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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Does vectorized implementation of logistic regression give better results than RandomForest or DecisionTrees?

I just learned the on vectorized implementation of Logistic regression along with linear regression. Do these techniques provide a better result or are they in anyway better than prewritten ...
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How to change Linear model in SGDClassifier scikit learn?

The SGDClassifier of scikit learn defines it as "Linear classifiers (SVM, logistic regression, etc.) with SGD training.". I understand from this that any Linear classifier can be used here. ...
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Can I do bagging method as improvement technique to decision tree in research?

Bagging use decision tree as base classifier. I want to use bagging with decision tree(c4.5) as base as the method that improve decision tree(c4.5) in my research that solve problem overfitting. Is ...
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Visualization of transformed features in BERT

So I'm trying the Intent Recognition with BERT using Keras and TensorFlow 2 available at kdnuggets.com and this is the code for the results evaluation. ...
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Are these precision recall curves possible where single curve intersects each other? [closed]

Someone just showed me this precision-recall curve for decision tree classifiers after feeding scores and targets to scikit learns precision-recall curve module. Is this possible that the single curve ...
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predict_proba() on a continuos target made binary

I am building a Newtons gravitation equation model. It receives two mass and a radius and outputs what it generates from the formula. $F = G \frac{m1 · m2}{r^2}$ I want to transform it now to a binary ...
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How to use 5 by 5 or 7 by 7 kernel size for a deep learning network with 3 by 3 kernels?

I am using a U-Net architecture. The visual area of the segmentation mask is very small and after learning it is giving a lot of false positives. I am thinking of ...
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Support Vector Machines

I have ASCII data which converted from raster data in ArcGIS. I would like to use Support Vector Machine (SVM) algorithm to create a flood prediction map. My question is that can I use ASCII file ...
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Using constants in input of a ML Model

I'm currently building a binary classifier. My input is a sequence of 32 time-steps. Certain time-steps of the input will be constant (ex: t-0 will always be 0, t-5 will always be 9, etc) Does it make ...
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Why does the overfitting decreases if we choose K to be large in K-nearest neighbors?

I am studying machine learning and I am focusing on K-nearest neighbors . I have understood the algorithm, but I have still a doubt, which is on how to choose the K for the number of neighbors. I ...
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Both my train and val accuracy are fluctuating [closed]

I am training a time series classifier. But my train and validation set both are fluctating, although it's giving accuracy of 91% and auc of 0.97 I don't know what to conclude out of this. Notebook ...
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How to structure the output layer of an MLP that finds the quadrant of an arbitrary point in a rectangle?

I'm trying to write a neural network that outputs the quadrant of a rectangle that an arbitrary point lies in. This rectangle has its upper left at {0, 0} and its lower right at {1, 1} (e.g. point {0....
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Distinguish randomly generated texts from reasonable for human texts [closed]

I have strings short texts of 2 types: '23jd2032n0d2mn', 'fn830n30rn83', 'fhui29n4ok', 'qn4foml', ... and ...
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Does adding a class to a model increase its performance?

I am experimenting with object detection in images via CNN, to be more specific with yolov5 and faster RCNN. The models I use were pre-trained on the coco dataset before I finetune them. My dataset is ...
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feature importance after classification

I have time series data and more or less 200 features for each sample, I used a recurrent neural network for the binary classification task. After the classification I would like to know which ...
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What's the difference between setting negative label as 0 or -1 in binary classification?

I would like know what's the difference between setting the negative labels as 0 or -1 in the binary classification. I guess there is no important difference, just some notation changes in the model. ...
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Can i expect good results having low correlation attributes?

This was a question i saw in an interview for a data scientist position: "Here is the following correlation heatmap that i got from my attributes. Regarding the correlation of each feature with ...
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ValueError: output_type='binary', but y.shape = (30, 3)

I have a custom estimator that i implemented myself and i am not able to use cross_val_score(), which i believe it has something to do with my ...
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Drastic increase in accuracy while using pickle file with sklearn

I trained a xgboost classifier and it gave an accuracy of 49.99 % and i saved that model into a pickle file. When i ran the same data with pickle file (.pkl) it's giving an accuracy of 88.99 percent. ...
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what other metrics can i use to estimate quality of the model predicting income range - interval estimation task?

I trained a model that predicts customer's income given the features: age, declared income number of oustanding instalment, overdue total amount active credit limit, total credit limit total amount ...
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Time series binary classification probability smoothing

Problem Suppose we have trained binary classifier and want to predict value of [x1, ..., x5] with associated timestamps ...
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What are the major differences between rule based classifiers and association rule mining? [closed]

I was looking through some exam questions on Data Mining and came across this question in of the question papers.
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Algorithm for Binary classification

I have a data set with huge number of features ( Approximately 3000) and a binary target variable . The reason I have too many features is because of one hot encoding many categorical variables in ...
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SVM is taking too long for hyperparameter tuning

I am running SVM,Logistic Rregression and Random Forest on the credit card dataset. My training dataset has the shape (454491, 30). I performed 5-fold cross validation(which took more than an hour) ...
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Reinforcement Learning for classification of a CSV file

I would like to try a Reinforcement Learning approach for a multi-label or binary classification of a CSV file. I know that Supervised Learning is probably easier and I have already tried a couple of ...
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Loss function for age classification

I am building a CNN model for age classification. Assuming age of a person is between 1-100, my last Linear Layer contains 100 output neuron. Now i want to find an appropriate loss function for this ...
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Can I treat concept drift detection as a binary clarification problem?

Assume I have the ground truth labels for both non-drited and drifted samples, can I treat concept drift detection as a binary classication problem (one class non-drifted one class drifted)? If not ...
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Use predicted data to improve Multinomial Naive Bayes model for text classification [closed]

For a small project, I am making use of Naïve Bayes Multinomial Model to do some text classification. It has shown some very promising results, especially since I don't have a lot of Training data. ...
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How to predict multiple images from folder in python

Here is the code for the Prediction of multiple images from the folder. But getting the same label(class) for all the images.I'm not able to find out why every image shows the same label. ...
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Support Vectors of SVM

I have read somewhere that the value of slack variables of support vectors is not 0. Does that mean the points lying in the wrong region e.g a positive point lying in the negative region will also be ...
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1answer
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Distribution of training dataset in binary classification problem

Is it important to have the same amount of positives and negatives in our training dataset for a binary classification problem? Or for example, it doesn't matter if we have 70% positives and 30% ...
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Area under the ROC curve approximation

I wanted to compute the Area under the ROC curve for a logistic regression model in the context of binary classification. For that I computed, for a list of thresholds, say 0.1 0.2 ... 0.9, the TPR ...
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Plotting Learning Curve

I was drawing learning curve for a classification model. But I am not being able to plot train set, test set and validation set in the same graph. Can anybody help me to find this out? Here is my code ...
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Association rules for classification

I'm working on a classification project. I have many rows, containing many binary attributes, some often appearing together, exactly like what we can encounter in the Market Basket problem (in which ...
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How to incorporate features available only at training time into my model

I'm building classifier with a binary label. In my particular dateset the training data has many more features than the test set, in fact most of them are not available in the test set (beyond my ...
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Trying to use CBOW for tweet classification

I'm trying to use the Continuous Bag Of Words method for word embedding on a corpus of 7503 tweets. In particular, I'm trying to use CBOW on this Kaggle competition, which involves classifying tweets ...
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Multiclass Classification and log_loss

I hope I can make this clear with few lines of code/explanation. I've a 16K list of texts, labelled over 30 different classes that were ran through different classifiers; my Prediction and the Ground ...
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Proximity multi-dimentional arrays. Which algorithms are commonly used?

I'm new to data science so still learning so much. I've been searching for proximity algorithms but I'm not sure which are suited for multi-dimensional arrays. Any guidance would be greatly ...
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IIoT Sound Classification with Little Data

[Problem Statement] I have been working on a sound a classification problem with less 200 sound files (wav format) with the following imbalanced class distribution: ...
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Adaboost with other classifier fitting

There is the opportunity to fit decision trees with other decision trees. For example: ...
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1answer
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Is there any reason not to use classification instead of regression when looking for ranges?

In the case I want to predict only ranges from a continuous value, is there any reason to use regression instead of classification ? Could it depend on the type of model I am using (neural network, ...
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Apply two different Sklearn classifiers to two different subsets of the same data

I have a dataset that I need to run through a classification Pipeline. The dataset has 2 types of rows: described: description column POPULATED non-described: <...
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what exactly is the hessian matrix in xgboost?

i would like to understand a bit more the mathematics behind xgboost. I understand that the hessian is the second partial derivative of the loss function, i originally thought this was with respect to ...
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Test Binary Classifier on a Test-set that includes only one class

I'm working on a disease binary classification problem. 0 = healthy , 1 = not healthy The disease is a movement disorder that appears on the patient while moving a specific movement. I applied leave-...
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Clusterize Spectrum

I have pandas table which contains data about different observations, each one was measured in different wavlength. These observsations are different than each other in the treatment they have gotten. ...
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How can I label (predict) an unseen set of data based on an existing model?

I'm working on a learning multi-label classification project, for which I've taken 16K lines of text and kind of manually classified them achieving around 94% of accuracy/recall (out of three models). ...
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[under/over]-sampling teaches model the wrong distribution?

TLDR: Will under/oversampling during the training phase teach the model the wrong distribution and adversely affect accuracy? Let us assume you want to train a classifier to differentiate between ...
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Clustering time series data using dynamic time warping

I would like to cluster/group the curves in the attached picture with Python. The data is already normalized and my approach would be to use dtw (dynamic time warping) to calculate the distance and ...
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Train a model using multiple data sources

I have to train a classification model to predict if a customer will buy a product or not. I have multiple (eg. 3 or 4) data sources. The variable distributions among the different data sources is ...

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