Questions tagged [objective-function]
The objective-function tag has no usage guidance.
22
questions
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 ...
- 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 ...
- 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:
...
- 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 ...
- 127
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 ...
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 ...
- 181
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 ...
- 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 ...
- 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?
- 103
1
vote
0
answers
51
views
Stochastic gradient descent (SGD)
The objective function 𝐽(𝜃) = [1𝑛∑𝑖=1𝑛Lossℎ(𝑦(𝑖)𝜃⋅𝑥(𝑖))]+𝜆2‖𝜃‖2
where Lossℎ(𝑧)=max{0,1−𝑧} is the hinge loss function, (𝑥(𝑖),𝑦(𝑖)) with for 𝑖=1,…𝑛 are the training ...
- 71
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 <...
- 121
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 ...
- 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 ...
- 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 ...
- 131
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 ...
- 201
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. ...
- 143
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.
...
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 ...
- 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 ...
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 ...
- 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 ...
- 41
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 ...
- 17.6k