# 3.1. Linear Regression¶

To start off, we will introduce the problem of regression. This is the
task of predicting a *real valued target* \(y\) given a data point
\(\mathbf{x}\). Regression problems are common in practice, arising
whenever we want to predict a continuous numerical value. Some examples
of regression problems include predicting house prices, stock prices,
length of stay (for patients in the hospital), tomorrow’s temperature,
demand forecasting (for retail sales), and many more. Note that not
every prediction problem is a regression problem. In subsequent sections
we will discuss classification problems, where our predictions are
discrete categories.

## 3.1.1. Basic Elements of Linear Regression¶

Linear regression, which dates to Gauss and Legendre, is perhaps the
simplest, and by far the most popular approach to solving regression
problems. What makes linear regression *linear* is that we assume that
the output truly can be expressed as a *linear* combination of the input
features.

### 3.1.1.1. Linear Model¶

To keep things simple, we will start with running example in which we consider the problem of estimating the price of a house (e.g. in dollars) based on area (e.g. in square feet) and age (e.g. in years). More formally, the assumption of linearity suggests that our model can be expressed in the following form:

In economics papers, it is common for authors to write out linear models in this format with a gigantic equation that spans multiple lines containing terms for every single feature. For the high-dimensional data that we often address in machine learning, writing out the entire model can be tedious. In these cases, we will find it more convenient to use linear algebra notation. In the case of \(d\) variables, we could express our prediction \(\hat{y}\) as follows:

or alternatively, collecting all features into a single vector \(\mathbf{x}\) and all parameters into a vector \(\mathbf{w}\), we can express our linear model as

Above, the vector \(\mathbf{x}\) corresponds to a single data point.
Commonly, we will want notation to refer to the entire dataset of all
input data points. This matrix, often denoted using a capital letter
\(X\), is called the *design matrix* and contains one row for every
example, and one column for every feature.

Given a collection of data points \(X\) and a vector containing the
corresponding target values \(\mathbf{y}\), the goal of linear
regression is to find the *weight* vector \(w\) and bias term
\(b\) (also called an *offset* or *intercept*) that associates each
data point \(\mathbf{x}_i\) with an approximation \(\hat{y}_i\)
of its corresponding label \(y_i\).

Expressed in terms of a single data point, this gives us the expression same as (3.1.3).

Finally, for a collection of data points \(\mathbf{X}\), the predictions \(\hat{\mathbf{y}}\) can be expressed via the matrix-vector product:

Even if we believe that the best model to relate \(\mathbf{x}\) and \(y\) is linear, it’s unlikely that we’d find data where \(y\) lines up exactly as a linear function of \(\mathbf{x}\). For example, both the target values \(y\) and the features \(X\) might be subject to some amount of measurement error. Thus even when we believe that the linearity assumption holds, we will typically incorporate a noise term to account for such errors.

Before we can go about solving for the best setting of the parameters \(w\) and \(b\), we will need two more things: (i) some way to measure the quality of the current model and (ii) some way to manipulate the model to improve its quality.

### 3.1.1.2. Training Data¶

The first thing that we need is training data. Sticking with our running
example, we’ll need some collection of examples for which we know both
the actual selling price of each house as well as their corresponding
area and age. Our goal is to identify model parameters that minimize the
error between the predicted price and the real price. In the terminology
of machine learning, the data set is called a *training data* or
*training set*, a house (often a house and its price) here comprises one
*sample*, and its actual selling price is called a *label*. The two
factors used to predict the label are called *features* or *covariates*.

Typically, we will use \(n\) to denote the number of samples in our dataset. We index the samples by \(i\), denoting each input data point as \(x^{(i)} = [x_1^{(i)}, x_2^{(i)}]\) and the corresponding label as \(y^{(i)}\).

### 3.1.1.3. Loss Function¶

In model training, we need to measure the error between the predicted value and the real value of the price. Usually, we will choose a non-negative number as the error. The smaller the value, the smaller the error. A common choice is the square function. For given parameters \(\mathbf{w}\) and \(b\), we can express the error of our prediction on a given a sample as follows:

The constant \(1/2\) is just for mathematical convenience, ensuring that after we take the derivative of the loss, the constant coefficient will be \(1\). The smaller the error, the closer the predicted price is to the actual price, and when the two are equal, the error will be zero.

Since the training dataset is given to us, and thus out of our control,
the error is only a function of the model parameters. In machine
learning, we call the function that measures the error the *loss
function*. The squared error function used here is commonly referred to
as *square loss*.

To make things a bit more concrete, consider the example below where we plot a regression problem for a one-dimensional case, e.g. for a model where house prices depend only on area.

As you can see, large differences between estimates \(\hat{y}^{(i)}\) and observations \(y^{(i)}\) lead to even larger contributions in terms of the loss, due to the quadratic dependence. To measure the quality of a model on the entire dataset, we can simply average the losses on the training set.

When training the model, we want to find parameters (\(\mathbf{w}^*, b^*\)) that minimize the average loss across all training samples:

### 3.1.1.4. Analytic Solution¶

Linear regression happens to be an unusually simple optimization problem. Unlike nearly every other model that we will encounter in this book, linear regression can be solved easily with a simple formula, yielding a global optimum. To start we can subsume the bias \(b\) into the parameter \(\mathbf{w}\) by appending a column to the design matrix consisting of all \(1s\). Then our prediction problem is to minimize \(||\mathbf{y} - X\mathbf{w}||\). Because this expression has a quadratic form it is clearly convex, and so long as the problem is not degenerate (our features are linearly independent), it is strictly convex.

Thus there is just one global critical point on the loss surface corresponding to the global minimum. Taking the derivative of the loss with respect to \(\mathbf{w}\) and setting it equal to 0 gives the analytic solution:

While simple problems like linear regression may admit analytic solutions, you should not get used to such good fortune. Although analytic solutions allow for nice mathematical analysis, the requirement of an analytic solution confines one to an restrictive set of models that would exclude all of deep learning.

### 3.1.1.5. Gradient descent¶

Even in cases where we cannot solve the models analytically, and even when the loss surfaces are high-dimensional and nonconvex, it turns out that we can still make progress. Moreover, when those difficult-to-optimize models are sufficiently superior for the task at hand, figuring out how to train them is well worth the trouble.

The key trick behind nearly all of deep learning and that we will repeatedly throughout this book is to reduce the error gradually by iteratively updating the parameters, each step moving the parameters in the direction that incrementally lowers the loss function. This algorithm is called gradient descent. On convex loss surfaces it will eventually converge to a global minimum, and while the same can’t be said for nonconvex surfaces, it will at least lead towards a (hopefully good) local minimum.

The most naive application of gradient descent consists of taking the
derivative of the true loss, which is an average of the losses computed
on every single example in the dataset. In practice, this can be
extremely slow. We must pass over the entire dataset before making a
single update. Thus, we’ll often settle for sampling a random mini-batch
of examples every time we need to computer the update, a variant called
*stochastic gradient descent*.

In each iteration, we first randomly and uniformly sample a mini-batch \(\mathcal{B}\) consisting of a fixed number of training data examples. We then compute the derivative (gradient) of the average loss on the mini batch with regard to the model parameters. Finally, the product of this result and a predetermined step size \(\eta > 0\) are used to update the parameters in the direction that lowers the loss.

We can express the update mathematically as follows (\(\partial\) denotes the partial derivative):

To summarize, steps of the algorithm are the following: (i) we initialize the values of the model parameters, typically at random; (ii) we iterate over the data many times, updating the parameters in each by moving the parameters in the direction of the negative gradient, as calculated on a random minibatch of data.

For quadratic losses and linear functions we can write this out explicitly as follows. Note that \(\mathbf{w}\) and \(\mathbf{x}\) are vectors. Here the more elegant vector notation makes the math much more readable than expressing things in terms of coefficients, say \(w_1, w_2, \ldots w_d\).

In the above equation \(|\mathcal{B}|\) represents the number of
samples (batch size) in each mini-batch, \(\eta\) is referred to as
*learning rate* and takes a positive number. It should be emphasized
that the values of the batch size and learning rate are set somewhat
manually and are typically not learned through model training.
Therefore, they are referred to as *hyper-parameters*. What we usually
call *tuning hyper-parameters* refers to the adjustment of these terms.
In the worst case this is performed through repeated trial and error
until the appropriate hyper-parameters are found. A better approach is
to learn these as parts of model training. This is an advanced topic and
we do not cover them here for the sake of simplicity.

### 3.1.1.6. Model Prediction¶

After completing the training process, we record the estimated model
parameters, denoted \(\hat{\mathbf{w}}, \hat{b}\) (in general the
“hat” symbol denotes estimates). Note that the parameters that we learn
via gradient descent are not exactly equal to the true minimizers of the
loss on the training set, that’s because gradient descent converges
slowly to a local minimum but does not achieve it exactly. Moreover if
the problem has multiple local minimum, we may not necessarily achieve
the lowest minimum. Fortunately, for deep neural networks, finding
parameters that minimize the loss *on training data* is seldom a
significant problem. The more formidable task is to find parameters that
will achieve low loss on data that we have not seen before, a challenge
called *generalization*. We return to these topics throughout the book.

Given the learned linear regression model \(\hat{\mathbf{w}}^\top x + \hat{b}\), we can now estimate the price of any house outside the training data set with area (square feet) as \(x_1\) and house age (year) as \(x_2\). Here, estimation also referred to as ‘model prediction’ or ‘model inference’.

Note that calling this step *inference* is a misnomer, but has become
standard jargon in deep learning. In statistics, inference means
estimating parameters and outcomes based on other data. This misuse of
terminology in deep learning can be a source of confusion when talking
to statisticians.

## 3.1.2. From Linear Regression to Deep Networks¶

So far we only talked about linear functions. While neural networks cover a much richer family of models, we can begin thinking of the linear model as a neural network by expressing it the language of neural networks. To begin, let’s start by rewriting things in a ‘layer’ notation.

### 3.1.2.1. Neural Network Diagram¶

Commonly, deep learning practitioners represent models visually using neural network diagrams. In Fig. 3.1.2, we represent linear regression with a neural network diagram. The diagram shows the connectivity among the inputs and output, but does not depict the weights or biases (which are given implicitly).

In the above network, the inputs are \(x_1, x_2, \ldots x_d\).
Sometimes the number of inputs are referred to as the feature dimension.
For linear regression models, we act upon \(d\) inputs and output
\(1\) value. Because there is just a single computed neuron (node)
in the graph, we can think of linear models as neural networks
consisting of just a single neuron. Since all inputs are connected to
all outputs (in this case it’s just one), this layer can also be
regarded as an instance of a *fully-connected layer*, also commonly
called a *dense layer*.

### 3.1.2.2. Biology¶

Neural networks derive their name from their inspirations in
neuroscience. Although linear regression predates computation
neuroscience, many of the models we subsequently discuss truly owe to
neural inspiration. To understand the neural inspiration for artificial
neural networks it is worth while considering the basic structure of a
neuron. For the purpose of the analogy it is sufficient to consider the
*dendrites* (input terminals), the *nucleus* (CPU), the *axon* (output
wire), and the *axon terminals* (output terminals) which connect to
other neurons via *synapses*.

Information \(x_i\) arriving from other neurons (or environmental
sensors such as the retina) is received in the dendrites. In particular,
that information is weighted by *synaptic weights* \(w_i\) which
determine how to respond to the inputs (e.g. activation or inhibition
via \(x_i w_i\)). All this is aggregated in the nucleus
\(y = \sum_i x_i w_i + b\), and this information is then sent for
further processing in the axon \(y\), typically after some nonlinear
processing via \(\sigma(y)\). From there it either reaches its
destination (e.g. a muscle) or is fed into another neuron via its
dendrites.

Brain *structures* vary significantly. Some look (to us) rather
arbitrary whereas others have a regular structure. For example, the
visual system of many insects is consistent across members of a species.
The analysis of such structures has often inspired neuroscientists to
propose new architectures, and in some cases, this has been successful.
However, much research in artificial neural networks has little to do
with any direct inspiration in neuroscience, just as although airplanes
are *inspired* by birds, the study of orninthology hasn’t been the
primary driver of aeronautics innovaton in the last century. Equal
amounts of inspiration these days comes from mathematics, statistics,
and computer science.

### 3.1.2.3. Vectorization for Speed¶

In model training or prediction, we often use vector calculations and process multiple observations at the same time. To illustrate why this matters, consider two methods of adding vectors. We begin by creating two 100000 dimensional ones first.

```
%matplotlib inline
import d2l
import numpy as np
import math
from mxnet import nd
import time
n = 100000
a = np.ones(n)
b = np.ones(n)
```

Since we will benchmark the running time frequently in this book, let’s define a timer to do simply analysis of the running time.

```
# Save to the d2l package.
class Timer(object):
"""Record multiple running times."""
def __init__(self):
self.times = []
self.start()
def start(self):
"""Start the timer"""
self.start_time = time.time()
def stop(self):
"""Stop the timer and record the time in a list"""
self.times.append(time.time() - self.start_time)
return self.times[-1]
def avg(self):
"""Return the average time"""
return sum(self.times)/len(self.times)
def sum(self):
"""Return the sum of time"""
return sum(self.times)
def cumsum(self):
"""Return the accumuated times"""
return np.array(self.times).cumsum().tolist()
```

Now we can benchmark the workloads. One way to add vectors is to add them one coordinate at a time using a for loop.

```
timer = Timer()
c = np.zeros(n)
for i in range(n):
c[i] = a[i] + b[i]
'%.5f sec' % timer.stop()
```

```
'0.05606 sec'
```

Another way to add vectors is to add the vectors directly:

```
timer.start()
d = a + b
'%.5f sec' % timer.stop()
```

```
'0.00058 sec'
```

Obviously, the latter is vastly faster than the former. Vectorizing code is a good way of getting order of magnitude speedups. Likewise, as we saw above, it also greatly simplifies the mathematics and with it, it reduces the potential for errors in the notation.

## 3.1.3. The Normal Distribution and Squared Loss¶

The following is optional and can be skipped but it will greatly help with understanding some of the design choices in building deep learning models. As we saw above, using the squared loss \(l(y, \hat{y}) = \frac{1}{2} (y - \hat{y})^2\) has many nice properties, such as having a particularly simple derivative \(\partial_{\hat{y}} l(y, \hat{y}) = (\hat{y} - y)\). That is, the gradient is given by the difference between estimate and observation. You might reasonably point out that linear regression is a classical statistical model. Legendre first developed the method of least squares regression in 1805, which was shortly thereafter rediscovered by Gauss in 1809. To understand this a bit better, recall the normal distribution with mean \(\mu\) and variance \(\sigma^2\).

Let’s define the function to compute the normal distribution.

```
x = np.arange(-7, 7, 0.01)
def normal(x, mu, sigma):
p = 1 / math.sqrt(2 * math.pi * sigma**2)
return p * np.exp(- 0.5 / sigma**2 * (x - mu)**2)
```

For a similar reason to create a `Timer`

class, we define a `plot`

function to draw multiple lines and set the figure properly, since we
will visualize lines frequently later.

```
# Save to the d2l package.
def plot(X, Y=None, xlabel=None, ylabel=None, legend=[], xlim=None,
ylim=None, xscale='linear', yscale='linear', fmts=None,
figsize=(3.5, 2.5), axes=None):
"""Plot multiple lines"""
d2l.set_figsize(figsize)
axes = axes if axes else d2l.plt.gca()
if isinstance(X, nd.NDArray): X = X.asnumpy()
if isinstance(Y, nd.NDArray): Y = Y.asnumpy()
if not hasattr(X[0], "__len__"): X = [X]
if Y is None: X, Y = [[]]*len(X), X
if not hasattr(Y[0], "__len__"): Y = [Y]
if len(X) != len(Y): X = X * len(Y)
if not fmts: fmts = ['-']*len(X)
axes.cla()
for x, y, fmt in zip(X, Y, fmts):
if isinstance(x, nd.NDArray): x = x.asnumpy()
if isinstance(y, nd.NDArray): y = y.asnumpy()
if len(x):
axes.plot(x, y, fmt)
else:
axes.plot(y, fmt)
set_axes(axes, xlabel, ylabel, xlim, ylim, xscale, yscale, legend)
# Save to the d2l package.
def set_axes(axes, xlabel, ylabel, xlim, ylim, xscale, yscale, legend):
"""A utility function to set matplotlib axes"""
axes.set_xlabel(xlabel)
axes.set_ylabel(ylabel)
axes.set_xscale(xscale)
axes.set_yscale(yscale)
axes.set_xlim(xlim)
axes.set_ylim(ylim)
if legend: axes.legend(legend)
axes.grid()
```

Now visualize the normal distributions.

```
# Mean and variance pairs
parameters = [(0,1), (0,2), (3,1)]
plot(x, [normal(x, mu, sigma) for mu, sigma in parameters],
xlabel='x', ylabel='p(x)', figsize=(4.5, 2.5),
legend = ['mean %d, var %d'%(mu, sigma) for mu, sigma in parameters])
```

As can be seen in the figure above, changing the mean shifts the function, increasing the variance makes it more spread-out with a lower peak. The key assumption in linear regression with least mean squares loss is that the observations actually arise from noisy observations, where noise is added to the data, e.g. as part of the observations process.

This allows us to write out the *likelihood* of seeing a particular
\(y\) for a given \(\mathbf{x}\) via

A good way of finding the most likely values of \(b\) and
\(\mathbf{w}\) is to maximize the *likelihood* of the entire dataset

The notion of maximizing the likelihood of the data subject to the
parameters is well known as the *Maximum Likelihood Principle* and its
estimators are usually called *Maximum Likelihood Estimators* (MLE).
Unfortunately, maximizing the product of many exponential functions is
pretty awkward, both in terms of implementation and in terms of writing
it out on paper. Instead, a much better way is to minimize the *Negative
Log-Likelihood* \(-\log p(\mathbf y|\mathbf X)\). In the above case
this works out to be

A closer inspection reveals that for the purpose of minimizing \(-\log p(\mathbf y|\mathbf X)\) we can skip the first term since it doesn’t depend on \(\mathbf{w}, b\) or even the data. The second term is identical to the objective we initially introduced, but for the multiplicative constant \(\frac{1}{\sigma^2}\). Again, this can be skipped if we just want to get the most likely solution. It follows that maximum likelihood in a linear model with additive Gaussian noise is equivalent to linear regression with squared loss.

## 3.1.4. Summary¶

Key ingredients in a machine learning model are training data, a loss function, an optimization algorithm, and quite obviously, the model itself.

Vectorizing makes everything better (mostly math) and faster (mostly code).

Minimizing an objective function and performing maximum likelihood can mean the same thing.

Linear models are neural networks, too.

## 3.1.5. Exercises¶

Assume that we have some data \(x_1, \ldots x_n \in \mathbb{R}\). Our goal is to find a constant \(b\) such that \(\sum_i (x_i - b)^2\) is minimized.

Find the optimal closed form solution.

What does this mean in terms of the Normal distribution?

Assume that we want to solve the optimization problem for linear regression with quadratic loss explicitly in closed form. To keep things simple, you can omit the bias \(b\) from the problem.

Rewrite the problem in matrix and vector notation (hint - treat all the data as a single matrix).

Compute the gradient of the optimization problem with respect to \(w\).

Find the closed form solution by solving a matrix equation.

When might this be better than using stochastic gradient descent (i.e. the incremental optimization approach that we discussed above)? When will this break (hint - what happens for high-dimensional \(x\), what if many observations are very similar)?.

Assume that the noise model governing the additive noise \(\epsilon\) is the exponential distribution. That is, \(p(\epsilon) = \frac{1}{2} \exp(-|\epsilon|)\).

Write out the negative log-likelihood of the data under the model \(-\log p(Y|X)\).

Can you find a closed form solution?

Suggest a stochastic gradient descent algorithm to solve this problem. What could possibly go wrong (hint - what happens near the stationary point as we keep on updating the parameters). Can you fix this?