Questions tagged [objective-function]
The objective-function tag has no usage guidance.
21
questions
2
votes
1answer
32 views
Optimizing MAE degrades MAE metrics
I have run a lighgbm regression model by optimizing on RMSE and measuring the performance on RMSE:
...
1
vote
0answers
46 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 ...
0
votes
0answers
19 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 ...
0
votes
0answers
18 views
Calibration of a few binary classifiers is not perfect - why?
I am working on a binary classifier using LightGBM. I try to see the results of the classifiers when changing the costs of false positives and false negatives, still working on the same training and ...
1
vote
1answer
22 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 ...
5
votes
0answers
743 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 ...
4
votes
1answer
502 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 ...
0
votes
1answer
89 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?
1
vote
0answers
45 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 ...
1
vote
0answers
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 <...
7
votes
2answers
573 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 ...
0
votes
1answer
64 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 ...
2
votes
0answers
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 ...
1
vote
0answers
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 ...
2
votes
1answer
109 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. ...
1
vote
2answers
69 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
0answers
48 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 ...
0
votes
2answers
64 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
3answers
266 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 ...
4
votes
1answer
41 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 ...
4
votes
1answer
199 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 ...
