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Correlation between predictions vs correlation between targets

In a multi-target model framework - where a separate model is estimated for each target - how can one take into account for correlations between targets during the training process ? For example say I ...
Kreol's user avatar
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Relationship among different classifiers of a model in multiclass problems

Suppose we are fitting a LogisticRegression model with scikit-learn, or the same model with pytorch. In multiclass problems, the strategy OneVsRest will fit a different classifier for each of the ...
CasellaJr's user avatar
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1 answer
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Whats the advantage of single target neural network over multi target neural network?

So right now I have made 2 neural networks to predict x and y coordinates separately. One for x, and one for y. I'm looking for a reason to backup my assignment. I have search for this and most of the ...
Secondary Juggernaut's user avatar
1 vote
0 answers
24 views

Validation Accuracy incorrect when multiple outputs are in the dense layer!

I have a set-up a parallel LSTM architecture with two LSTM layers and one Dense layer producing several outputs which are converted into a probability with a sigmoid function on the dense layer. For ...
Abdi's user avatar
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Is it possible to train an ML model that predicts two different types of variables (numeric, categorial)? Is learning a ranking function possible?

Problem setup: We are given a set of training instances and an algorithm C, composed of two randomized algorithms, A and B. Algorithm A has no hyperparameters. In contrast, B has only one ...
AlgoTriceratops's user avatar
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1 answer
52 views

using forecast values from a univariate model as Input to linear regression?

I have weekly time series data for the last 2 years with variables "week", "marketing_spend", "web_traffic", and "revenue" ...
sdave's user avatar
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234 views

How do I build a multi-input and multi-output neural network?

I want to build a neural network classifier which has as inputs array of dimension (24, 2, 20001) and as outputs array of dimension ...
HelpNeederStudent's user avatar
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1 answer
681 views

Grid-search for a multi-output regression task using Scikit-learn's API

I'm trying to make a model for a multi-output regression task where $y=(y_1, y_2,..., y_n)$ is a vector rather than a single scalar. I am using Scikit-learn's ...
Hassan Abedi's user avatar
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1 answer
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After validating a model, how to extract optimal inputs inputs based on a given output?

I Looked around and could not find a similar question (at least not with the keywords I used). After I trained and tested a classification model, I understand how I can supply it with a new input to ...
BarDro's user avatar
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0 votes
1 answer
89 views

Can ML or DL Predict output vector target?

I have output data as follows: Then I encode into : Then I convert into vector: The input of model is word embedding of sentence. My question is that: Can ML or DL return a vector output above? If ...
Sherry's user avatar
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1 answer
802 views

Why do we need "MultiOutputClassifier" if we can get same results without it?

I am learning about multi-label multi-classification examples It is when you have a case like this ...
asmgx's user avatar
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1 answer
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How to show combined overall accuracy for a multi-ouput model in Keras?

I have a model of the following structure. It has 6 outputs. Given an image, the model predicts classes of 6 different components from the image. The metrics I used are: As you can see it outputs an ...
Exo's user avatar
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1 answer
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Output representation for a neural network to learn grid-based game with multiple units

I have a round based game played on a grid map with multiple units that I would like to control in some fashion using neural network (NN). All of the units are moved at once. Each unit can move in any ...
Druudik's user avatar
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1 answer
2k views

How to calculate a single accuracy for a model with multiple outputs in Keras?

Consider the following, rather simple, model: ...
Mehran's user avatar
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1 answer
344 views

Early stopping based on average val_loss of last ten epoches and with some n partiences

I am training a DNN with CNN in Keras. Though, I can write an EarlyStopping criteria based on val_loss but due to minor oscillations in the val_loss, I want to monitor the average validation loss over ...
prashanta_himalay's user avatar
1 vote
0 answers
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Would it make sense to have an output layer connected to other output layers in a NN?

I'm working with data that has multiple variables which could be predicted, nonetheless I need to predict just one that is directly correlated to all of the others. Would it make sense to have a NN ...
gianluca samaritani's user avatar
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0 answers
47 views

Multi-Label Regression of Categorical Probability Distribution that adds up to one

What would an ideal Tensorflow/Keras architecture look like, if the target is a multi-regression with values that add up to one? Toy Example: Tv Channels You work for a big TV-Station and your boss ...
Marcus's user avatar
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5 votes
1 answer
676 views

Multi-target regression tree with additional constraint

I have a regression problem where I need to predict three dependent variables ($y$) based on a set of independent variables ($x$): $$ (y_1,y_2,y_3) = \beta_0 + \beta_1 x_1 + \beta_2 x_2 + \dots + \...
Peter's user avatar
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0 votes
1 answer
74 views

Multiple targets in a classification problem

I have a vector of length $n \gt 4$ which has exactly 4 targets, so for example [0, 0, 0, 1, 0, 1, 0, 1, 1]. I would like to know how I can modify the softmax ...
Dennis's user avatar
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0 votes
1 answer
33 views

What input for a combined model (3 nets)

I have this architecture, made of 3 NNs: In code: ...
CasellaJr's user avatar
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3 votes
1 answer
2k views

Multi-output, multi-timestep sequence prediction with Keras

I've been searching for about three hours and I can't find an answer to a very simple question. I have a time series prediction problem. I am trying to use a Keras LSTM model (with a Dense at the end) ...
MGreen's user avatar
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2 votes
1 answer
39 views

Given a regression based model with many feature variables; what tools would you utilize to figure out which feature variables add the most variance?

Given a hypothetical dataset {S} with 100 X feature variables and 10 predicted Y variables. X1 ... X100 Y1 .... Y10 1 .. 2 3 .. 4 4 .. 3 2 .. 1 Let's say I want to improve the accuracy of Y1. I am ...
Sad CRUD Developer's user avatar
2 votes
1 answer
174 views

What method/algorithm for constrained multi-target regression

I am working with three dimensional measurement data and want to model them using a multivariate linear regression. I have already implemented a simple gradient descent algorithm to solve the classic ...
schafran's user avatar
  • 131
0 votes
1 answer
60 views

How To Motivate A Neural Network

Suppose a training dataset contains the following inputs: company size number of employees turnover average salary country years of operation ...and outputs: ...
Alan's user avatar
  • 111
0 votes
1 answer
576 views

Neural Network for Multiple Dependent Outputs

I have a dataset with approx 6 input features and 5 output values to be predicted. I am trying to understand what kind of neural network would be most suitable to assign probability across multiple ...
Alan's user avatar
  • 111
1 vote
1 answer
437 views

Multiple output size in neural network

In the paper "A NOVEL FOCAL TVERSKY LOSS FUNCTION WITH IMPROVED ATTENTIONU-NETFOR LESION SEGMENTATION" the author use deep supervision by outputing multiple outputmask which have different ...
Chopin's user avatar
  • 352
7 votes
2 answers
4k views

Custom output names for keras model

I have a model like this with multiple outputs and i want to change it's output names ...
amin msh's user avatar
  • 171
1 vote
0 answers
42 views

Derivative of multi-output Gaussian Process

I am working on a project where I estimate transition and measurements models for a kalman filter using Gaussian Processes. In order to linearize the models I require the Jacobian of the estimated ...
Michael's user avatar
  • 21
1 vote
1 answer
352 views

Keras loaded model output is different from the training model output

When I train my model it has a two-dimension output - it is (none, 1) - corresponding to the time series I'm trying to predict. But whenever I load the saved model in order to make predictions, it has ...
Marlon Teixeira's user avatar
1 vote
0 answers
125 views

Validation Accuracy greater than train accuracy, validation loss lesser than training loss MTL

I am training a multi task model using VGG16. Datase: Dataset contain 11K images. There are two tasks: The dataset is imbalanced, 1) PFR classification: 10 classes 0 --- 5776 10-12 --- 1066 6-...
Obiii's user avatar
  • 131
2 votes
1 answer
57 views

Control which features are used for every task in multioutput classification?

I would like to perform a multiclass-multioutput classification task, on vectorized textual data. I started by using a random forest classifier in a multioutput startegy: ...
R Sorek's user avatar
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1 vote
0 answers
27 views

What toolbox to use to create multi-output random forest(reggression) with custom spltting function at each node?

I am trying to implement "Real Time Head Pose Estimation fromConsumer Depth Cameras" by Fanelli et al. I need to train a random forest(regression) with the following criterion The predicted output is ...
user27665's user avatar
  • 121
3 votes
1 answer
159 views

Physical modelling with neural networks - single output + stack ensemble vs multi-output

We are trying to replace an existing physical model (8 inputs/7 outputs) with artificial neural networks. The physics behind the existing model is mainly thermodynamics of humid air for air ...
fernikalo's user avatar
4 votes
2 answers
372 views

Are there any Python libraries for predicting the closest value to a correct label out of a variable-size list of possible label values?

Imagine I have the following dataset: ...
Guillermo Mosse's user avatar
2 votes
1 answer
131 views

unique predictions for "multi-label multi-output" classification task

Let’s assume that four participants (A, B, C and D) take on five sport-challenges (e.g. swimming, running, ...). Our goal is to predict the placement of each participant for each challenge. Moreover, ...
Blackout's user avatar
0 votes
0 answers
78 views

Combining several Multi-Output-Models into a single Multi-Output-Model

I'm trying to create a k-Nearest-Neighbor based model of 76-dimensional input data $I$ and 44-dimensional output data $O$. Through domain knowledge I know that only certain input dimensions are ...
twes3's user avatar
  • 1
1 vote
2 answers
5k views

How to feed data to multi-output Keras model from a single TFRecords file

I know how to feed data to a multi-output Keras model using numpy arrays for the training data. However, I have all my data in a single TFRecords file comprising several feature columns: an image, ...
magomar's user avatar
  • 131
0 votes
1 answer
682 views

Minimal example: Keras functional API & multi-input/multi-output regression

Problem: I have a regression problem, where I want to predict two or more numerical outcomes $y_i$ based on a number of numerical features $X_i$. The model would look like: $$y_{1,i}, y_{2,i} = \...
Peter's user avatar
  • 7,526
1 vote
2 answers
645 views

A neural network with more output neurons than labels

When we train a neural network model for a classification problem, we usually have a dense output layer of a size equal to the number of labels (classes) we have. If the layer size was greater, the ...
Abdulrahman Bres's user avatar