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1 A quick review of linear regression

Terence Parr

(Terence is a tech lead at Google and ex-Professor of computer/data science in University of San Francisco's MS in Data Science program. You might know Terence as the creator of the ANTLR parser generator.)


1My plan is to keep mathematics notation to a minimum in this article though. Also, regularization for linear and logistic regression is done through the same penalty term in the loss function and so I will focus on just linear regression in this article.

I'm assuming that readers are more or less familiar with the mathematics of linear models and how we find optimal model coefficients to fit lines, but let's take a minute to review the important equations so we're all on the same page.1

Figure .1. Best fit line through 10 sample data points with ordinary least squares regression.

A single-variable linear regression model is familiar to us from high school algebra: , where (coefficient) is the y-intercept and is the slope of the line. For example, let's say we have the following 10 training records and want to draw the best fit line through the points, as shown in Figure .1. (The code to generate all images is available.)


The best fit line is , where we use to indicate it is an approximation of the true underlying relationship between x and y. Using Python and sklearn, it's trivial to fit a linear model to this data:

lm = LinearRegression() # Create a linear model, y) # Fit to x,y training data

and get those optimal coefficients:

optimal_beta0 = lm.intercept_ optimal_beta1 = lm.coef_[0] y_pred = lm.predict(x) # What does model predict for x? (orange line)

optimal_beta0 = -0.170, optimal_beta1 = 1.022 y_pred [-0.17 0.97 2.1 3.24 4.37 5.51 6.64 7.78 8.91 10.05]

Figure .2. Loss function in 3D where the black dot shows the smallest loss, the bottom of the bowl.

The notion of best fit means choosing and to minimize the average error, the average difference between the known true values and the model predictions, . To make things nice mathematically, and to avoid needing an absolute value operator, linear regression optimizes the average (mean) squared error. That's where the term Ordinary Least Squares (OLS) comes from. The MSE function is a quadratic that always gives us a bowl shaped loss function, for 2 coefficients anyway, as shown in Figure .2. For all n training records , we find to minimize:

(If you're a numpy junkie, that is just np.mean(y - m.predict(x)) for vectors y and x.) Plugging the model, our line equation, into that MSE we get:

Figure .3. Loss function contour projected onto 2D plane, as if we were looking from above.

The loss function goes up as and move away from the bottom of the bowl. The big black dot represents the minimum loss location, = (-0.17, 1.022). (See code/ for the code.) Three-dimensional plots are sometimes hard to interpret on a two-dimensional screen, so you will often see the loss function projected down onto the , plane, as shown in Figure .3 (code/

It's important to see the relationship between Figure .1 and Figure .2. So, just to be clear, shifting and in Figure .2 causes the orange line in Figure .1 to tilt or move up and down, away from the best fit.

1.1 Motivation for regularization

So far so good. Given some data, we can fit a best fit line through the data where “best fit” means the line that minimizes the average squared between true y values and those predicted by the model. Now, let's tweak the last y value to be about 10 times as big:

y.iloc[-1] = 100 # Make last y be an outlier 10x as big lm = LinearRegression(), y) y_pred = lm.predict(x) # Get orange line points again

optimal_beta0 = -13.150, optimal_beta1 = 5.402 y_pred [-13.15 -7.15 -1.14 4.86 10.86 16.86 22.87 28.87 34.87 40.87]

Figure 1.4. .

Look what happens to the best (orange) fit line, as shown in Figure 1.4! It has tilted substantially upwards towards the outlier. Because the loss function squares the error, an outlier can seriously distort the shape of the “bowl” and, hence, the minimum location of the optimal and coefficients. Instead of and , the coefficients are and . All real data is noisy and sometimes outliers are common, which provides us with the motivation to regularize our linear models.

Let's try Ridge regularization. Using sklearn again, we can fit a new line through the data using Ridge regression:

lm = Ridge(alpha=300), y) y_pred = lm.predict(x)

optimal_beta0 = 7.015, optimal_beta1 = 1.369 y_pred [ 7.02 8.54 10.06 11.58 13.1 14.62 16.14 17.67 19.19 20.71]

Figure 1.5. .

The alpha=300 hyper parameter controls how much regularization we need, in this case a lot. (For those using TensorFlow's keras interface, you might use something like activity_regularizer=regularizers.l2(300) in one of your layers.) While sklearn uses alpha, we will use as the regularization hyper parameter as we get into the regularization penalty term of the loss function. Notice that the regularized slope, , is very close to the unregularized without the outlier. With regularization, the orange fitted line is held back to an appropriate angle, as shown in Figure 1.5. Using Lasso regularization, Lasso(alpha=45).fit(x, y), we'd get similar results.

The price we pay for keeping the angle sane, is a less accurate (biased) overall result than we saw for the unregularized model for non-outlier data. The regularized y-intercept is larger, , compared to unregularized for the data without the outlier. You can see that the orange line rests above the majority of the data points instead of going through them. The outlier is still pulling the best fit line upward a little bit.

1.2 The premise and trade-off of regularization

Figure 1.6. OLS regression coefficients; data was normalized, dummy-encoded categorical variables.

Figure 1.7. Ridge regression coefficients; data was normalized, dummy-encoded categorical variables. 5-fold cross validation grid search used to identify best alpha value.

We've motivated the need for regularization by showing how even a single outlier can seriously skew our model, but we still have no idea how regularization works. Let's look at a real but small data set called Ames housing price data (ames.csv) because it will point in the direction of the solution. Figure 1.6 shows a bar chart with one bar per coefficient using unregularized linear regression (with normalized explanatory variables and dummy-encoded categorical variables). Wow. Those are some big coefficients and, in fact, I had to clip them to slope magnitudes less than 1e8! Contrast that with the Ridge-regularized coefficients in Figure 1.7, which are in a much more reasonable range. The accuracy of the unregularized model is ridiculously bad, with an error of dollars on a 20% validation set. Using Ridge regression, however, the error is only about $18k per house. With an average house price of about $180k, that's only 10% error on the same validation set. (If you're an R^2 fan, the regularized validation R^2 is 0.84.)

That gives us the clue we need to arrive at the premise of regularization: extreme coefficients are unlikely to yield models that generalize well. The solution, therefore, is simply to constrain the magnitude of linear model coefficients so they don't get too big. Constraining the coefficients means not allowing them to reach their optimal position, at the minimum loss location. That means we pay a price for improved generality in the form of decreased accuracy (increase in bias). Recall what we observed in Figure 1.5 where the orange line sat a bit above the majority of the data. This is a worthwhile trade because, as we can see from this example, unregulated models on real data sets don't generalize well (they have terrible accuracy on validation sets).