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Questions tagged [ridge-regression]

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

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8 views

Regularizing the intercept - particular case

Yesterday I posted this thread Regularizing the intercept where I had a question about penalizing the intercept. In short, I asked wether there exist cases where penalizing the intercept leads to a ...
0 votes
1 answer
9 views

How to extract MSEP or RMSEP from lassoCV?

I'm doing lasso and ridge regression in R with the package chemometrics. With ridgeCV it is easy to extract the SEP and MSEP values by ...
0 votes
1 answer
73 views

Why are my ridge regression coefficients completely different from ordinary linear regression coefficients in MATLAB?

I am attempting to implement my own Ridge Regression algorithm and I am trying to achieve similar coefficients found in a MATLAB tutorial on regression. Specifically, on the MATLAB tutorial page you ...
• 101
1 vote
1 answer
60 views

What's the correct cost function for Linear Regression

As we all know the cost function for linear regression is: Where as when we use Ridge Regression we simply add lambda*slope**2 but there I always seee the below as cost function of linear Regression ...
• 163
1 vote
1 answer
28 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?
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3 votes
2 answers
410 views

Constraining linear regressor parameters in scikit-learn?

I'm using sklearn.linear_model.Ridge to use ridge regression to extract the coefficients of a polynomial. However, some of the coefficients have physical ...
• 31
0 votes
0 answers
105 views

Should I use or tune reg_lambda or reg_alpha hyperparameters when using a tree booster in XGBoost

XGBoost has 3 types of boosters: tree boosters (gbtree, dart) linear booster (gbliner) ...
• 101
1 vote
1 answer
57 views

Why we take $\alpha\sum B_j^2$ as penalty in Ridge Regression?

$$RSS_{RIDGE}=\sum_{i=1}^n(\hat{y_i}-y_i)^2+\alpha\sum_{i=1}^nB_j^2$$ Why we are taking $\alpha\sum B_j^2$ as a penalty here? We are adding this term for minimizing variance in Machine Learning Model. ...
-1 votes
1 answer
100 views

Lack of standardization in Kaggle jupyter notebooks when using lasso/ridge?

I've recently started using Kaggle, and I've noticed that for a lot of these jupyter notebooks written by others, when they use Ridge/Lasso, they don't standardize the non-categorical numerical ...
1 vote
1 answer
125 views

Do the benefits of ridge regression diminish with larger datasets?

I have a question about ridge regression and about its benefits (relative to OLS) when the datasets are big. Do the benefits of ridge regression disappear when the datasets are larger (e.g. 50,000 vs ...
1 vote
2 answers
366 views

What is the meaning of the sparsity parameter

Sparse methods such as LASSO contain a parameter $\lambda$ which is associated with the minimization of the $l_1$ norm. Higher the value of $\lambda$ ($>0$) means that more coefficients will be ...
• 521
0 votes
2 answers
152 views

how Lasso regression helps to shrinks the coefficient to zero and why ridge regression dose not shrink the coefficient to zero?

How Lasso regression helps feature selection of model by making the coefficient to zero? , I could see few below with below diagram ,can any please explain in simple terms how to corelate below ...
• 1,131
1 vote
0 answers
16 views

what other metrics can i use to estimate quality of the model predicting income range - interval estimation task?

I trained a model that predicts customer's income given the features: age, declared income number of oustanding instalment, overdue total amount active credit limit, total credit limit total amount ...
• 111
2 votes
2 answers
1k views

How do standardization and normalization impact the coefficients of linear models?

One benefit of creating a linear model is that you can look at the coefficients the model learns and interpret them. For example, you can see which features have the most predictive power and which do ...
• 177
1 vote
1 answer
87 views

Why is Regularization after PCA or Factor Analysis a bad idea?

I have done Factor Analysis on my data and applied various machine learning models on it. I particularly find it giving high MSE value for Ridge and Lasso Regression compared to other models. I want ...
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1 vote
1 answer
438 views

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 ...
0 votes
1 answer
279 views

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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4 votes
2 answers
1k views

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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3 votes
1 answer
294 views

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 ...
• 195
1 vote
1 answer
125 views

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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3 votes
3 answers
6k views

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 ...
• 197
3 votes
1 answer
165 views

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\|...
1 vote
1 answer
273 views

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. ...
• 655
4 votes
2 answers
2k views

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 ...
• 1,391