Questions tagged [objective-function]

The tag has no usage guidance.

Filter by
Sorted by
Tagged with
0 votes
0 answers
13 views

What are desirable properties of layers in Deep Learning?

I have been thinking about the following the problem: Given some task we assume there is a magical function that perfectly solve this task. For example, if we want to distinguish cats and dogs, then ...
user avatar
  • 148
0 votes
0 answers
10 views

Find suitable function to score example data based on given rule

I'm not an expert in the AI topic but for my underlying problem I need to find a function which rates data samples based on a specific value x. This means that based on the output of the function it ...
user avatar
  • 1
2 votes
1 answer
83 views

Optimizing MAE degrades MAE metrics

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

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 ...
user avatar
0 votes
0 answers
24 views

How can i have a NN without target?

I have some candidate items that I want to choose a subset of them that maximize an objective function. I don't know what is the target, or which subset is really best according to my objective ...
user avatar
1 vote
1 answer
25 views

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 ...
user avatar
7 votes
1 answer
1k views

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 ...
user avatar
  • 6,912
4 votes
1 answer
697 views

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 ...
user avatar
  • 165
0 votes
1 answer
190 views

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?
user avatar
1 vote
0 answers
51 views

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 ...
user avatar
1 vote
0 answers
18 views

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 <...
user avatar
7 votes
2 answers
832 views

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 ...
user avatar
  • 203
0 votes
1 answer
131 views

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 ...
user avatar
  • 101
2 votes
0 answers
26 views

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 ...
user avatar
1 vote
0 answers
30 views

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 ...
user avatar
2 votes
1 answer
113 views

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. ...
user avatar
1 vote
2 answers
99 views

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. ...
user avatar
1 vote
0 answers
49 views

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 ...
user avatar
  • 11
0 votes
2 answers
69 views

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 ...
user avatar
0 votes
3 answers
349 views

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 ...
user avatar
  • 741
4 votes
1 answer
48 views

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 ...
user avatar
5 votes
1 answer
215 views

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 ...
user avatar
  • 17.6k