Questions tagged [linear-models]

The tag has no usage guidance.

Filter by
Sorted by
Tagged with
1 vote
1 answer
25 views

Temperature lag forecasting

I am working on a data science project on an industrial machine. This machine has two heating infrastructures. (fuel and electricity). It uses these two heatings at the same time, and I am trying to ...
user avatar
  • 23
0 votes
0 answers
28 views

Feature importance of a linear regression

What is the easiest and easy to explain feature importance calculation for linear regression? I know I can use Shap to compute feature importance, but I find it difficult to explain it to stakeholders,...
user avatar
  • 209
0 votes
0 answers
19 views

How to write this TeX equation appropriately for publication?

I have a 2 variables that resemble 2 different tests. I would like to multiple it by 0.2 with conditions. If test1 is available and ...
user avatar
  • 1
0 votes
1 answer
44 views

What is the SHAP values for a liner model? How do we derive that?

What is the SHAP values for a linear model? it is given as below in the documentation Assuming features are independent leads to interventional SHAP values which for a linear model are coef[i] * (x[i]...
user avatar
  • 209
1 vote
1 answer
38 views

Is it possible to explain why Lasso models eliminated certain coefficient?

Is it possible to understand why Lasso models eliminated specific coefficients?. During the modelling, many of the highly correlated features in data is being eliminated by Lasso regression. Is it ...
user avatar
  • 209
0 votes
0 answers
6 views

Is it possible to find the feature importance of an aggregate feature from the corresponding independent features in a linear model?

I have a model to predict energy consumption in a food processing plant. Different food products are produced in the plant. My model is given as ...
user avatar
  • 209
0 votes
1 answer
31 views

Can I include a quotient as dependent variable and independent variables with same denominator in a linear model? How do we interpret such models?

I want to create a model in a food processing plant where my dependent variable is Electricity (KWhr) consumption per kg. Plant produce different food items with varying electricity consumption. I'm ...
user avatar
  • 209
0 votes
0 answers
27 views

Lasso (or Ridge) vs Bayesian MAP

This is the first time I have posted here. I am looking for some feedback or perspective on this question. To make it simple, let's just talk about linear models. We know the MLE solution for the $l_1$...
user avatar
0 votes
0 answers
7 views

Ideas to enforce uniformity of error in linear models

I am looking for ideas to not only solve the least square problem, but to enforce errors to be roughly similar. One idea I had is to add the variance of errors in the classical Ordinary Least Square ...
user avatar
  • 1
0 votes
0 answers
20 views

Approaches for multiclass classification with a reference level to extract variables of importance?

I have a dataset with with multiple classes (< 20) which I want to classify in reference to one of the classes.The final goal is to extract the variables of importance which are useful to ...
user avatar
1 vote
0 answers
25 views

Does the appliance of R-squared to non-linear models depends on how we calculate it?

Does the appliance of R-squared to non-linear models depends on how we calculate it? $R^2 = \frac{SS_{exp}}{SS_{tot}}$ is going to be an inadequate measure for non-linear models since an increase of $...
user avatar
  • 121
1 vote
1 answer
29 views

The effect of the λ in the Ridge regression

Why by increasing value of λ in Ridge estimator the slope of the line is decreasing? How exactly λ affects to the y = kx + b?
user avatar
  • 11
1 vote
2 answers
809 views

How to visualize optimization problems' feasible region?

Is there any tool to visualize the feasible region when given a set of Linear equations (equalities and inequalities). If not, can anyone suggest a way to visualize it? If I am going to do it myself ...
user avatar
1 vote
0 answers
46 views

Understanding the math behind linear classification [closed]

For example we have $X$ train data, $y$ and $w$ Our margin is $M = y_i \langle w, x_i \rangle$ If $M_i > 0$ classifier return True predict and otherwise, if $M_i < 0$ we get False predict. How ...
user avatar
1 vote
0 answers
46 views

Does linear kernel make SVM a linear model?

I have deleloped several SVR models for my case study using the linear kernel, and those models were optimized using the RMSE as criterion. Now Im searching for additional evaluation metrics and it ...
user avatar
  • 33
0 votes
1 answer
157 views

How to make a linear model with a constant value in R?

I'm working on an unassessed homework problem from unpublished course notes of a statistics module from a second year university mathematics course. I'm trying to plot a 2-parameter full linear model ...
user avatar
  • 103
0 votes
0 answers
42 views

What is the Intuition behind weight vector W which is normal to the plane? Is the weight vector W same as the W which is normal to the plane π?

In an interview, I was asked the intuition behind the weight vector. I told the weight vector is a vector which we try to minimize to a local minima with the help of regulariser so we don't overfit. ...
user avatar
0 votes
1 answer
250 views

Can absolute or relative contributions from X be calculated for a multiplicative model? $\log{ y}$ ~ $\log {x_1} + \log{x_2}$

(How) can absolute or relative contributions be calculated for a multiplicative (log-log) model? Relative contributions from a linear (additive) model E.g., there are 3 contributors to $y$ (given by ...
user avatar
  • 131
1 vote
0 answers
83 views

How can I compare a NN model and a linear regression?

I have a small dataset (1500 rows) and to predict the imbalanced target, I am running two linear models (linear regression and lasso) and one nonlinear model (Neural Network) on it. I am using Area ...
user avatar
3 votes
1 answer
308 views

what is difference between Logistic regression and SGDClassifier with log loss OR SVM and SGDClassifer with hinge loss?

Can we just use SGDClassifier with log loss instead of Logistic regression, would they have similar results ?
user avatar