A Tutorial on Ridge and Lasso Regression in Python

Overview

• Ridge and Lasso Regression are types of Regularization techniques
• Regularization techniques are used to deal with overfitting and when the dataset is large
• Ridge and Lasso Regression involve adding penalties to the regression function

Brief Overview

Ridge and Lasso regression are powerful techniques generally used for creating parsimonious models in presence of a ‘large’ number of features. Here ‘large’ can typically mean either of two things:

1. Large enough to enhance the tendency of a model to overfit (as low as 10 variables might cause overfitting)
2. Large enough to cause computational challenges. With modern systems, this situation might arise in case of millions or billions of features

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 along with minimizing the error between predicted and actual observations. These are called ‘regularization’ techniques. The key difference is in how they assign penalty to the coefficients:

Ridge Regression:

• Performs L2 regularization, i.e. adds penalty equivalent to square of the magnitude of coefficients
• Minimization objective = LS Obj + α * (sum of square of coefficients)

Lasso Regression:

• Performs L1 regularization, i.e. adds penalty equivalent to absolute value of the magnitude of coefficients
• Minimization objective = LS Obj + α * (sum of absolute value of coefficients

Note that here ‘LS Obj’ refers to ‘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 talk about these in more detail through the course of this article. Before digging further into how they work, lets try to get some intuition into why penalizing the magnitude of coefficients should work in the first place.

Why Penalize the Magnitude of Coefficients?

Lets try to understand the impact of model complexity on the magnitude of coefficients. As an example, I have simulated a sine curve (between 10° and 360°) and added some random noise using the following code:

The input-output looks like:

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:

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}.

Now Split the data to train and test :

Linear Regression :

Using one Features:

Hence, the plot by using one features :

Using three Features:

Hence, the plot by using three features :

First, we’ll define a generic function which 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 ‘Residual Sum of Squares’ which is nothing but the sum of square of errors between the predicted and actual values in the training data set. The python code defining the function is:

Note that this function will not plot the model fit for all the powers but will return the RSS and coefficients 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 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:

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 tends to fit even smaller deviations in the training data set. Though this leads to overfitting, lets 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 analysed by looking at the data frame created above.

It is clearly evident that the size of coefficients increase exponentially with 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.

Train / Test Plot :

Lets 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 modelling intricate relations to estimate the output and ends up overfitting to the particular training data.

I hope the concept is clear. I’ll be happy to discuss further in comments if needed. Now, lets understand ridge and lasso regression in detail and see how well they work for the same problem.

Ridge Regression

As mentioned before, ridge regression performs ‘L2 regularization‘, i.e. it adds a factor of sum of squares of coefficients in the optimization objective. Thus, ridge regression optimizes the following:

Objective = RSS + α * (sum of square of coefficients)

Here, α (alpha) is the parameter which balances the amount of emphasis given to minimizing RSS vs minimizing sum of square of coefficients. α can take various values:

1. α = 0:

• The objective becomes same as simple linear regression.
• We’ll get the same coefficients as simple linear regression

1. α = ∞:

• The coefficients will be zero. Why? Because of infinite weightage on square of coefficients, anything less than zero will make the objective infinite.

1. 0 < α < ∞:

• The magnitude of α will decide the weightage given to different parts of objective.
• The coefficients will be somewhere between 0 and ones for simple linear regression.

I hope this gives some sense on how α would impact the magnitude of coefficients. One thing is for sure that 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, lets see ridge regression in action on the same problem as above.

First, lets define a generic function for ridge regression similar to the one defined for simple linear regression. The Python code is:

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 case of ridge regression as well.

Now, lets 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 change 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 is different from the simple linear regression case where each model had a subset of features.

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 (eg. alpha = 5). Thus alpha should be chosen wisely. A widely accept technique is cross-validation, i.e. the value of alpha is iterated over a range of values and the one giving higher cross-validation score is chosen.

Lets have a look at the value of coefficients in the above models:

This straight away gives us the following inferences:

1. The RSS increases with increase in alpha, this model complexity reduces
2. An alpha as small as 1e-15 gives us significant reduction in magnitude of coefficients. How? Compare the coefficients in the first row of this table to the last row of simple linear regression table.
3. High alpha values can lead to significant underfitting. Note the rapid increase in RSS for values of alpha greater than 1
4. Though the coefficients are very very small, they are NOT zero.

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:

This confirms that all the 15 coefficients are greater than zero in magnitude (can be +ve or -ve). Remember this observation and have a look again until its clear. This will play an important role in later while comparing ridge with lasso regression.

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 key words here — ‘absolute‘ and ‘selection‘.

Lets consider the former first and worry about the latter later.

Lasso regression performs L1 regularization, i.e. it adds a factor of sum of absolute value of coefficients in the optimization objective. Thus, lasso regression optimizes the following:

Objective = RSS + α * (sum of absolute value of coefficients)

Here, α (alpha) works similar to that of ridge and provides a trade-off between balancing RSS and magnitude of coefficients. Like that of ridge, α can take various values. Lets iterate it here briefly:

1. α = 0: Same coefficients as simple linear regression
2. α = ∞: All coefficients zero (same logic as before)
3. 0 < α < ∞: coefficients between 0 and that of simple linear regression

Yes its appearing to be very similar to Ridge till now. But just hang on with me and you’ll know the difference by the time we finish. Like before, lets run lasso regression on the same problem as above. First we’ll define a generic function:

Notice the additional parameters defined in 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 as well but setting this to a higher than default value was required in this case. Why? I’ll come to this in next section, just keep it in the back of the envelope.

This again tells us that the model complexity decreases with 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:

1. For the same values of alpha, the coefficients of lasso regression are much smaller as compared to that of ridge regression (compare row 1 of the 2 tables).
2. For the same alpha, lasso has higher RSS (poorer fit) as compared to ridge regression
3. Many of the coefficients are zero even for very small values of alpha

Inferences #1,2 might not generalize always but will hold for many cases. The real difference from ridge is coming out in the last inference. Lets check the number of coefficients which are zero in each model using following code:

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, its just a baseline model! This phenomenon of most of the coefficients being zero is called ‘sparsity‘.

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, lets take a bird’s eye view of the mathematics behind why coefficients are zero in case of lasso but not ridge.

Conclusion

Now that we have a fair idea of how ridge and lasso regression work, lets try to consolidate our understanding by comparing them and try to appreciate their specific use cases. I will also compare them with some alternate approaches. Lets analyze these under three buckets:

1. Key Difference

• Ridge: It includes all (or none) of the features in the model. Thus, the major advantage of ridge regression is coefficient shrinkage and reducing model complexity.
• Lasso: Along with shrinking coefficients, lasso performs feature selection as well. (Remember the ‘selection‘ in the lasso full-form?) As we observed earlier, some of the coefficients become exactly zero, which is equivalent to the particular feature being excluded from the model.

Traditionally, techniques like stepwise regression were used to perform feature selection and make parsimonious models. But with advancements in Machine Learning, ridge and lasso regression provide very good alternatives as they give much better output, require fewer tuning parameters and can be automated to a large extend.

2. Typical Use Cases

• Ridge: It is majorly used to prevent overfitting. Since it includes all the features, it is not very useful in case of exorbitantly high #features, say in millions, as it will pose computational challenges.
• Lasso: Since it provides sparse solutions, it is generally the model of choice (or some variant of this concept) for modelling cases where the #features are in millions or more. In such a case, getting a sparse solution is of great computational advantage as the features with zero coefficients can simply be ignored.

Its not hard to see why the stepwise selection techniques become practically very cumbersome to implement in high dimensionality cases. Thus, lasso provides a significant advantage.

3. Presence of Highly Correlated Features

• Ridge: It generally works well even in presence of highly correlated features as it will include all of them in the model but the coefficients will be distributed among them depending on the correlation.
• Lasso: It arbitrarily selects any one feature among the highly correlated ones and reduced the coefficients of the rest to zero. Also, the chosen variable changes randomly with change in model parameters. This generally doesn’t work that well as compared to ridge regression.

This disadvantage of 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 techniques which 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.