Questions tagged [objective-function]

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0answers
72 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 ...
3
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1answer
98 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
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1answer
17 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?
0
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0answers
25 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 ...
0
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0answers
22 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 ...
1
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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 <...
6
votes
2answers
199 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
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1answer
30 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
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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 ...
1
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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
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1answer
98 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
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2answers
48 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
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0answers
43 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
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2answers
55 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
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3answers
131 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 ...
3
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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 ...
3
votes
1answer
185 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 ...