What is Ridge Regression?

What is Ridge regression?

Ridge regression is a model-tuning method that is used to analyze any data that suffers from multicollinearity. This method performs L2 regularization. When the issue of multicollinearity occurs, least-squares are unbiased, and variances are large, this results in predicted values being far away from the actual values.

The cost function for ridge regression:

Min(||Y – X(theta)||^2 + λ||theta||^2)

Lambda is the penalty term. λ given here is denoted by an alpha parameter in the ridge function. So, by changing the values of alpha, we are controlling the penalty term. The higher the values of alpha, the bigger is the penalty and therefore the magnitude of coefficients is reduced.

It shrinks the parameters. Therefore, it is used to prevent multicollinearity
It reduces the model complexity by coefficient shrinkage
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Ridge Regression Models

For any type of regression machine learning model, the usual regression equation forms the base which is written as:

Y = XB + e

Where Y is the dependent variable, X represents the independent variables, B is the regression coefficients to be estimated, and e represents the errors are residuals.

Once we add the lambda function to this equation, the variance that is not evaluated by the general model is considered. After the data is ready and identified to be part of L2 regularization, there are steps that one can undertake.

Standardization

In ridge regression, the first step is to standardize the variables (both dependent and independent) by subtracting their means and dividing by their standard deviations. This causes a challenge in notation since we must somehow indicate whether the variables in a particular formula are standardized or not. As far as standardization is concerned, all ridge regression calculations are based on standardized variables. When the final regression coefficients are displayed, they are adjusted back into their original scale. However, the ridge trace is on a standardized scale.

Also Read: Support Vector Regression in Machine Learning

Bias and variance trade-off

Bias and variance trade-off is generally complicated when it comes to building ridge regression models on an actual dataset. However, following the general trend which one needs to remember is:

The bias increases as λ increases.
The variance decreases as λ increases.

Assumptions of Ridge Regressions

The assumptions of ridge regression are the same as those of linear regression: linearity, constant variance, and independence. However, as ridge regression does not provide confidence limits, the distribution of errors to be normal need not be assumed.

Now, let’s take an example of a linear regression problem and see how ridge regression if implemented, helps us to reduce the error.

We shall consider a data set on Food restaurants trying to find the best combination of food items to improve their sales in a particular region.

Upload Required Libraries

import numpy as np   
import pandas as pd
import os
 
import seaborn as sns
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt   
import matplotlib.style
plt.style.use('classic')
 
import warnings
warnings.filterwarnings("ignore")

df = pd.read_excel("food.xlsx")

After conducting all the EDA on the data, and treatment of missing values, we shall now go ahead with creating dummy variables, as we cannot have categorical variables in the dataset.

df =pd.get_dummies(df, columns=cat,drop_first=True)

Where columns=cat is all the categorical variables in the data set.

After this, we need to standardize the data set for the Linear Regression method.

Scaling the variables as continuous variables has different weightage

#Scales the data. Essentially returns the z-scores of every attribute
 
from sklearn.preprocessing import StandardScaler
std_scale = StandardScaler()
std_scale

df['week'] = std_scale.fit_transform(df[['week']])
df['final_price'] = std_scale.fit_transform(df[['final_price']])
df['area_range'] = std_scale.fit_transform(df[['area_range']])

Train-Test Split

# Copy all the predictor variables into X dataframe
X = df.drop('orders', axis=1)
 
# Copy target into the y dataframe. Target variable is converted in to Log. 
y = np.log(df[['orders']])

# Split X and y into training and test set in 75:25 ratio
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25 , random_state=1)

Linear Regression Model

Difference Between Ridge Regression Vs Lasso Regression

Aspect	Ridge Regression	Lasso Regression
Regularization Approach	Adds penalty term proportional to square of coefficients	Adds penalty term proportional to absolute value of coefficients
Coefficient Shrinkage	Coefficients shrink towards but never exactly to zero	Some coefficients can be reduced exactly to zero
Effect on Model Complexity	Reduces model complexity and multicollinearity	Results in simpler, more interpretable models
Handling Correlated Inputs	Handles correlated inputs effectively	Can be inconsistent with highly correlated features
Feature Selection Capability	Limited	Performs feature selection by reducing some coefficients to zero
Preferred Usage Scenarios	All features assumed relevant or dataset has multicollinearity	When parsimony is advantageous, especially in high-dimensional datasets
Decision Factors	Nature of data, desired model complexity, multicollinearity	Nature of data, desire for feature selection, potential inconsistency with correlated features
Selection Process	Often determined through cross-validation	Often determined through cross-validation and comparative model performance assessment

Ridge Regression in Machine Learning

Ridge regression is a key technique in machine learning, indispensable for creating robust models in scenarios prone to overfitting and multicollinearity. This method modifies standard linear regression by introducing a penalty term proportional to the square of the coefficients, which proves particularly useful when dealing with highly correlated independent variables. Among its primary benefits, ridge regression effectively reduces overfitting through added complexity penalties, manages multicollinearity by balancing effects among correlated variables, and enhances model generalization to improve performance on unseen data.

The implementation of ridge regression in practical settings involves the crucial step of selecting the right regularization parameter, commonly known as lambda. This selection, typically done using cross-validation techniques, is vital for balancing the bias-variance tradeoff inherent in model training. Ridge regression enjoys widespread support across various machine learning libraries, with Python’s scikit-learn being a notable example. Here, implementation entails defining the model, setting the lambda value, and employing built-in functions for fitting and predictions. Its utility is particularly notable in sectors like finance and healthcare analytics, where precise predictions and robust model construction are paramount. Ultimately, ridge regression’s capacity to improve accuracy and handle complex data sets solidifies its ongoing importance in the dynamic field of machine learning.

The higher the value of the beta coefficient, the higher is the impact.

Dishes like Rice Bowl, Pizza, Desert with a facility like home delivery and website_homepage_mention plays an important role in demand or number of orders being placed in high frequency.
Variables showing negative effect on regression model for predicting restaurant orders: cuisine_Indian,food_category_Soup , food_category_Pasta , food_category_Other_Snacks.
Final_price has a negative effect on the order – as expected.
Dishes like Soup, Pasta, other_snacks, Indian food categories hurt model prediction on the number of orders being placed at restaurants, keeping all other predictors constant.
Some variables which are hardly affecting model prediction for order frequency are week and night_service.
Through the model, we are able to see object types of variables or categorical variables are more significant than continuous variables.

Also Read: Introduction to Regular Expression in Python

Regularization

Value of alpha, which is a hyperparameter of Ridge, which means that they are not automatically learned by the model instead they have to be set manually. We run a grid search for optimum alpha values
To find optimum alpha for Ridge Regularization we are applying GridSearchCV

from sklearn.linear_model import Ridge
from sklearn.model_selection import GridSearchCV
 
ridge=Ridge()
parameters={'alpha':[1e-15,1e-10,1e-8,1e-3,1e-2,1,5,10,20,30,35,40,45,50,55,100]}
ridge_regressor=GridSearchCV(ridge,parameters,scoring='neg_mean_squared_error',cv=5)
ridge_regressor.fit(X,y)

print(ridge_regressor.best_params_)
print(ridge_regressor.best_score_)

{'alpha': 0.01}
-0.3751867421112124

The negative sign is because of the known error in the Grid Search Cross Validation library, so ignore the negative sign.

predictors = X_train.columns
 
coef = Series(ridgeReg.coef_.flatten(),predictors).sort_values()
plt.figure(figsize=(10,8))
coef.plot(kind='bar', title='Model Coefficients')
plt.show()

From the above analysis we can decide that the final model can be defined as:

Orders = 4.65 + 1.02home_delivery_1.0 + .46 website_homepage_mention_1 0+ (-.40* final_price) +.17area_range + 0.57food_category_Desert + (-0.22food_category_Extras) + (-0.73food_category_Pasta) + 0.49food_category_Pizza + 1.6food_category_Rice_Bowl + 0.22food_category_Salad + 0.37food_category_Sandwich + (-1.05food_category_Soup) + (-0.37food_category_Starters) + (-1.13cuisine_Indian) + (-0.16center_type_Gurgaon)

Top 5 variables influencing regression model are:

food_category_Rice Bowl
home_delivery_1.0
food_category_Pizza
food_category_Desert
website_homepage_mention_1

The higher the beta coefficient, the more significant is the predictor. Hence, with certain level model tuning, we can find out the best variables that influence a business problem.

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