Questions tagged [multi-output]
The multi-output tag has no usage guidance.
33
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Comparison of performance of regression models for multi-regression tasks
I have a sample time-series dataset (23, 14291) a pivot table count for 24hrs for some users. After pre-processing, I have a dataset with (23, 200) shape. I filtered some of the columns/features which ...
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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
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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 ...
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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 ...
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409
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How to calculate a single accuracy for a model with multiple outputs in Keras?
Consider the following, rather simple, model:
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106
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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 ...
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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 ...
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87
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How to top n values from .predict_proba in multioutputclassifier?
I am following MultiOutputClassifier technique to predict roles (the data are transformed to numeric so that's not a concern)
I want to use .predict_proba() and ...
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Feature selection algorithm for psychometrics, when there is several predicted variables
I'm on a psychometric study. It is a survey.
All variables are on a scale of 7. So these are considered as continuous variables.
I have this dataset:
600 features
100 predicted variables
100 survey ...
- 111
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34
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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 ...
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150
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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 + \...
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How to handle partial labels in multi output classifier using Keras API?
I am training a model with multiple categorical inputs/outputs. Some samples have partial labels.
Currently, I am dropping samples with any missing label but I am wasting a lot of data.
I would like ...
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35
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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 ...
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447
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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) ...
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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
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Y1
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Y10
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Let's say I want to improve the accuracy of Y1. I am ...
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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 ...
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53
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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:
...
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201
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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 ...
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237
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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 ...
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Custom output names for keras model
I have a model like this with multiple outputs and i want to change it's output names
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30
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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 ...
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229
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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 ...
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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-...
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43
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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:
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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 ...
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109
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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 ...
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293
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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:
...
- 325
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89
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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, ...
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31
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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 ...
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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, ...
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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} = \...
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299
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More output neurons than labels?
When we train a neural network model for a classification problem, we usually have a dense output layer of size equal to the number of labels we have.
If the layer size was greater, the model can ...
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