# Questions tagged [objective-function]

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### 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 ...
0answers
324 views

### XGBoost custom objective for regression in R

I implemented a custom objective and metric for a xgboost regression task. In order to see if I'm doing this correctly, I started with a quadratic loss. The ...
1answer
224 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 ...
1answer
32 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?
0answers
33 views

### Gamma objective function XGBoost

I am using XGBoost to predict a variable that is highly skewed and always is greater than zero. I did a significant search to see some materials for gamma objective function in XGBoost but I could not ...
0answers
37 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 ...
0answers
17 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 <...
2answers
324 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 ...
1answer
36 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 ...
0answers
25 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 ...
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 ...
1answer
100 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. ...
2answers
51 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. ...
0answers
46 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 ...
2answers
60 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 ...
3answers
180 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 ...
0answers
32 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 ...
1answer
191 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 ...