I split into 2 parts 80% training/20% ...
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1answer
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What brings the performance difference in Deep Learning with different data augmentation strategies?

I am studying the performance of deep learning models toward abnormality detection in chest X-rays. Due to sparsity of data, I augment the data using different augmentation strategies including: ...
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Train OneVsRest svms separately

I need to perform classfication of hundreds of classes. New classes arrive regularly. I also have some large training set (thousands of samples). ...
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Do I need to correct predict_proba by training fraction?

Many algorithms provide a predict_proba function indicating probability of a case to belong to that class (e.g. https://scikit-learn.org/stable/modules/generated/...
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Training multi-label classifier with unbalanced samples in Keras

I'm trying to train a keras model that takes in samples, let's say $x_i$ for sample $i$, and predicts multiple independent labels, $\hat{y}_{ij}$, such that $\hat{y}_{ij} = 1$ if the model predicts ...
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1answer
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why do we have to calculate the entropy of parent node in Information Gain?

Why do we need the entropy of parent node in the Information Gain. Information Gain = entropy(parent) - w * entropy(children) We can compare the entropy of the children without the need for the ...
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How CNN contributes differently in sentiment classification task than RNN?

I would like to know how fundamentally CNN is different from RNN (Many to one) for sentiment classification task. More specifically, what CNN models can learn from data that the RNN can't learn or ...
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Generating labeled dataset for training a neural network

I am currently working on a project in which i'm supposed to classify whether an image contains a translucent watermark or not. This is hard to do with standard object classification or template ...
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13 views

How to group text categorical data basis on clustering?

So if I have text dataset where I have more than 50 categories. Sample: ...
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1answer
21 views

Using classification of previous sample in neural networks

I am trying to classify the state of a machine using different features coming from a set of sensors. I am treating the problem like a time series, so I windowed the stream of the sensors each X ...
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Naive Bayes + unbalanced dataset + Tfidf

I am trying to make a clasificator of Amazon review of books using the Naive Bayes. My goal is to predict whether the review is positive, neutral or negative . I consider review as positive if it has ...
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2answers
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Autonomous evalution

Do you think it is possible to learn the app, how to autonomous evaluate good or bad parking of the bikes? The thing is you need to take a picture with your phone and app need to decide according to ...
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1answer
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Difference between sklearn’s “log_loss” and “LogisticRegression”?

I am a newbie currently learning data science from scratch and I have a rather stupid question to ask. I’m currently learning about binary classification, and I understand that the logistic function ...
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1answer
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Classification model challenge

I want to implement ML to monitor log file, classify them as normal and abnormal. The model must learn via this process of classification and after time be able to classify the log files itself. This ...
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1answer
42 views

Binary classification model with time series as variables

This is probably a simple question. Assume I'm interested in modelling a binary variable, with various covariates, including ones that are time series observations. In the usual modelling approach, ...
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1answer
25 views

Does this type of classification exist?

Im fairly new to data science and trying to see if a type of classification exists for my needs. I understand that a classification into 2 categories will look something like this: You have 2 ...
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support vector machine under noisy labels

I am relatively new to machine learning and had just read the SVM chapter in the introduction to statistical learning book. I am interested in applying SVM to my data. In theory, my data is perfectly ...
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1answer
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How to include class features to linear SVM

I am planning to do a simple classification with a linear SVM. One feature I have is another classification of some sort done previously. Can I just use this class feature as a 1-hot encoded array? So,...
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1answer
21 views

Adding recommendations to the output of a classification model

I have built a binary classification model using: logit decision trees random forest bagging classifier gradientboost xgboost adaboost I have evaluated the above models and chose xgboost based on ...
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High loss value with good classification result

I have a dataset which contains news articles (the articles are long, where for each record I have about 1500 words). During the training of my lstm network, I noticed when I get the best macro F1 ...
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1answer
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Grouping domain specific words/phrases with same meaning

I am looking at NLP methods to group together words/phrases which could have the same meaning. For example, in the sentence 'the table is broken' broken could be replaced by the following words/...
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Unary Classification Spark MLlib

Checking to see if anyone has insight or implementations within a Spark environment for unary classification. For instance, ML implementations such as scikit-learn have one-class SVM (https://scikit-...
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1answer
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Classification vs Regression Algorithms - Should exists algorithms only for Classification and/or Regression

Dummy question: There exists algorithms that should only be used for Classification or Regression problems? For example, should Random Forest should only be apply on Classification problems and ...
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How to evaluate accuracy for a multiclass classification algorithm which creates its own classes?

I have a classification algorithm, which (very) regularly sees data which is not part of any previously seen classes and must create a new class, which future data could be classified into. How should ...
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1answer
25 views

How can I detect anomalies/outliers in my online streaming data on a real-time basis?

Say, I've a huge set of data(infinite in size) consisting of alternating sine wave and step pulses one after the other. What I want from my model is to parse the data sequence wise or point wise and ...
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2answers
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Rule of thumb for good number of features when dealing with grouped data

I have a classification problem on clinical data where I have multiple samples for each patient. So the samples related to the same patient are somehow dependent from each other. I know that is not ...
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1answer
29 views

Is there any way how to make samples balanced?

I have a dataset which consists of attributes on breakdown of machines.The target variable is machine status which are populated with ones and zeros. The distribution of ones and zeros are given below ...
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1answer
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What's the difference between Sklearn F1 score 'micro' and 'weighted' for a multi class classification problem?

I have a multi-class classification problem with class imbalance. I search the best metric to evaluate my model. Sklearn has multiple way of calculating F1 score. I would like to understand the ...