Questions tagged [ridge-regression]

A regularization method for regression models that shrinks coefficients towards zero.

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How is learning rate calculated in sklearn Lasso regression?

I was applying different regression models to Kaggle Housing dataset for advanced regression. I am planning to test out lasso, ridge and elastic net. However, none of these models have learning rate ...
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Determining the degree of freedom in ridge regression | Interpretation of the head matrix

right now, I am diving into statistical learning and stumbled over the so-called "head-matrix" and the determination of the degree of freedom. I am referring to ridge regression: So the ...
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Can we use both ridge-lasso and PCA in the same model for better results?

My question here is if we are using the PCA, the dimensionality is reduced and no question of feature selection is required using ridge & lasso. So should I use ride-lasso followed by PCA or I ...
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Is there a reference data set for ridge regression?

In order to test an algorithm, I am looking for a reference data set for ridge regression in research papers. Kind of like the equivalent of MNIST but for regression.
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Extremely high MSE/MAE for Ridge Regression(sklearn) when the label is directly calculated from the features

Edit: Removing TransformedTargetRegressor and adding more info as requested. Edit2: There were 18K rows where the relation did not hold. I'm sorry :(. After ...
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Does ridge regression always reduce coefficients by equal proportions?

Below is an excerpt from the book Introduction to statistical learning in R, (chapter-linear model selection and regularization) "In ridge regression, each least squares coefficient estimate is ...
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Matlab - Financial Modeling, Linear Regression with Prior

Am trying to implement this equation from the book Doing Data Science Straight Talk from the frontline, In chapter 6, page 161, equation below: From what i can tell it is pretty much an enchanced ...
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Dividing the weights obtained on an already standardized data set by the standard deviation of the features? (Ridge regression)

I'm trying to understand a code snippet from my lecture on Machine Learning (see the code below). It extracts the mean and standard deviation of the features and uses them to 'normalize' (...
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Does ridge regression reduce the coefficients of the variables all the way to zero at very high penalty

I have read in one article that ridge regression doesn't reduce the coefficients of the features to zero where as in some other article I read that it can reduce the coefficients to zero when a very ...
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Can ridge regression be used for feature selection?

I'm trying to figure out whether using Ridge Regression for regularization can be used to cause a more sparse hypothesis however to me it seems like ridge will never actually bring any coefficients to ...
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Does it matter whether we put regularization parameter ($C$) with error or weight term in Kernel ridge regression?

Kernel ridge regression associate a regularization parameter $C$ with weight term ($\beta$): $\text{Minimize}: {KRR}=C\frac{1}{2} \left \|\beta\right\|^{2} + \frac{1}{2}\sum_{i=1}^{\mathcal{N}}\left\|...
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Running into strange errors when using glmnet and generating graphs

I developed a very simple Ridge Regression with glmnet, the R package. When I use the plot.glmnet() function I encounter strange errors: ...
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How to improve Regression Model with High Training Performance and Low Test Performance

I am performing regression analysis on some data. I keep getting very high training score and low test score. My code is below, what can i do to enhance it? Thank you in advance. ...
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What does a negative coefficient of determination mean for evaluating ridge regression?

Judging by the negative result being displayed from my ridge.score() I am guessing that I am doing something wrong. Maybe someone could point me in the right ...