Creating accurate predictive models is a fundamental task in data analysis. It involves splitting data into training and test sets and applying statistical models or machine learning algorithms. Linear regression is a popular choice, but it often faces the challenge of overfitting, especially with a high number of parameters. This is where ridge and lasso regression comes in, offering practical solutions to enhance model accuracy and make informed decisions in data analysis. Regularization techniques are used to address overfitting and enhance model generalizability. Ridge and lasso regression are effective methods in machine learning, that introduce penalties on the magnitude of regression coefficients. However, their approaches and suitability differ depending on the specific data analysis problem.
In this article, we will explore the key differences between ridge and lasso regression, providing insights into when to choose one over the other. Data analysts can improve model accuracy and make more reliable predictions by understanding these regularization techniques.
When we talk about regression, we often end up discussing Linear and Logistic Regression, as they are the most popular of the 7 types of regressions. In this article, we’ll focus on Ridge and Lasso regression, which are powerful techniques generally used for creating parsimonious models in the presence of a ‘large’ number of features. Here ‘large’ can typically mean either of two things:
Though Ridge and Lasso might appear to work towards a common goal, the inherent properties and practical use cases differ substantially. If you’ve heard of them before, you must know that they work by penalizing the magnitude of coefficients of features and minimizing the error between predicted and actual observations. These are called ‘regularization’ techniques.
Here’s a table outlining the key differences between ridge vs lasso regression:
Feature | Ridge Regression | Lasso Regression |
---|---|---|
Description | Ridge regression, also known as Tikhonov regularization, is a technique that introduces a penalty term to the linear regression model to shrink the coefficient values. | Lasso regression, or Least Absolute Shrinkage and Selection Operator, is a regularization method that also includes a penalty term but can set some coefficients exactly to zero, effectively selecting relevant features. |
Penalty Type | Ridge regression utilizes an L2 penalty, which adds the sum of the squared coefficient values multiplied by a tuning parameter (lambda). | Lasso regression employs an L1 penalty, which sums the absolute values of the coefficients multiplied by lambda. |
Coefficient Impact | The L2 penalty in ridge regression discourages large coefficient values, pushing them towards zero but never exactly reaching zero. This shrinks the less important features’ impact. | The L1 penalty in lasso regression can drive some coefficients to exactly zero when the lambda value is large enough, performing feature selection and resulting in a sparse model. |
Feature Selection | Ridge regression retains all features in the model, reducing the impact of less important features by shrinking their coefficients. | Lasso regression can set some coefficients to zero, effectively selecting the most relevant features and improving model interpretability. |
Use Case | Ridge regression is useful when the goal is to minimize the impact of less important features while keeping all variables in the model. | Lasso regression is preferred when the goal is feature selection, resulting in a simpler and more interpretable model with fewer variables. |
Model Complexity | Ridge regression tends to favor a model with a higher number of parameters, as it shrinks less important coefficients but keeps them in the model. | Lasso regression can lead to a less complex model by setting some coefficients to zero, reducing the number of effective parameters. |
Interpretability | The results of ridge regression may be less interpretable due to the inclusion of all features, each with a reduced but non-zero coefficient. | Lasso regression can improve interpretability by selecting only the most relevant features, making the model’s predictions more explainable. |
Sparsity | Ridge regression does not yield sparse models since all coefficients remain non-zero. | Lasso regression can produce sparse models by setting some coefficients to exactly zero. |
Sensitivity | More robust and less sensitive to outliers compared to lasso regression. | More sensitive to outliers due to the absolute value in the penalty term. |
The key difference is in how they assign penalties to the coefficients:
Here, LS Obj refers to the ‘least squares objective,’ i.e., the linear regression objective without regularization.
If terms like ‘penalty’ and ‘regularization’ seem very unfamiliar to you, don’t worry; we’ll discuss these in more detail throughout this article. Before digging further into how they work, let’s try to understand why penalizing the magnitude of coefficients should work in the first place.
Let’s try to understand the impact of model complexity on the magnitude of coefficients. As an example, I have simulated a sine curve (between 60° and 300°) and added some random noise using the following code:
#Importing libraries. The same will be used throughout the article.
import numpy as np
import pandas as pd
import random
import matplotlib.pyplot as plt
from matplotlib.pylab import rcParams
rcParams['figure.figsize'] = 12, 10
#Define input array with angles from 60deg to 300deg converted to radians
x = np.array([i*np.pi/180 for i in range(60,300,4)])
np.random.seed(10) #Setting seed for reproducibility
y = np.sin(x) + np.random.normal(0,0.15,len(x))
data = pd.DataFrame(np.column_stack([x,y]),columns=['x','y'])
plt.plot(data['x'],data['y'],'.')
plt.show()
This resembles a sine curve but not exactly because of the noise. We’ll use this as an example to test different scenarios in this article. Let’s try to estimate the sine function using polynomial regression with powers of x from 1 to 15. Let’s add a column for each power upto 15 in our dataframe. This can be accomplished using the following code:
for i in range(2,16): #power of 1 is already there
colname = 'x_%d'%i #new var will be x_power
data[colname] = data['x']**i
print(data.head())
add a column for each power upto 15
The dataframe looks like this:
Now that we have all the 15 powers, let’s make 15 different linear regression models, with each model containing variables with powers of x from 1 to the particular model number. For example, the feature set of model 8 will be – {x, x_2, x_3, …, x_8}.
First, we’ll define a generic function that takes in the required maximum power of x as an input and returns a list containing – [ model RSS, intercept, coef_x, coef_x2, … upto entered power ]. Here RSS refers to the ‘Residual Sum of Squares,’ which is nothing but the sum of squares of errors between the predicted and actual values in the training data set and is known as the cost function or the loss function. The python code defining the function is:
#Import Linear Regression model from scikit-learn.
from sklearn.linear_model import LinearRegression
def linear_regression(data, power, models_to_plot):
#initialize predictors:
predictors=['x']
if power>=2:
predictors.extend(['x_%d'%i for i in range(2,power+1)])
#Fit the model
linreg = LinearRegression(normalize=True)
linreg.fit(data[predictors],data['y'])
y_pred = linreg.predict(data[predictors])
#Check if a plot is to be made for the entered power
if power in models_to_plot:
plt.subplot(models_to_plot[power])
plt.tight_layout()
plt.plot(data['x'],y_pred)
plt.plot(data['x'],data['y'],'.')
plt.title('Plot for power: %d'%power)
#Return the result in pre-defined format
rss = sum((y_pred-data['y'])**2)
ret = [rss]
ret.extend([linreg.intercept_])
ret.extend(linreg.coef_)
return ret
Note that this function will not plot the model fit for all the powers but will return the RSS and coefficient values for all the models. I’ll skip the details of the code for now to maintain brevity. I’ll be happy to discuss the same through the comments below if required.
Now, we can make all 15 models and compare the results. For ease of analysis, we’ll store all the results in a Pandas dataframe and plot 6 models to get an idea of the trend. Consider the following code:
#Initialize a dataframe to store the results:
col = ['rss','intercept'] + ['coef_x_%d'%i for i in range(1,16)]
ind = ['model_pow_%d'%i for i in range(1,16)]
coef_matrix_simple = pd.DataFrame(index=ind, columns=col)
#Define the powers for which a plot is required:
models_to_plot = {1:231,3:232,6:233,9:234,12:235,15:236}
#Iterate through all powers and assimilate results
for i in range(1,16):
coef_matrix_simple.iloc[i-1,0:i+2] = linear_regression(data, power=i, models_to_plot=models_to_plot)
We would expect the models with increasing complexity to better fit the data and result in lower RSS values. This can be verified by looking at the plots generated for 6 models:
This clearly aligns with our initial understanding. As the model complexity increases, the models tend to fit even smaller deviations in the training data set. Though this leads to overfitting, let’s keep this issue aside for some time and come to our main objective, i.e., the impact on the magnitude of coefficients. This can be analyzed by looking at the data frame created above.
#Set the display format to be scientific for ease of analysis
pd.options.display.float_format = '{:,.2g}'.format
coef_matrix_simple
The output looks like this:
It is clearly evident that the size of coefficients increases exponentially with an increase in model complexity. I hope this gives some intuition into why putting a constraint on the magnitude of coefficients can be a good idea to reduce model complexity.
Let’s try to understand this even better.
What does a large coefficient signify? It means that we’re putting a lot of emphasis on that feature, i.e., the particular feature is a good predictor for the outcome. When it becomes too large, the algorithm starts modeling intricate relations to estimate the output and ends up overfitting the particular training data.
I hope the concept is clear. Now, let’s understand ridge and lasso regression in detail and see how well they work for the same problem.
As mentioned before, ridge regression performs ‘L2 regularization‘, i.e., it adds a factor of the sum of squares of coefficients in the optimization objective. Thus, ridge regression optimizes the following:
Objective = RSS + α * (sum of the square of coefficients)
Here, α (alpha) is the parameter that balances the amount of emphasis given to minimizing RSS vs minimizing the sum of squares of coefficients. α can take various values:
I hope this gives some sense of how α would impact the magnitude of coefficients. One thing is for sure – any non-zero value would give values less than that of simple linear regression. By how much? We’ll find out soon. Leaving the mathematical details for later, let’s see ridge regression in action on the same problem as above.
First, let’s define a generic function for ridge regression similar to the one defined for simple linear regression. The Python code is:
from sklearn.linear_model import Ridge
def ridge_regression(data, predictors, alpha, models_to_plot={}):
#Fit the model
ridgereg = Ridge(alpha=alpha,normalize=True)
ridgereg.fit(data[predictors],data['y'])
y_pred = ridgereg.predict(data[predictors])
#Check if a plot is to be made for the entered alpha
if alpha in models_to_plot:
plt.subplot(models_to_plot[alpha])
plt.tight_layout()
plt.plot(data['x'],y_pred)
plt.plot(data['x'],data['y'],'.')
plt.title('Plot for alpha: %.3g'%alpha)
#Return the result in pre-defined format
rss = sum((y_pred-data['y'])**2)
ret = [rss]
ret.extend([ridgereg.intercept_])
ret.extend(ridgereg.coef_)
return ret
Note the ‘Ridge’ function used here. It takes ‘alpha’ as a parameter on initialization. Also, keep in mind that normalizing the inputs is generally a good idea in every type of regression and should be used in the case of ridge regression as well.
Now, let’s analyze the result of Ridge regression for 10 different values of α ranging from 1e-15 to 20. These values have been chosen so that we can easily analyze the trend with changes in values of α. These would, however, differ from case to case.
Note that each of these 10 models will contain all the 15 variables, and only the value of alpha would differ. This differs from the simple linear regression case, where each model had a subset of features.
#Initialize predictors to be set of 15 powers of x
predictors=['x']
predictors.extend(['x_%d'%i for i in range(2,16)])
#Set the different values of alpha to be tested
alpha_ridge = [1e-15, 1e-10, 1e-8, 1e-4, 1e-3,1e-2, 1, 5, 10, 20]
#Initialize the dataframe for storing coefficients.
col = ['rss','intercept'] + ['coef_x_%d'%i for i in range(1,16)]
ind = ['alpha_%.2g'%alpha_ridge[i] for i in range(0,10)]
coef_matrix_ridge = pd.DataFrame(index=ind, columns=col)
models_to_plot = {1e-15:231, 1e-10:232, 1e-4:233, 1e-3:234, 1e-2:235, 5:236}
for i in range(10):
coef_matrix_ridge.iloc[i,] = ridge_regression(data, predictors, alpha_ridge[i], models_to_plot)
This would generate the following plot:
Here we can clearly observe that as the value of alpha increases, the model complexity reduces. Though higher values of alpha reduce overfitting, significantly high values can cause underfitting as well (e.g., alpha = 5). Thus alpha should be chosen wisely. A widely accepted technique is cross-validation, i.e., the value of alpha is iterated over a range of values, and the one giving a higher cross-validation score is chosen.
Let’s have a look at the value of coefficients in the above models:
#Set the display format to be scientific for ease of analysis
pd.options.display.float_format = '{:,.2g}'.format
coef_matrix_ridge
The table looks like:
This straight away gives us the following inferences:
The first 3 are very intuitive. But #4 is also a crucial observation. Let’s reconfirm the same by determining the number of zeros in each row of the coefficients data set:
coef_matrix_ridge.apply(lambda x: sum(x.values==0),axis=1)
This confirms that all 15 coefficients are greater than zero in magnitude (can be +ve or -ve). Remember this observation and have a look again until it’s clear. This will play an important role later while comparing ridge with lasso regression.
LASSO stands for Least Absolute Shrinkage and Selection Operator. I know it doesn’t give much of an idea, but there are 2 keywords here – ‘absolute‘ and ‘selection. ‘
Let’s consider the former first and worry about the latter later.
Lasso regression performs L1 regularization, i.e., it adds a factor of the sum of the absolute value of coefficients in the optimization objective. Thus, lasso regression optimizes the following:
Objective = RSS + α * (sum of the absolute value of coefficients)
Here, α (alpha) works similar to that of the ridge and provides a trade-off between balancing RSS and the magnitude of coefficients. Like that of the ridge, α can take various values. Let’s iterate it here briefly:
Yes, its appearing to be very similar to Ridge till now. But hang on with me, and you’ll know the difference by the time we finish. Like before, let’s run lasso regression on the same problem as above. First, we’ll define a generic function:
from sklearn.linear_model import Lasso
def lasso_regression(data, predictors, alpha, models_to_plot={}):
#Fit the model
lassoreg = Lasso(alpha=alpha,normalize=True, max_iter=1e5)
lassoreg.fit(data[predictors],data['y'])
y_pred = lassoreg.predict(data[predictors])
#Check if a plot is to be made for the entered alpha
if alpha in models_to_plot:
plt.subplot(models_to_plot[alpha])
plt.tight_layout()
plt.plot(data['x'],y_pred)
plt.plot(data['x'],data['y'],'.')
plt.title('Plot for alpha: %.3g'%alpha)
#Return the result in pre-defined format
rss = sum((y_pred-data['y'])**2)
ret = [rss]
ret.extend([lassoreg.intercept_])
ret.extend(lassoreg.coef_)
return ret
Notice the additional parameters defined in the Lasso function – ‘max_iter. ‘ This is the maximum number of iterations for which we want the model to run if it doesn’t converge before. This exists for Ridge as well, but setting this to a higher than default value was required in this case. Why? I’ll come to this in the next section.
Let’s check the output for 10 different values of alpha using the following code:
#Initialize predictors to all 15 powers of x
predictors=['x']
predictors.extend(['x_%d'%i for i in range(2,16)])
#Define the alpha values to test
alpha_lasso = [1e-15, 1e-10, 1e-8, 1e-5,1e-4, 1e-3,1e-2, 1, 5, 10]
#Initialize the dataframe to store coefficients
col = ['rss','intercept'] + ['coef_x_%d'%i for i in range(1,16)]
ind = ['alpha_%.2g'%alpha_lasso[i] for i in range(0,10)]
coef_matrix_lasso = pd.DataFrame(index=ind, columns=col)
#Define the models to plot
models_to_plot = {1e-10:231, 1e-5:232,1e-4:233, 1e-3:234, 1e-2:235, 1:236}
#Iterate over the 10 alpha values:
for i in range(10):
coef_matrix_lasso.iloc[i,] = lasso_regression(data, predictors, alpha_lasso[i], models_to_plot)
This gives us the following plots:
This again tells us that the model complexity decreases with an increase in the values of alpha. But notice the straight line at alpha=1. Appears a bit strange to me. Let’s explore this further by looking at the coefficients:
Apart from the expected inference of higher RSS for higher alphas, we can see the following:
Inferences #1 and 2 might not always generalize but will hold for many cases. The real difference from the ridge is coming out in the last inference. Let’s check the number of coefficients that are zero in each model using the following code:
coef_matrix_lasso.apply(lambda x: sum(x.values==0),axis=1)
We can observe that even for a small value of alpha, a significant number of coefficients are zero. This also explains the horizontal line fit for alpha=1 in the lasso plots; it’s just a baseline model! This phenomenon of most of the coefficients being zero is called ‘sparsity. ‘ Although lasso performs feature selection, this level of sparsity is achieved in special cases only, which we’ll discuss towards the end.
This has some really interesting implications on the use cases of lasso regression as compared to that of ridge regression. But before coming to the final comparison, let’s take a bird’s eye view of the mathematics behind why coefficients are zero in the case of lasso but not ridge.
'''
LINEAR, RIDGE AND LASSO REGRESSION
'''
# importing requuired libraries
import numpy as np
import pandas as pd
from pandas import Series, DataFrame
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Lasso, Ridge
# read test and train file
train = pd.read_csv('train.csv')
test = pd.read_csv('test.csv')
print('\n\n---------DATA---------------\n\n')
print(train.head())
#splitting into training and test
## try building model with the different features and compare the result.
X = train.loc[:,['Outlet_Establishment_Year','Item_MRP']]
x_train, x_cv, y_train, y_cv = train_test_split(X,train.Item_Outlet_Sales,random_state=5)
print('--------Trainig Linear Regression Model---------------')
lreg = LinearRegression()
#training the model
lreg.fit(x_train,y_train)
#predicting on cv
pred = lreg.predict(x_cv)
#calculating mse
mse = np.mean((pred - y_cv)**2)
print('\nMean Sqaured Error = ',mse )
#Let us take a look at the coefficients of this linear regression model.
# calculating coefficients
coeff = DataFrame(x_train.columns)
coeff['Coefficient Estimate'] = Series(lreg.coef_)
print(coeff)
print('\n\nModel performance on Test data = ')
print(lreg.score(x_cv,y_cv))
print('\n\n---------Training Ridge Regression Model----------------')
ridge = Ridge()
ridge.fit(x_train,y_train)
pred1 = ridge.predict(x_cv)
mse_1 = np.mean((pred1-y_cv)**2)
print('\n\nMean Squared Error = ',mse_1)
# calculating coefficients
coeff = DataFrame(x_train.columns)
coeff['Coefficient Estimate'] = Series(ridge.coef_)
print(coeff)
print('\n\nModel performance on Test data = ')
print(ridge.score(x_cv,y_cv))
print('\n\n---------Training Lasso Regression Model----------------')
lasso = Lasso()
lasso.fit(x_train,y_train)
pred2 = lasso.predict(x_cv)
mse_2 = np.mean((pred2-y_cv)**2)
print('\n\nMean Squared Error = ',mse_2)
# calculating coefficients
coeff = DataFrame(x_train.columns)
coeff['Coefficient Estimate'] = Series(lasso.coef_)
print(coeff)
print('\n\nModel performance on Test data = ')
print(lasso.score(x_cv,y_cv))
Here’s a sneak peek into some of the underlying mathematical principles of regression. If you wish to get into the details, I recommend taking a good statistics textbook, like Elements of Statistical Learning.
Let’s start by reviewing the basic structure of data in a regression problem.
In this infographic, you can see there are 4 data elements:
Here, N is the total number of data points available, and M is the total number of features. X has M+1 columns because of M features and 1 intercept.
The predicted outcome for any data point i is:
It is simply the weighted sum of each data point with coefficients as the weights. This prediction is achieved by finding the optimum value of weights based on certain criteria, which depends on the type of regression algorithm being used. Let’s consider all 3 cases:
The objective function (also called the cost) to be minimized is just the RSS (Residual Sum of Squares), i.e., the sum of squared errors of the predicted outcome as compared to the actual outcome. This can be depicted mathematically as:
In order to minimize this cost, we generally use a ‘gradient descent’ algorithm. The overall algorithm works like this:
1. initialize weights (say w=0)
2. iterate till not converged
2.1 iterate over all features (j=0,1...M)
2.1.1 determine the gradient
2.1.2 update the jth weight by subtracting learning rate times the gradient
w(t+1) = w(t) - learning rate * gradient
Here the important step is #2.1.1, where we compute the gradient. A gradient is nothing but a partial differential of the cost with respect to a particular weight (denoted as wj). The gradient for the jth weight will be:
This is formed from 2 parts:
Step #2.1.2 involves updating the weights using the gradient. This updating step for simple linear regression looks like this:
Note the +ve sign in the RHS is formed after the multiplication of 2 -ve signs. I would like to explain point #2 of the gradient descent algorithm mentioned above, ‘iterate till not converged.‘ Here convergence refers to attaining the optimum solution within the pre-defined limit.
It is checked using the value of the gradient. If the gradient is small enough, it means we are very close to the optimum, and further iterations won’t substantially impact the coefficients. The lower limit on the gradient can be changed using the ‘tol‘ parameter.
Let’s consider the case of ridge regression now.
The objective function (also called the cost) to be minimized is the RSS plus the sum of squares of the magnitude of weights. This can be depicted mathematically as:
In this case, the gradient would be:
Again in the regularization part of a gradient, only wj remains, and all others would become zero. The corresponding update rule is:
Here we can see that the second part of the RHS is the same as that of simple linear regression. Thus, ridge regression is equivalent to reducing the weight by a factor of (1-2λη) first and then applying the same update rule as simple linear regression. I hope this explains why the coefficients get reduced to small numbers but never become zero.
Note that the criteria for convergence, in this case, remains similar to simple linear regression, i.e., checking the value of gradients. Let’s discuss Lasso regression now.
The objective function (also called the cost) to be minimized is the RSS plus the sum of the absolute value of the magnitude of weights. This can be depicted mathematically as:
In this case, the gradient is not defined as the absolute function is not differentiable at x=0. This can be illustrated as:
We can see that the parts on the left and right sides of 0 are straight lines with defined derivates, but the function can’t be differentiated at x=0. In this case, we have to use a different technique called coordinate descent, which is based on the concept of sub-gradients. One of the coordinate descent follows the following algorithms (this is also the default in sklearn):
1. initialize weights (say w=0)
2. iterate till not converged
2.1 iterate over all features (j=0,1...M)
2.1.1 update the jth weight with a value which minimizes the cost
#2.1.1 might look too generalized. But I’m intentionally leaving the details and jumping to the update rule:
Demand forecasting is a key component of every growing online business. Without proper demand forecasting processes in place, it can be nearly impossible to have the right amount of stock on hand at any given time. A food delivery service has to deal with a lot of perishable raw materials, which makes it all the more important for such a company to accurately forecast daily and weekly demand.
Too much inventory in the warehouse means more risk of wastage, and not enough could lead to out-of-stocks — and push customers to seek solutions from your competitors. In this challenge, get a taste of the demand forecasting challenge using a real dataset.
Here g(w-j) represents (but not exactly) the difference between the actual outcome and the predicted outcome considering all EXCEPT the jth variable. If this value is small, it means that the algorithm is able to predict the outcome fairly well even without the jth variable, and thus it can be removed from the equation by setting a zero coefficient. This gives us some intuition into why the coefficients become zero in the case of lasso regression.
In coordinate descent, checking convergence is another issue. Since gradients are not defined, we need an alternate method. Many alternatives exist, but the simplest one is to check the step size of the algorithm. We can check the maximum difference in weights in any particular cycle overall feature weights (#2.1 of the algorithm above).
If this is lower than the specified ‘tol,’ the algorithm will stop. The convergence is not as fast as the gradient descent. If a warning appears saying that the algorithm stopped before convergence, we might have to set the ‘max_iter’ parameter. This is why I specified this parameter in the Lasso generic function.
Let’s summarize our understanding by comparing the coefficients in all the three cases using the following visual, which shows how the ridge and lasso coefficients behave in comparison to the simple linear regression case.
Apologies for the lack of visual appeal. But I think it is good enough to re-inforced the following facts:
Before going further, one important issue in the case of both ridge and lasso regression is intercept handling. Generally, regularizing the intercept is not a good idea and should be left out of regularization. This requires slight changes in the implementation, which I’ll leave you to explore.
Now that we have a fair idea of how ridge and lasso regression work, let’s try to consolidate our understanding by comparing them and appreciating their specific use cases. I will also compare them with some alternate approaches. Let’s analyze these under three buckets:
Traditionally, techniques like stepwise regression were used to perform feature selection and make parsimonious models. But with advancements in Machine-Learning, ridge and lasso regressions provide very good alternatives as they give much better output, require fewer tuning parameters, and can be automated to a large extent.
It’s not hard to see why the stepwise selection techniques become practically cumbersome to implement in high-dimensionality cases. Thus, the lasso provides a significant advantage.
This disadvantage of the lasso can be observed in the example we discussed above. Since we used a polynomial regression, the variables were highly correlated. (Not sure why? Check the output of data.corr() ). Thus, we saw that even small values of alpha were giving significant sparsity (i.e., high #coefficients as zero).
Along with Ridge and Lasso, Elastic Net is another useful technique that combines both L1 and L2 regularization. It can be used to balance out the pros and cons of ridge and lasso regression. I encourage you to explore it further.
This study explored the prediction of wine quality using various regression models. We analyzed a dataset with a substantial number of predictors, employing ordinary least squares (OLS) regression as the baseline model. To enhance our predictions and handle multicollinearity, we incorporated the Lasso model, which includes a regularization parameter λ.
Our findings suggested that the Lasso model outperformed OLS in terms of mean squared error (MSE), particularly when dealing with a large number of predictors and categorical variables. The Bayesian approach to model selection, implemented through the use of λ, allowed us to choose the optimal subset of predictors, improving prediction accuracy.
Moreover, we utilized NumPy extensively for data manipulation and model implementation, reflecting its utility in data science and machine learning tasks. Overall, this study underscores the importance of employing advanced regression techniques such as the Lasso model in predicting wine quality, especially when faced with datasets featuring numerous predictors and categorical variables.
A. Ridge and Lasso Regression are regularization techniques in machine learning. Ridge adds L2 regularization, and Lasso adds L1 to linear regression models, preventing overfitting.
A. Use Ridge when you have many correlated predictors and want to avoid multicollinearity. Use Lasso when feature selection is crucial or when you want a sparse model.
A. Ridge Regression adds a penalty term proportional to the square of the coefficients, while Lasso adds a penalty term proportional to the absolute value of the coefficients, which can lead to variable selection.
A. Ridge and Lasso are used to prevent overfitting in regression models by adding regularization terms to the cost function, encouraging simpler models with fewer predictors and more generalizability.
A. Ridge in Python refers to a regularization technique used in linear regression to prevent overfitting by penalizing large coefficient values.
Lorem ipsum dolor sit amet, consectetur adipiscing elit,