# Questions tagged [ridge-regression]

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

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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 ...
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 ...
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 ...
144 views

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 ...
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' (...
225 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. ...
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 ...
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 ...
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 ...
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 ...
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) ...
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 ...