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Reason for softmax approximation in Ian Goodfellow's deep learning book

In section 6.2.2.2 (equation 6.31) they state: Overall, unregularized maximum likelihood will drive the model to learn parameters that drive the softmax to predict the fraction of counts of each ...
Philipp's user avatar
  • 11
1 vote
0 answers
18 views

How to train a neural network to identify multiple features in one image, where the order of predicted features doesn't matter

I recently created a toy dataset for myself, which I call "multi-color MNIST", where multiple digits with different colors appear on a single RGB image. See the image I attached. I am using ...
Innocuous Rift's user avatar
0 votes
3 answers
139 views

False Negative vs False Positive for Multiclass classification

Suppose I have three classes 1,2,3. And there's evaluation like below, where second element is false prediction where model predict class 3 while ground truth is 2. ...
Muhammad Ikhwan Perwira's user avatar
0 votes
0 answers
47 views

Applying PCA Before Training Multiple SVM Binary Classifiers To Reduce Data

I am working on a project which has a goal to determine if a new sample is part of Class A or Class A'. I need multiple of those classifiers. I will have an SVM to classify between: ClassA - ClassA' ...
guitardenver's user avatar
0 votes
0 answers
27 views

Theoretical support for sample size exponential increase with dimensionality in nearest neighbour multi-class classification

In multi-class classification using nearest neighbour, I believe that as the dimension of the space increases, we need exponentially more samples to keep the classification error under a certain ...
orematasaburo's user avatar
3 votes
3 answers
359 views

One class never gets predicted, regardless of the model

I'm working on a classification problem from a dataset containing three classes, with proportions {"0":0.43, "1":0.25, "2":0.30}. ...
Mordechai's user avatar
0 votes
0 answers
22 views

How to compare if multi-class outputs are different?

I am testing different multi-class (16 classes) classification models (ANN, DNN, DL, and so on) and the overall accuracy varies from 0.75 to 0.92. Despite that 0.92 is greater than 0.75, is there a ...
aldo_tapia's user avatar
0 votes
1 answer
248 views

Making a multiclass classification problem binary during data preprocessing vs using a multiclass classifier

Sometimes you have a problem that presents as a multiclass problem in your data, but you only care about the result of a binary classification. E.g. you have a manufacturing process which can result ...
Grumpy's user avatar
  • 3
1 vote
1 answer
80 views

Clustered data and multiclass classification with GPBoost

Is it possible to do multiclass classification using GPBoost? For example when we have 3 or more classes (e.g. specie A/ specie B/ specie C) from a clustered data set (e.g. several measurements over ...
Oscar's user avatar
  • 11
2 votes
1 answer
180 views

XGBoost Calibration for weighted loss function

I am currently using XGBoost (in R) to perform multiclass classification. I am using merror=eval_metric and my objective is <...
HeyCool08's user avatar
4 votes
4 answers
749 views

Using regression where the ultimate goal is classification

Some background information about what I am trying to model before asking my question: Long periods of hot temperatures and dry conditions (heatwaves) puts pressure on the components in the power ...
Sam Malek's user avatar
2 votes
2 answers
105 views

Ideal scoring rules for multitask classification?

I am seeking advice for the best way to score a multi-output/multitask classification model's output. Problem setup A simplified version of the model is as follows: Training data have F features, say ...
FidusAchates's user avatar
0 votes
0 answers
91 views

Metrics for multiclass classification

Background Consider a clustering problem where a dataset of measurements $Z\triangleq\{z_i\}_{i=1}^m$ must be partioned in $n$ clusters, where $n$ is unknown and must be estimated. Here the term "...
matteogost's user avatar
0 votes
1 answer
13 views

Hierarchical labelling for independent variable in training data

I have data where the objective is to use supervised learning to predict 4 different outcomes. Say the classes are 1, 2, 3, 4. Though discrete, they are also hierarchical, where class 2 is of an ...
Mikee's user avatar
  • 101
2 votes
0 answers
59 views

Is there a multi-class classification model which can incorporate partly known conditional probabilities of the targets?

I am faced with a classification problem where I wish to predict 2 binary variables, say x and y. x and y are dependent in the sense that one conditional probability vanishes: P(y=1 | x=0) = 0. I am ...
Sebastian Chejniak's user avatar
1 vote
1 answer
1k views

Multi-Label Classification where each label is a Multi-class problem

Problem: Currently, I have 15 classification models(multi-class + binary). Training and Maintaining 15 models take a huge time and cost. Also, I need to inference 15 models for every input. So I ...
Naren Babu R's user avatar
1 vote
1 answer
106 views

R SVM classification with kernlab and user-defined kernel

I am trying to perform multi-class classification using SVMs (C-SVC). I am using the ksvm function from the kernlab package in R....
HeyCool08's user avatar
2 votes
0 answers
430 views

Does scikit-learn support plotting calibration curve for multiclass classifier?

I have trained a multi class classifier and calibrated the classified using scikit-learn library's CalibratedClassifierCV class. To check how well the probabilities are calibrated, I tried plotting ...
yodasoda18's user avatar
1 vote
1 answer
48 views

Incorporate known class likelihood (proportions, ratios, etc..) in the classification output

I'm working on the multi-class prediction problem, with 6 output classes. These represent different types of land cover. The classification model is pixel-based and I have extracted different ...
kap.provalija's user avatar
2 votes
1 answer
431 views

Can XGBoost do classification based on linear combinations?

Suppose we have a data set $\mathcal{D}$ consisting of $n_C$ continuous features $\boldsymbol{X}_1, \boldsymbol{X}_2, \dots, \boldsymbol{X}_{n_C}$ and we wish to target a discrete variable $\...
HeyCool08's user avatar
4 votes
1 answer
3k views

LightGBM interpretation of monotonic constraints in multiclass classification

When using LightGBM in classification problems it is possible to use monotonic constraints. In binary classification problems the interpretation is straightforward: "The probability of class (say)...
BLaursen's user avatar
  • 303
1 vote
1 answer
253 views

How to handle with long-tail classification

I have a long tailed distribution with many classes, and the num of samples per class is ...
Cranjis's user avatar
  • 57
1 vote
0 answers
45 views

3 classes imbalanced and hierarchical classification

I need to classift a dataset with 3 classes: A - 85% of the data B - 10% of the data C - 5% of the data Where C is a subset of B. What should be the best way to approach it?
Cranjis's user avatar
  • 57
1 vote
1 answer
101 views

Linear Classifiers -- Single Linear Layer with k Neurons vs 'one vs rest' (k Linear Layers with 1 Neuron)

When using a linear classifier for a k-class classification problem, is there actually any difference of using a single linear layer with ...
keezar's user avatar
  • 35
1 vote
0 answers
150 views

Multiclass ROC curve about one vs one

I am learning ROC about multiclass problem. I read this article https://towardsdatascience.com/multiclass-classification-evaluation-with-roc-curves-and-roc-auc-294fd4617e3a . I am confused about ovo ...
李宜倫's user avatar
4 votes
2 answers
286 views

Prediction of 'other' class

I'm training a MLP classifier with a softmax output that outputs 4 classes. For my particular application I'd like the classifier to output a fifth 'other' class when the input don't belong to any of ...
nico's user avatar
  • 4,591
1 vote
1 answer
34 views

What is the benefit from going from unordered to oredered data?

Let us assume that we have naturally ordered data that we want to classify. Then we can use ordinal regression/classification methods. Yet we can treat those as unordered and use multiclass ...
Hubert Drążkowski's user avatar
1 vote
0 answers
37 views

Classifying dataset with different number of features

I have a dataset like below: samplename position reference alternative S1 201 C T S1 3567 A G S1 760 T C S2 356 C T S2 6787 T C These data belongs to patients and ...
Nmgh's user avatar
  • 31
1 vote
1 answer
92 views

How to calculate the total number of inputs in CNN?

I search this kind of question for a while and I find many discussions involve on counting the number of parameters of a Convolutional Neural Network, but not on the inputs. Using the Fashion MNIST ...
rodericktung's user avatar
1 vote
1 answer
37 views

Should I train my classifier with examples that are outside my classes of interest? And should I create an "others" class to handle them?

This is a 2 part question regarding a multi-class classifier based on a neural network that is expected to predict whether the input image has a cat or a dog. If shown something different (like a man),...
Augustine Charly's user avatar
1 vote
1 answer
156 views

CNN for multi-class classification with occasional multi-labels

I have about 10 classes, on which I train a CNN with a softmax output layer using one-hot encoding and categorical cross-entropy loss. The problem is that two pairs of these of these classes (let's ...
Paul92's user avatar
  • 141
2 votes
1 answer
690 views

Optimize classification rule in multinomial logistic regression

We know that in the case of logistic regression, a classification threshold p=0.5 is generally not an optimal choice when seeking to optimise sensitivity and specificity. This is generally due to the ...
Lelouch's user avatar
  • 50
0 votes
1 answer
456 views

Low classification accuracy

I want to do a multi class classification with 6 classes. Whole dataset has 12750 and 56 features samples, so every class has 2125 samples. Before prediction I reduces amount of outliers by ...
jared's user avatar
  • 31
2 votes
1 answer
2k views

How to use time-series observations on multi-class classification problem?

I have a multi-class classification problem with time-series features. You can find an example series below. It shows the same series over time for different classes (actually, each line represents ...
Enes's user avatar
  • 63
3 votes
2 answers
2k views

Difference between ROC-AUC and Multiclass AUC (MAUC)

I am trying to understand the interpretation of these metrics in a multiclass scenario: ROC-AUC and MAUC. Scikit-learn provides ...
arilwan's user avatar
  • 283
3 votes
1 answer
918 views

How to predict both category and sub category in machine learning classification?

There are 4 actions available. Each action has its own varying number of categories. The target is to predict an action along with the category of action, given input data. Assume actions are a,b,c,d ...
MrSpectre's user avatar
4 votes
2 answers
875 views

Machine learning for causal inference

I have a multiclass classification problem where the target variable is actually different categories of causes, and the dataset is observational. I know of causal inference, and I would like to learn ...
herman's user avatar
  • 151
4 votes
1 answer
167 views

How the SVM algorithm works with one label in range?

I have a dataset where the features are configurations (numeric values) that describe the situation and the label (only one) is the ranking of the situation (natural value between $[1,5]$). If label ...
vesii's user avatar
  • 221
0 votes
0 answers
322 views

Can I use Pearson's correlation coefficient or Spearman's rank correlation coefficient as metrics for multi-class time series classification?

I am doing a 3-class time series classification problem, where classes are 0,1 and 2. For example, the test set is ...
user900476's user avatar
4 votes
3 answers
1k views

Effects of class imbalance on neural network weights

My question is about unbalanced classes problem in case of a classifier neural network for natural language processing (in particular, a neural network with LSTM). I want to train a neural network to ...
HelpNeederStudent's user avatar
2 votes
1 answer
620 views

How to fully evaluate a multiclass classification problem?

When you have a multiclass classification problem, what is the right way to evaluate it's performance? What I usually do is to display the confusion matrix and the ...
Federico Gentile's user avatar
0 votes
1 answer
474 views

How to practically calculate the accuracy of each class in muliclass classification problem?

I have the following confusion matrix: ...
Federico Gentile's user avatar
0 votes
0 answers
105 views

Should I scale my data before a Cartesian-to-spherical conversion?

If I have three features, should I scale them before converting them to a spherical coordinate system? I have been working on a ternary classification problem. My data is high-dimensional, so I've ...
michen00's user avatar
  • 111
0 votes
0 answers
1k views

under sampling a multi-label dataset

I have a multi-label dataset, whose label distribution looks something like this, with label on x-axis and number of rows it occurs in the dataset in y-axis. ...
Naveen Reddy Marthala's user avatar
2 votes
1 answer
163 views

Looking for multilclass classifier that can handle repeated measures

I am looking for a multiclass classifier that can handle repeated measures. Specifically, each of my subjects appears multiple times with the same number of n classes. Now I would like to fit a ...
Johannes Wiesner's user avatar
5 votes
1 answer
1k views

How to draw ROC curve for a multi-class dataset?

I have a multi-class confusion matrix as below and would like to draw its associated ROC curve for one of its classes (e.g. class 1). I know the "one-VS-all others" theory should be used in ...
Hamid Rajabi's user avatar
1 vote
1 answer
402 views

Can in theory a multiclass neural network classifier be seen as multiple binary neural network classifiers? [closed]

I would like to know more about the theoretical implications of such a statement.
Complicated's user avatar
0 votes
0 answers
339 views

Given a multiclass classifier, calculate one threshold per class to maximize recall under precision constraint

Given a classifier $f$, $N$ possible classes, and an input $x$, $f$ produces a class from $[1,..., N]$ and its matching confidence $[0,...,100]$. Then I run $f$ on a large set of examples $X$, and I ...
Danny's user avatar
  • 53
0 votes
0 answers
17 views

How to get multiple outputs using classification techniques?

I want to predict roles based on technical skills column.I have column technical skills for ...
10sha25's user avatar
  • 63
0 votes
0 answers
46 views

Which model to use for multiple outputs in classification problem

I want to predict roles based on name, experience, soft skills, technical skills . Based on all these variables I want to ...
10sha25's user avatar
  • 63

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