# Questions tagged [ridge-regression]

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

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99 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 ...
20 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) ...
41 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. ...
93 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 ...
12 views

### Prove Ridge regression duality of dropout

EDIT: Found one mistake, confused rows and columns in the step of summing over column operations. I'm trying to prove the Ridge regression duality of Dropout, as described in section 9.1 of this paper....
20 views

### Should I normalize the dependent variable in a penalized linear regression model?

When I compute penalized regression on the data without normalizing using the glmnet package in R, the lambda values and RMSE generated in lasso, ridge, and elastic net are unreasonably large. The ...
52 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 ...
204 views

### What is the meaning of the sparsity parameter

Sparse methods such as LASSO contains a parameter $\lambda$ which is associated with the minimization of the $l_1$ norm. Higher the value of $\lambda$ ($>0$) it means that more coefficients will be ...
53 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 ...
15 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 ...
585 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 ...
68 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 ...
254 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 ...
128 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.
793 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 ...
211 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 ...
99 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' (...
4k 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 ...
144 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\|...