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Questions tagged [objective-function]

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Objective function in Bayesian Hyperparameter Tuning

I have a question that has been going around in my head for a while and I'd like to leverage the wisdom of the crowd for getting a few opinions on it. Let me describe the Problem: I have a relatively ...
Hive5's user avatar
  • 1
1 vote
0 answers

Xgboost custom objective function. How to modify the weights?

I have a custom objective function to xgboost: ...
Gábor B's user avatar
0 votes
0 answers

Custom objective function for xgboost to optimize lift in best decile

I've tried to define a custom objective function for xgboost to optimize the lift for a binary classification problem in the upper decile. The task is simply to concentrate the training effort to the ...
Arne Bøckmann's user avatar
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0 answers

In the GAN objective function, why do we first do we first find the D(x) that maximizes the objective function and then maximise wrt the generator?

The GAN objective function is optimised like this: argmin(argmax(L(G,D))) where the argmax finds the D (Discriminator) that maximises L(G,D). Why is it not the other way around, i.e. argmax(argmin(L(G,...
thebasqueinterdisciplinarian's user avatar
1 vote
1 answer

Why is monotonic constraint disabled when using MAE as an objective to LGBM?

I tried to use monotonic constraints in LGBM, but if I use mean absolute error as an objective, it gives a warning that monotonic constraints cannot be done in l1. What is the reason? Thanks!
morqueatsz's user avatar
0 votes
1 answer

Gradient tree boosting additive training

In the XGBoost documentation, they specify that the additive training is done given an objective $obj^{(t)}$ defined as $obj^{(t)} = \sum\limits_{i=1}^n \ell(y_i, \hat{y}_i^{(t-1)}+f_t(x_i)) + \sum\...
Hadar's user avatar
  • 167
2 votes
2 answers

Optimizing MAE degrades MAE metrics

I have run a lighgbm regression model by optimizing on RMSE and measuring the performance on RMSE: ...
Mark531's user avatar
  • 121
1 vote
0 answers

Best way to optimize problem with additively separable fitness function?

I am using a genetic algorithm to maximize a few hundred thousand real-valued variables. Each of the variables, $x_i$, has its own independent boundary condition. The fitness function uses each of ...
João Bravo's user avatar
1 vote
1 answer

What is a good reward function when objective is to minimize the average along with the variance?

I am trying to formulate a problem where we are trying to minimize the average resource allocated to different users. Due to some inherent properties of the environment, some users can be easily ...
user3656142's user avatar
10 votes
1 answer

XGBoost custom objective for regression in R

I implemented a custom objective and metric for a xgboost regression. In order to see if I'm doing this correctly, I started with a quadratic loss. The ...
Peter's user avatar
  • 7,536
4 votes
2 answers

Appropriate objective function and evaluation metric when I DO care about outliers?

I am reading these two pages: xgboost documentation Post on evaluation metrics I have a dataset where I am trying to predict future spend at the user level. A lot of our spend comes from large ...
Doug Fir's user avatar
  • 165
1 vote
1 answer

XGB custom objective function - small change to default regression squared error objective function

Where can I find the code for the default squared error objective function? I just want to make a small change to re-weight certain datapoints?
xxanissrxx's user avatar
1 vote
0 answers

Stochastic gradient descent (SGD)

The objective function 𝐽(πœƒ) = [1π‘›βˆ‘π‘–=1𝑛Lossβ„Ž(𝑦(𝑖)πœƒβ‹…π‘₯(𝑖))]+πœ†2β€–πœƒβ€–2 where Lossβ„Ž(𝑧)=max{0,1βˆ’π‘§} is the hinge loss function, (π‘₯(𝑖),𝑦(𝑖)) with for 𝑖=1,…𝑛 are the training ...
user3159445's user avatar
1 vote
0 answers

Optimization problem with different type of constraints

I'm new to optimization problems. I want to find optimum values for my objective function. You can imagine my function as E = f(t1, t2, t3). I want to minimize <...
Babak.Abad's user avatar
9 votes
2 answers

Optimising for Brier objective function directly gives worse Brier score than optimising with custom objective - what does it tell me?

I am training an XGBoost model and as I care the most about resulting probabilities, not classification itself I have chosen Brier score as a metric for my model, so that probabilities would be well ...
Xaume's user avatar
  • 202
0 votes
2 answers

Using DNN as the objective function for a multi-objective optimization algorithm

When creating a multi-objective optimisation/MCDM algorithm such as NSGA-ii, does it make sense to use a deep neural network trained on a supervised tabular regression prediction task, in place of a ...
Edan's user avatar
  • 101
2 votes
0 answers

Image reconstruction using low-light components

Let's say we have a regular photo and three low-light photos illuminated in different colors. Each pixel is a three-component vector $q=(R,G,B)$. Then $q_k^{A}$ is the $k$-th pixel of the regular ...
Don Draper's user avatar
1 vote
0 answers

Deeplearning without an objective function?

In this article, the author talks about how deeplearning models no longer are trained for an objective function that humans specify, but find their own objective function. Specifically, he is talking ...
user637140's user avatar
2 votes
1 answer

Non-linear Regression

For example suppose I've data set which looks like: [[x,y,z], [1,2,5], [2,3,8], [4,5,14]] It's easy to find the theta parameters from those tiny data set. ...
Surya Bhusal's user avatar
1 vote
2 answers

How to determine the function is linear in linear regression problem?

I know that the first degree of the polynomial equation is considered as a linear function. But, I found some things confusing in linear regression. ...
Animesh Kumar Paul's user avatar
1 vote
0 answers

Linear regression space transformation

Can someone help me how space transformation works on linear regression problems because I have been confused. When we perform space transformation with a function e.g. $\varphi (x)$ we perform the ...
Er1Hall's user avatar
  • 11
0 votes
2 answers

Newbie: Objective Function

I am reading the book "Data Science for Business" by Foster Provost & Tom Fawcett. Only a fourth of the way through. I am unclear about the concept of Objective Function. I will nevertheless take ...
BluedogVIP's user avatar
0 votes
3 answers

can machine learning/Deep learning used to minimize an objective function?

I have data of construction site and am wondering if i can use machine learning to reduce the cost it takes to build a building. But, as far as i know, Machine learning can only does function ...
rawwar's user avatar
  • 861
4 votes
1 answer

neural network function approximation with constraints

I would like to approximate a function $f(\cdot)$ by means of a neural network given a finite set of observations $f(x_i)$ where $x_i\in\mathbb{R}^n$ and $i=1\dots,N$. However, I have some prior ...
user149575's user avatar
7 votes
1 answer

What is a good objective function for allowing close to 0 predictions?

Let's say we want to predict the probability of rain. So just the binary case: rain or no rain. In many cases it makes sense to have this in the [5%, 95%] interval. And for many applications this ...
Martin Thoma's user avatar