Questions tagged [multiclass-classification]

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Is it possible to have a default class in multi class classification?

In the general text classification problem, training a machine learning model to detect if a text belongs to one of N number of classes always yields a value in N. Even if the text that was passed to ...
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High accuracy in one v. all, lower accuracy in all vs. all

I am training a classifier (similar to logistic regression) on MNIST. I have 10 one -vs.-all classifiers for each number, each of which independently achieves >90% test set accuracy. However, when I ...
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True positives and true negatives, F1 score: multi class classification

I have 4 classes for an application of classification of animal kingdom: 1 --> invertibrates; 2 --> vertibrates; 3--> mammal; 4 ---> ambhibian. Given a mixture of images the objective is to identify ...
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Difference of sklearns accuracy_score() to the commonly accepted Accuracy metric

I am trying to evaluate the accuracy of a multiclass classification setting and I'm wondering why the sklearn implementation of the accuracy score deviates from the commenly agreed on accuracy score: $...
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Improving classifcation when some are less represented?

I have a multi-class classification problem. It performs quite well but on the least represented classes it doesn't. Indeed, here is the distribution : And here are the classification results (I took ...
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Data quality improvement as a part of preprocessing: Imputation

I have a python pandas dataframe representing a superset. The data contains a lot of nulls which I want to overwrite with real values. the superset has: both numerical and categorical data some ...
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How can I train a machine learning model with below characterstics? [closed]

Hi I have a classifier model to solve, which has close to 56k samples and 30 features which ...
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9 views

Shrink the training set during the learning process

Is there any way to change the size of the training set during the learning process? For example, let's say we have four classes (with their distribution): [A (90%), B (5%), C(2%), D(3%)]. Can we ...
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1answer
19 views

Combining 'class_weight' with SMOTE

This might sound a weird question, but I could not find enough details in sklearn documentation about 'class_weight'. Can we first oversample the dataset using SMOTE and then call the classifier with ...
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1answer
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Saving LSTM hidden states while training and predicting for multi-class time series classification

I am trying to use an LSTM for multi-class classification of time series data. The training set has dimensions (390, 179), i.e. 390 objects with 179 time steps each. There are 37 possible classes. ...
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1answer
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What is the purpose of 'oversampling' when the test set is still unbalanced?

I understand that both training and testing sets should have the same distribution and also understand that we should not touch the test set (in terms of oversampling). But we know that oversampling ...
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How to implement an LSTM RNN with multiple input features

EDIT: Now I didn't convert to list. I am training LSTM for multiple time-series in an array which has a structure: 450x801. There are 450 time series with each of 801 timesteps / time series. The ...
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Does object detection do a better job at image classification than image classification

I read in an article that object segmentation can do object detection better than object detection algorithms. I assume this is because there is more detailed information in the annotation images. I ...
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Feature Engineering in Multi-class Classification

I am working on a 3 class classification problem. I am curious on what is the best way to bin continuous variables for this problem. When I worked previously on 2 class problems, for examples sale ...
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Can I turn any binary classification algorithms into multiclass algorithms using softmax and cross-entropy loss?

Softmax + cross-entropy loss for multiclass classification is used in ML algorithms such as softmax regression and (last layer of) neural networks. I wonder if this method could turn any binary ...
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Are more classes more favorable than a single combined class?

Imagine the following scenario. Train a classifier that classifies an object into one of these n+m classes: ...
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How to identify multiple lines/clusters in a single dataset

I'm currently struggling to wrap my head around how multi-linear regression could be done to find separate sets of linear models in a single data set. I can perform regression on single data set for a ...
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1answer
63 views

Transform multi-label problem to multi-class problem

What are the downsides of modelling a multi-label problem as a multi-class problem with a single classifier? Let my clarify what I mean. There at least two ways that one multi-label problem can be ...
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Which feature to use in feature selection?

Objective: Multiclass classification with supervised learning, small dataset (25h) Context: My dataset is composed of mobile network data collected with a smartphone. The labels correspond to the ...
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better confussion matrix higher LogLoss ? Is that possible>

I have tried a 2 different versions of a gbm in a multinomial classification problem. The second model results in better confusion matrix but in worse Log Loss value (at the test sample). How is that ...
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RuntimeError: Assertion `cur_target >= 0 && cur_target < n_classes' failed

I am referring this previously asked question in stack-overflow which remains unsolved till now. I am facing same problem with pytorch when I am solving ...
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Can we have a sampled sigmoid instead of softmax?

Thh solution proposed here: is for softmax negative sampling. How do we do a sigmoid negative sampling? I couldnt find a corresponding 'tf.nn.sampled_sigmoid_loss' function.
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Loss function for multi-class classifiction where output variable is a level i.e the various classes are dependent on each other

Let's say we are classifying Images of cat , fish and human. Classifying a cat as human is as wrong as classifying it as fish, so here the normal loss functions/ metrics like Confusion matrix is fine. ...
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Classifying Short Texts with Spatial Features

I have a dataset of short texts (like tweets) in addition there's some geographical data attached to each tweet - coordinates, whether it was made on the road, street, outside or in the building, ...
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What DNN topology can I use to tackle a hierarchical multi-class classification problem?

Suppose that the sample set consists of labelled data, where each label corresponds to a class (say a sub-topic), and every class belongs to a group (a topic). The model should be able to predict the ...
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threshold for word/embeddings based on frequency in DNNLinearCombinedClassifier

I'm using Tensorflow's DNNLinearCombinedClassifier for multi-class classification. Irrespective of my vocabulary size I'm ...
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2answers
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Difference between LASSO penalty in neural network and just LASSO regression

I wonder whether those two have any significant differences. I think in neural network, the lasso penalty put on the loss function makes the model simpler and introduces more sparsity by ...
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Keras input for multivariate classification with LSTM using current features and previous timesteps features and y values

I am working on a multivariate binary classification problem. What I want to do is to predict a binary classification given the features at the current timestep and the data (features+real ...
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1answer
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Tuning a classifier for high precision, with no regard for recall

I understand this falls under the decision making aspect, rather than the probabilistic, but for the purposes of some work I am doing, I need the classifier to have very high precision, as I can't ...
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1answer
12 views

Is it normal for F1 scores to be lower on a binary classification task as compared to a 3-class classification task?

I am trying to understand if the F1 scores are higher for a binary classification problem than for a multiclass classification problem.
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1answer
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Are there any algorithms for a classification problem involving unlimited classes, and only a few instances per class

The Scenario: A group of people must summarize specific parts of speeches they hear. They hear a new speech every day, and it's possible that multiple members of group are listening to the same speech....
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Multiclass classification of textual data

I have a problem statement in which I have to classify the text data into various classes, but the training data is very less (250-300 data points for 4 classes). I am confused about what approach to ...
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How can I evaluate out-of-domain question in a domain-specific Q&A bot when I only have in-domain data?

I learned that some popular bots like RASA or LUIS will have "confidence scores" to evaluate the out-of-domain questions, but none of them provide documentation of how they calculate these scores. ...
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what is the difference between multilabel and multilabel-multiclass classification?

I am trying to classify news articles into their required category. However I am confused by the above(multilabel and multilabel-multiclass) terminologies. My dataset consists of 2 csv files. The ...
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How to tackle a multilabel classification problem

I am trying to build a LSTM model for a multiclass classification problem on textual data. Until now, I have only built a model when one input belongs to one of the categories. What do I do when one ...
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1answer
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What is the intuition behind using LSTM for classification tasks?

LSTM is good for sequence prediction, because it can remember the previous context. What is the rationale behind using it in classification tasks ? In particular, they have used it for the following ...
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487 views

Which comes first? Multiple Imputation, Splitting into train/test, or Standardization/Normalization

I am working on a multi-class classification problem, with ~65 features and ~150K instances. 30% of features are categorical and the rest are numerical (continuous). I understand that standardization ...
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1answer
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Training textblob with 16k rows of labeled data won't work (only few are working)

I've got labeled data in a csv which looks like: title,type Women Jacket A,Clothes Mens Running Shoes B,Shoes Children backpack,Bags and a script: ...
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Different result between Rapidminer and Python imblearn

I'm currently working on imbalanced classification problem. However i found different result between SMOTE in rapidminer and SMOTE in imblearn (python). rapidminer SMOTE give 15-20% improvement on ...
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For a multiclass classification problem, how do we find the cohen kappa score?

So I have a multiclass classification problem and I have found the Matthews Correlation Coefficient of that (https://scikit-learn.org/stable/modules/model_evaluation.html#matthews-correlation-...
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How to use contrast coding schemes in the case of multiclass target variable? How to encode categorical features if contrast coding fails?

How do you deal with a dataset which only has categorical variables, all of whom have high cardinality? What is the right way to encode high cardinality categorical variables if the target variable ...
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1answer
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How to handle overfitting in the following classification case

The confusion matrix is as below :- [[ 0 0 5 1 0 0] [ 0 0 19 14 0 0] [ 0 0 217 151 0 0] [ 0 0 84 282 0 0] [ 0 0 6 111 0 0] [ 0 0 0 10 0 0]] ...
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How to compute AUC in gridsearchCV for multiclass problem

I'm currently working on a multiclass imbalanced problem. I am using random forest as learner and using different methods of resampling. I would like to use ...
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41 views

A robust metric in the presence of class imbalance

When evaluating the performance of a multiclass classification problem, on a highly imbalanced dataset, what is the most robust metric for this purpose? I read a paper that states: "Average ...
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Using Lift charts with multinomial classification model

I’m trying to understand the use of Lift charts with multinomial classification model in the evaluation phase. I can see only one category can be selected to use in ‘x’ axis e.g drugA . What kind of ...
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273 views

Why class weight is outperforming oversampling?

I am applying both class_weight and oversampling (SMOTE) techniques on a multiclass classification problem and getting better results when using the class_weight technique. Could someone please ...
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best approach for CNN training with multiple subcategories and one category

I need to classify pictures into 2 categories: approved and rejected. Rejected category has different type of images which are not allowed (subcategories), for example nude or gore or anime etc. What ...
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1answer
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Comparing multi-class results with binary classification results

We used machine learning to discriminate the following five disease classes: Normal (N) Myocardial Infarction (MI) Coronary Artery Disease (CAD) Congestive Heart Failure (CHF). In the past, these ...
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Improve accuracy of Keras multiclass image classification with pretrained VGG16 conv_base

In the moment, I'm training my first "larger" image classification model with Keras (22 classes, 2000 train samples, 500 val samples each class). I use a pretrained model (VGG16). My current model is ...
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Any non Deep Learning python packages for sequence classification.?

Stats model or any other machine learning python packages for doing sequence classification(that can be multi class) and sequence prediction (Both next step and regression). PS : Input data will be ...