Ridge regression shrinks the coefficient to a lower value

What is ridge regression?

Ridge regression is a method used to create a predictive model by reducing the magnitude of the coefficients. This method is used when there is a need to reduce the variance in the estimate of the coefficients. The ridge regression method is also known as the L2 regularization method.

How does it work?

Ridge regression is very similar to ordinary least squares, except that the coefficients are estimated by minimizing a slightly different cost function. In particular, ridge regression minimizes the sum of squared residuals plus a penalty equivalent to squared values of the estimated coefficients. The amount of shrinkage is directly proportional to the amount of regularization parameter lambda, and inversely proportional to the number of predictors in the model. As lambda increases, more variables are penalized toward zero. When lambda is set to zero, no variable is penalized and ridge regression becomes equivalent to least squares.

Why is it used?

Ridge regression is used to create models that are able to predict continuous values. It is often used in time series analysis or when there is collinearity present in the data. Ridge regression shrinks the coefficient estimates to reduce the standard errors, and also improves the prediction accuracy.

How to perform ridge regression in Python

Ridge regression is a type of linear regression that includes a regularization term in the cost function. The regularization term is used to control the overfitting of the model. In this section, we will discuss how to perform ridge regression in Python.

Set up the environment

If you don’t have Python installed yet, I would highly recommend doing that first. I prefer to use the Anaconda distribution because it comes with a lot of useful packages already installed and it makes it easy to install new packages.

Once you have Anaconda installed, create a new environment for this tutorial:

conda create -n ridge-regression python=3.6 anaconda
We will also need the following packages for this tutorial:

-scikit-learn: Machine learning in Python
-pandas: Data manipulation and analysis in Python
-matplotlib: 2D plotting in Python

Load the data

In this tutorial, we’ll be using a dataset from the UCI Machine Learning Repository that describes the chemical composition of wine, and trying to predict the quality of the wine using regression.

We’ll use pandas to load and inspect the data, and seaborn to visualize it.

import pandas as pd
import seaborn as sns

Preprocess the data

Before you can fit a ridge regression model, you need to preprocess the data. This means that you need to standardize your data and split it into training and test sets.

Standardizing your data is important because it ensures that all the features are on the same scale. This makes it easier for the model to learn from the data and improves the interpretability of the coefficients.

You can standardize your data using scikit-learn’s StandardScaler class. This class centeres the data around 0 and scales it so that all the features have a variance of 1.

Once you’ve standardized your data, you need to split it into training and test sets. You’ll use the train_test_split function from scikit-learn to do this. By default, this function will split 75% of your data into a training set and 25% into a test set.

Train the model

To train the ridge regression model, you need to import the Ridge class from the sklearn.linear_model library.

Next, you need to create an instance of the Ridge class and specify the value of alpha. Again, you can use any value for alpha. A good starting point is 0.1.

Then, you need to fit the model on your training data using the fit() method.

You can make predictions using the predict() method.

Evaluate the model

After you have trained your model, it is important to evaluate it to make sure that it is performing well. There are two common ways to evaluate linear models:
-R-squared: This metric measures how well the model predicts the data. It can be between 0 and 1, and the higher the better.
-Mean squared error: This metric measures the average squared error of the model. This is useful if you want to compare different models.

In general, you want to choose a model with a high R-squared and a low mean squared error.

To evaluate a ridge regression model in Python, you can use the r2_score() and mean_squared_error() metrics from the sklearn.metrics package.

Advantages and disadvantages of ridge regression

Ridge regression is a type of regression analysis that is used to create predictive models. It shrinks the coefficients to lower values, which can help reduce overfitting. Ridge regression can also help with multicollinearity. However, ridge regression can be computationally expensive and it does not always produce the best results.


Ridge regression is an approach used when the data has multicollinearity (correlation between independent variables). In multicollinearity, even though the least squares approach (ordinary regression) produces an estimated equation, the predictions made by the equation may have a large error. This is because the least squares estimates are highly sensitive to random errors in the data. Ridge regression reduces this variance.

Ridge regression also has some advantages over least squares in cases where you do not have a lot of data. When you have less than 20 observations per independent variable in your model, ridge regression performs better than least squares. Finally, ridge regression does not require that you standardize your data before performing the analysis, as least squares does.


The first disadvantage of ridge regression is that it can only be used when there is a linear relationship between the dependent and independent variables. If there is a non-linear relationship, then ridge regression will not be able to accurately model the data.

Another disadvantage of ridge regression is that it can be computationally expensive to train. This is because the training process requires the calculation of a large number of coefficients, which can take a significant amount of time if there are a large number of features.

Finally, ridge regression may not be able to accurately learn the underlying structure of the data if there is a lot of noise. This is because ridge regression will try to fit a straight line through the data points, even if there is a lot of variation in the points.

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