Questions tagged [linear-models]
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Logistics or linear regression for a regression task involving outputs between 0 and 1
Problem
Consider a regression task of mapping inputs $X$ to outputs $y$ where $y \in [0,1]$.
Two linear models that we can use to model this input-output relationships are logistic regression $f_\...
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Can reducing information improve regression prediction?
Variable A is either 0 or 1. It is 0 if the sum of variables a + b + c + d … is less than some constant threshold, and is 1 if the sum of variables a + b + c + d … is greater than some constant ...
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R lm log v log log for asymptotic fit
I have data from a "bleed" experiment. That is, I fill a chamber with a gas that I can measure precisely (say, methane), and then just let it sit. The chamber has a natural bleed rate, which ...
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Linear Regression and Logistic Regression
I'm a beginner, and I'm wondering whether a logistic regression in a nut-shell is just normalizing a linear regression? Correct me if I'm wrong, but I came to this conclusion because the predicted ...
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warning 'newdata' had X row but variables found have Y rows
Linear Discriminant Analysis (LDA)+logistic regression model
lda_model <- lda(train_labels ~ Sepal.Length + Sepal.Width + Petal.Length + Petal.Width, data = train_data)
LDA scores for the training ...
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Making a NN closer to a linear regression?
It is possible to 'initialise' a gradient boosted model with a simpler model, such as linear regression, by manually setting the initial score. This seems to help reduce the discrepencies between the ...
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Need feedback on idea for new regularization term
I've been working on creating a regularization term that ensures that correlated attributes are given similar weights in a linear model. This helps to avoid some of the inconsistency in the weights of ...
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Linear Model With Highly Correlated Attributes Producing Inconsistent Weights
I know that having correlated attributes violates the linear model assumption of independent attributes, and I'm not interested in creating a more sophisticated model to tease apart the dependent ...
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What are different ways to determine how an explanatory variable affect a target variable?
I'm trying to determine a quantitative value by which a target variable change (inflation) by changing an indicator variable (interest rate).
The industry basically uses linear models such as VAR. Are ...
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Calculating weight and bias of linear perceptron on convergence given number mistakes for each sample
A linear perceptron has been trained with a set of n points (∈ ℝ²) and their corresponding labels ...
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What are the advantages of model drift vs concept drift in online learning?
I have asked this question here but I'm also posting it here to get a better insight:
https://stats.stackexchange.com/questions/602282/what-are-the-advantages-of-model-drift-vs-concept-drift-in-online-...
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Is Linear kernel SVM always better than Logistic regression?
We know that linear kernel SVM may give better results than logistic regression since maximizing the margin usually leads to more stable results/better displacement of the decision boundary. But is ...
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How do the intercept and slope calculated in linear regression relate to the output of lm?
I have been looking at how to calculate coefficients by hand
and the example produces
$Y = 1,383.471380 + 10.62219546 * X$
However the output shown of lm does not show these values anywhere.
How do I ...
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Linear Regression: Won't adding irrelevant features still improve the prediction
Assume we are predicting weight based on height: this is simple linear regression. If we now add gender, this creates multiple linear regression and improves our model, and makes it more capable of ...
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Is it a good idea to test the robustness of a Neural Network on a linear relation?
Just to give you more context, I'm currently working on a finance project relying on neural network. I'm principally using Neural Network to achieve regression task. So my neural network aims to ...
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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 ...
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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]...
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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 ...
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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 ...
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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 ...
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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 $...
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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?
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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 ...
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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 ...
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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 ...
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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. ...
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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 ...
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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 ...
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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 ?
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