Prevously, we learned about supervised learning, derived our first algorithm, and used it to predict diabetes risk.

In this lecture, we are going to dive deeper into why supevised learning really works.

First, let's look at the data, and define where it comes from.

Later, this will be useful to precisely define when supervised learning is guaranteed to work.

At a high level, a supervised machine learning problem has the following structure:

$$ \underbrace{\text{Training Dataset}}_\text{Attributes + Features} + \underbrace{\text{Learning Algorithm}}_\text{Model Class + Objective + Optimizer } \to \text{Predictive Model} $$Where does the dataset come from?

We will assume that the dataset is sampled from a probability distribution $\mathbb{P}$, which we will call the *data distribution*. We will denote this as
$$x, y \sim \mathbb{P}.$$

The training set $\mathcal{D} = \{(x^{(i)}, y^{(i)}) \mid i = 1,2,...,n\}$ consists of *independent and identicaly distributed* (IID) samples from $\mathbb{P}$.

The key assumption in that the training examples are *independent and identicaly distributed* (IID).

- Each training example is from the same distribution.
- This distribution doesn't depend on previous training examples.

**Example**: Flipping a coin. Each flip has same probability of heads & tails and doesn't depend on previous flips.

**Counter-Example**: Yearly census data. The population in each year will be close to that of the previous year.

Let's implement an example of a data distribution in numpy.

In [1]:

```
import numpy as np
np.random.seed(0)
def true_fn(X):
return np.cos(1.5 * np.pi * X)
```

Let's visualize it.

In [2]:

```
import matplotlib.pyplot as plt
plt.rcParams['figure.figsize'] = [12, 4]
X_test = np.linspace(0, 1, 100)
plt.plot(X_test, true_fn(X_test), label="True function")
plt.legend()
```

Out[2]:

<matplotlib.legend.Legend at 0x120e92668>

Let's now draw samples from the distribution. We will generate random $x$, and then generate random $y$ using $$ y = f(x) + \epsilon $$ for a random noise variable $\epsilon$.

In [3]:

```
n_samples = 30
X = np.sort(np.random.rand(n_samples))
y = true_fn(X) + np.random.randn(n_samples) * 0.1
```

We can visualize the samples.

In [4]:

```
plt.plot(X_test, true_fn(X_test), label="True function")
plt.scatter(X, y, edgecolor='b', s=20, label="Samples")
plt.legend()
```

Out[4]:

<matplotlib.legend.Legend at 0x12111c860>

Why assume that the dataset is sampled from a distribution?

- There is inherent uncertainty in the data. The data may consist of noisy measurements (readings from an imperfect thermometer).

- There is uncertainty in the process we model. If $y$ is a stock price, there is randomness in the market that cannot be modeled.

- We can use probability and statistics to analyze supervised learning algorithms and prove that they work.

We made the assumption that the training dataset is sampled from a data distribution.

Let's now use it to gain intuition about why supervised learning works.

We will assume that the dataset is sampled from a probability distribution $\mathbb{P}$, which we will call the *data distribution*. We will denote this as
$$x, y \sim \mathbb{P}.$$

The training set $\mathcal{D} = \{(x^{(i)}, y^{(i)}) \mid i = 1,2,...,n\}$ consists of *independent and identicaly distributed* (IID) samples from $\mathbb{P}$.

We'll say that a model is a function $$ f : \mathcal{X} \to \mathcal{Y} $$ that maps inputs $x \in \mathcal{X}$ to targets $y \in \mathcal{Y}$.

A good predictive model is one that makes **accurate predictions** on **new data** that it has not seen at training time.

A hold-out dataset $$\dot{\mathcal{D}} = \{(\dot{x}^{(i)}, \dot{y}^{(i)}) \mid i = 1,2,...,m\}$$ is another dataset that is sampled IID from the same distribution $\mathbb{P}$ as the training dataset $\mathcal{D}$ and the two datasets are disjoint.

Let's genenerate a hold-out dataset for the example we saw earlier.

In [5]:

```
import numpy as np
import matplotlib.pyplot as plt
np.random.seed(0)
def true_fn(X):
return np.cos(1.5 * np.pi * X)
X_test = np.linspace(0, 1, 100)
plt.plot(X_test, true_fn(X_test), label="True function")
plt.legend()
```

Out[5]:

<matplotlib.legend.Legend at 0x12116be48>

Let's genenerate a hold-out dataset for the example we saw earlier.

In [6]:

```
n_samples, n_holdout_samples = 30, 30
X = np.sort(np.random.rand(n_samples))
y = true_fn(X) + np.random.randn(n_samples) * 0.1
X_holdout = np.sort(np.random.rand(n_holdout_samples))
y_holdout = true_fn(X_holdout) + np.random.randn(n_holdout_samples) * 0.1
plt.plot(X_test, true_fn(X_test), label="True function")
plt.scatter(X, y, edgecolor='b', s=20, label="Samples")
plt.scatter(X_holdout, y_holdout, edgecolor='r', s=20, label="Holdout Samples")
plt.legend()
```

Out[6]:

<matplotlib.legend.Legend at 0x121440f28>

Suppose that we have a function $\texttt{isaccurate}(y, y')$ that determines if $y$ is an accurate estimate of $y'$, e.g.:

- Is the the target variable close enough to the true target? $$\texttt{isaccurate}(y, y') = \text{true if } (|y - y'| \text{ is small), else false}$$

- Did we predict the right class? $$\texttt{isaccurate}(y, y') = \text{true if } (y = y') \text{ else false} $$

This defines accuracy on a data point. We say a supervised learning model is accurate if it correctly predicts the target on *new (held-out) data*.

We can say that a predictive model $f$ is accurate if it's probability of making an error on a random holdout sample is small:

$$ 1 - \mathbb{P} \left[ \texttt{isaccurate}(\dot y, f(\dot x)) \right] \leq \epsilon $$for $\dot{x}, \dot{y} \sim \mathbb{P}$, for some small $\epsilon > 0$ and some definition of accuracy.

We can also say that a predictive model $f$ is inaccurate if it's probability of making an error on a random holdout sample is large:

$$ 1 - \mathbb{P} \left[ \texttt{isaccurate}(\dot y, f(\dot x)) \right] \geq \epsilon $$or equivalently

$$\mathbb{P} \left[ \texttt{isaccurate}(\dot y, f(\dot x)) \right] \leq 1-\epsilon.$$In machine learning, **generalization** is the property of predictive models to achieve good performance on new, heldout data that is distinct from the training set.

Will supervised learning return a model that generalizes?

Recall that supervised learning at a high level performs the following procedure:

- Collect a training dataset $\mathcal{D}$ of labeled examples.
- Output a model that is accurate on $\mathcal{D}$.

I claim that the output model is also guaranteed to generalize if $\mathcal{D}$ is large enough.

In order to prove that supervised learning works, we will make two simplifying assumptions:

- We define a model class $\mathcal{M}$ containing $H$ different models.
- One of these models fits the training data perfectly (is accurate on every point) and we choose that model.

(Both of these assumptions can be relaxed.)

**Claim**: The probability that supervised learning will return an inaccurate model decreases exponentially with training set size $n$.

- A model $f$ is inaccurate if $\mathbb{P} \left[ \texttt{isaccurate}(\dot y, f(\dot x)) \right] \leq 1-\epsilon$. The probability that an inaccurate model $f$ perfectly fits the training set is at most $\prod_{i=1}^n \mathbb{P} \left[ \texttt{isaccurate}(y^{(i)}, f(x^{(i)})) \right] \leq (1-\epsilon)^n$.

- We have $H$ models in $\mathcal{M}$, and any of them could be in accurate. The probability that at least one the at most $H$ inaccurate models willl fit the training set perfectly is $\leq H (1-\epsilon)^n$.

Therefore, the claim holds.

Let's now dive deeper into the concept of generalization and two possible failure modes of supervised learning: overfitting and underfitting.

We will assume that the dataset is governed by a probability distribution $\mathbb{P}$, which we will call the *data distribution*. We will denote this as
$$x, y \sim \mathbb{P}.$$

A hold-out set $\dot{\mathcal{D}} = \{(\dot{x^{(i)}}, \dot{y^{(i)}}) \mid i = 1,2,...,n\}$ consists of *independent and identicaly distributed* (IID) samples from $\mathbb{P}$ and is distinct from the training set.

A model that **generalizes** is accurate on a hold-out set.

In 1D polynomial regression, we fit a model $$ f_\theta(x) := \theta^\top \phi(x) $$ that is linear in $\theta$ but non-linear in $x$ because the features $\phi(x) : \mathbb{R} \to \mathbb{R}^p$ are non-linear.

By using polynomial features such as $\phi(x) = [1\; x\; \ldots\; x^p]$, we can fit any polynomial of degree $p$.

When we switch from linear models to polynomials, we can better fit the data and increase the accuracy of our models.

Consider the synthetic dataset that we have seen earlier.

In [7]:

```
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LinearRegression
np.random.seed(0)
n_samples = 30
X = np.sort(np.random.rand(n_samples))
y = true_fn(X) + np.random.randn(n_samples) * 0.1
X_test = np.linspace(0, 1, 100)
plt.plot(X_test, true_fn(X_test), label="True function")
plt.scatter(X, y, edgecolor='b', s=20, label="Samples")
```

Out[7]:

<matplotlib.collections.PathCollection at 0x12e0c58d0>

Although fitting a linear model does not work well, qudratic or cubic polynomials improve the fit.

In [8]:

```
degrees = [1, 2, 3]
plt.figure(figsize=(14, 5))
for i in range(len(degrees)):
ax = plt.subplot(1, len(degrees), i + 1)
polynomial_features = PolynomialFeatures(degree=degrees[i], include_bias=False)
linear_regression = LinearRegression()
pipeline = Pipeline([("pf", polynomial_features), ("lr", linear_regression)])
pipeline.fit(X[:, np.newaxis], y)
ax.plot(X_test, true_fn(X_test), label="True function")
ax.plot(X_test, pipeline.predict(X_test[:, np.newaxis]), label="Model")
ax.scatter(X, y, edgecolor='b', s=20, label="Samples")
ax.set_xlim((0, 1))
ax.set_ylim((-2, 2))
ax.legend(loc="best")
ax.set_title("Polynomial of Degree {}".format(degrees[i]))
```

As we increase the complexity of our model class $\mathcal{M}$ to even higher degree polynomials, we are able to fit the data increasingly even better.

What happens if we further increase the degree of the polynomial?

In [10]:

```
degrees = [30]
plt.figure(figsize=(14, 5))
for i in range(len(degrees)):
ax = plt.subplot(1, len(degrees), i + 1)
polynomial_features = PolynomialFeatures(degree=degrees[i], include_bias=False)
linear_regression = LinearRegression()
pipeline = Pipeline([("pf", polynomial_features), ("lr", linear_regression)])
pipeline.fit(X[:, np.newaxis], y)
X_test = np.linspace(0, 1, 100)
ax.plot(X_test, true_fn(X_test), label="True function")
ax.plot(X_test, pipeline.predict(X_test[:, np.newaxis]), label="Model")
ax.scatter(X, y, edgecolor='b', s=20, label="Samples")
ax.set_xlim((0, 1))
ax.set_ylim((-2, 2))
ax.legend(loc="best")
ax.set_title("Polynomial of Degree {}".format(degrees[i]))
```

As the degree of the polynomial increases to the size of the dataset, we are increasingly able to fit every point in the dataset.

However, this results in a highly irregular curve: its behavior outside the training set is wildly inaccurate.

Overfitting is one of the most common failure modes of machine learning.

- A very expressive model (a high degree polynomial) fits the training dataset perfectly.
- The model also makes wildly incorrect prediction outside this dataset, and doesn't generalize.

A related failure mode is underfitting.

- A small model (e.g. a straight line), will not fit the training data well.
- Held-out data is similar to training data, so it will not be accurate either.

Finding the tradeoff between overfitting and underfitting is one of the main challenges in applying machine learning.

We can measure overfitting and underfitting by estimating accuracy on held-out data and comparing it to the training data.

- If training perforance is high but held-out performance is low, we are overfitting.
- If training perforance is low but held-out performance is low, we are underfitting.

In [11]:

```
degrees = [1, 20, 5]
titles = ['Underfitting', 'Overfitting', 'A Good Fit']
plt.figure(figsize=(14, 5))
for i in range(len(degrees)):
ax = plt.subplot(1, len(degrees), i + 1)
polynomial_features = PolynomialFeatures(degree=degrees[i], include_bias=False)
linear_regression = LinearRegression()
pipeline = Pipeline([("pf", polynomial_features), ("lr", linear_regression)])
pipeline.fit(X[:, np.newaxis], y)
ax.plot(X_test, true_fn(X_test), label="True function")
ax.plot(X_test, pipeline.predict(X_test[:, np.newaxis]), label="Model")
ax.scatter(X, y, edgecolor='b', s=20, label="Samples", alpha=0.2)
ax.scatter(X_holdout[::3], y_holdout[::3], edgecolor='r', s=20, label="Samples")
ax.set_xlim((0, 1))
ax.set_ylim((-2, 2))
ax.legend(loc="best")
ax.set_title("{} (Degree {})".format(titles[i], degrees[i]))
ax.text(0.05,-1.7, 'Holdout MSE: %.4f' % ((y_holdout-pipeline.predict(X_holdout[:, np.newaxis]))**2).mean())
```

Balancing overfitting vs. underfitting is a major challenges in applying machine learning. Briefly, here are some approaches:

- To fight under-fitting, we may increase our model class to encompass more expressive models.
- We may also create richer features for the data that will make the dataset easier to fit.

We will see many ways of dealing with overftting, but here are some ideas:

- If we're overfitting, we may reduce the complexity of our model by reducing the size of $\mathcal{M}$
- We may also modify our objective to penalize complex models that may overfit the data.

We will now see a very important way to reduce overfitting --- regularization. We will also see several important new algorithms.

We will assume that the dataset is governed by a probability distribution $\mathbb{P}$, which we will call the *data distribution*. We will denote this as
$$x, y \sim \mathbb{P}.$$

A hold-out set $\dot{\mathcal{D}} = \{(\dot{x^{(i)}}, \dot{y^{(i)}}) \mid i = 1,2,...,n\}$ consists of *independent and identicaly distributed* (IID) samples from $\mathbb{P}$ and is distinct from the training set.

Overfitting is one of the most common failure modes of machine learning.

- A very expressive model (a high degree polynomial) fits the training dataset perfectly.
- The model also makes wildly incorrect prediction outside this dataset, and doesn't generalize.

We can visualize overfitting by trying to fit a small dataset with a high degree polynomial.

In [12]:

```
degrees = [30]
plt.figure(figsize=(14, 5))
for i in range(len(degrees)):
ax = plt.subplot(1, len(degrees), i + 1)
polynomial_features = PolynomialFeatures(degree=degrees[i], include_bias=False)
linear_regression = LinearRegression()
pipeline = Pipeline([("pf", polynomial_features), ("lr", linear_regression)])
pipeline.fit(X[:, np.newaxis], y)
X_test = np.linspace(0, 1, 100)
ax.plot(X_test, true_fn(X_test), label="True function")
ax.plot(X_test, pipeline.predict(X_test[:, np.newaxis]), label="Model")
ax.scatter(X, y, edgecolor='b', s=20, label="Samples")
ax.set_xlim((0, 1))
ax.set_ylim((-2, 2))
ax.legend(loc="best")
ax.set_title("Polynomial of Degree {}".format(degrees[i]))
```

The idea of regularization is to penalize complex models that may overfit the data.

In the previous example, a less complex would rely less on polynomial terms of high degree.

The idea of regularization is to train models with an augmented objective $J : \mathcal{M} \to \mathbb{R}$ defined over a training dataset $\mathcal{D}$ of size $n$ as $$J(f) = \frac{1}{n} \sum_{i=1}^n L(y^{(i)}, f(x^{(i)})) + \lambda \cdot R(f).$$

Let's dissect the components of this objective:

$$J(f) = \frac{1}{n} \sum_{i=1}^n L(y^{(i)}, f(x^{(i)})) + \lambda \cdot R(f).$$- A loss function $L(y, f(x))$ such as the mean squared error.

- A regularizer $R : \mathcal{M} \to \mathbb{R}$ that penalizes models that are overly complex.

- A regularization parameter $\lambda > 0$, which controls the strength of the regularizer.

When the model $f_\theta$ is parametrized by parameters $\theta$, we can also use the following notation:

$$J(\theta) = \frac{1}{n} \sum_{i=1}^n L(y^{(i)}, f_\theta(x^{(i)})) + \lambda \cdot R(\theta).$$How can we define a regularizer $R: \mathcal{M} \to \mathbb{R}$ to control the complexity of a model $f \in \mathcal{M}$?

In the context of linear models $f(x) = \theta^\top x$, a widely used approach is L2 regularization, which defines the following objective: $$J(\theta) = \frac{1}{n} \sum_{i=1}^n L(y^{(i)}, \theta^\top x^{(i)}) + \frac{\lambda}{2} \cdot ||\theta||_2^2.$$

Let's dissect the components of this objective. $$J(\theta) = \frac{1}{n} \sum_{i=1}^n L(y^{(i)}, \theta^\top x^{(i)}) + \frac{\lambda}{2} \cdot ||\theta||_2^2.$$

- The regularizer $R : \mathcal{M} \to \mathbb{R}$ is the function $R(\theta) = ||\theta||_2^2 = \sum_{j=1}^d \theta_j^2.$ This is also known as the L2 norm of $\theta$.

- The regularizer penalizes large parameters. This prevents us from over-relying on any single feature and penalizes wildly irregular solutions.

- L2 regularization can be used with most models (linear, neural, etc.)

Let's consider an application to the polynomial model we have seen so far. Given polynomial features $\phi(x)$, we optimize the following objective: $$ J(\theta) = \frac{1}{2n} \sum_{i=1}^n \left( y^{(i)} - \theta^\top \phi(x^{(i)}) \right)^2 + \frac{\lambda}{2} \cdot ||\theta||_2^2. $$

We are going to implement regularized and standard polynomial regression on three random training sets sampled from the same distribution.

In [13]:

```
from sklearn.linear_model import Ridge
degrees = [15, 15, 15]
plt.figure(figsize=(14, 5))
for idx, i in enumerate(range(len(degrees))):
# sample a dataset
np.random.seed(idx)
n_samples = 30
X = np.sort(np.random.rand(n_samples))
y = true_fn(X) + np.random.randn(n_samples) * 0.1
# fit a least squares model
polynomial_features = PolynomialFeatures(degree=degrees[i], include_bias=False)
linear_regression = LinearRegression()
pipeline = Pipeline([("pf", polynomial_features), ("lr", linear_regression)])
pipeline.fit(X[:, np.newaxis], y)
# fit a Ridge model
polynomial_features = PolynomialFeatures(degree=degrees[i], include_bias=False)
linear_regression = Ridge(alpha=0.1) # sklearn uses alpha instead of lambda
pipeline2 = Pipeline([("pf", polynomial_features), ("lr", linear_regression)])
pipeline2.fit(X[:, np.newaxis], y)
# visualize results
ax = plt.subplot(1, len(degrees), i + 1)
# ax.plot(X_test, true_fn(X_test), label="True function")
ax.plot(X_test, pipeline.predict(X_test[:, np.newaxis]), label="No Regularization")
ax.plot(X_test, pipeline2.predict(X_test[:, np.newaxis]), label="L2 Regularization")
ax.scatter(X, y, edgecolor='b', s=20, label="Samples")
ax.set_xlim((0, 1))
ax.set_ylim((-2, 2))
ax.legend(loc="best")
ax.set_title("Dataset sample #{}".format(idx))
```

We can show that by usinng small weights, we prevent the model from learning irregular functions.

In [14]:

```
print('Non-regularized weights of the polynomial model need to be large to fit every point:')
print(pipeline.named_steps['lr'].coef_[:4])
print()
print('By regularizing the weights to be small, we force the curve to be more regular:')
print(pipeline2.named_steps['lr'].coef_[:4])
```

In brief, the most common approach is to choose the value of $\lambda$ that results in the best performance on a held-out *validation* set.

We will later see this strategies and several other in more detail

How, do we fit regularized models? In the linear case, we can do this easily by deriving generalized normal equations!

Let $L(\theta) = \frac{1}{2} (X \theta - y)^\top (X \theta - y)$ be our least squares objective. We can write the Ridge objective as: $$ J(\theta) = \frac{1}{2} (X \theta - y)^\top (X \theta - y) + \frac{1}{2} \lambda ||\theta||_2^2 $$

This allows us to derive the gradient as follows: \begin{align*} \nabla_\theta J(\theta) & = \nabla_\theta \left( \frac{1}{2} (X \theta - y)^\top (X \theta - y) + \frac{1}{2} \lambda ||\theta||_2^2 \right) \\ & = \nabla_\theta \left( L(\theta) + \frac{1}{2} \lambda ||\theta||_2^2 \right) \\ & = \nabla_\theta L(\theta) + \lambda \theta \\ & = (X^\top X) \theta - X^\top y + \lambda \theta \\ & = (X^\top X + \lambda I) \theta - X^\top y \end{align*}

We used the derivation of the normal equations for least squares to obtain $\nabla_\theta L(\theta)$ as well as the fact that: $\nabla_x x^\top x = 2 x$.

We can set the gradient to zero to obtain normal equations for the Ridge model: $$ (X^\top X + \lambda I) \theta = X^\top y. $$

Hence, the value $\theta^*$ that minimizes this objective is given by: $$ \theta^* = (X^\top X + \lambda I)^{-1} X^\top y.$$

Note that the matrix $(X^\top X + \lambda I)$ is always invertible, which addresses a problem with least squares that we saw earlier.

**Type**: Supervised learning (regression)**Model family**: Linear models**Objective function**: L2-regularized mean squared error**Optimizer**: Normal equations

We will now look another form of regularization, which will have an important new property called sparsity.

The idea of regularization is to train models with an augmented objective $J : \mathcal{M} \to \mathbb{R}$ defined over a training dataset $\mathcal{D}$ of size $n$ as $$ J(f) = \frac{1}{n} \sum_{i=1}^n L(y^{(i)}, f(x^{(i)})) + \lambda \cdot R(f). $$

Let's dissect the components of this objective:

$$ J(f) = \frac{1}{n} \sum_{i=1}^n L(y^{(i)}, f(x^{(i)})) + \lambda \cdot R(f). $$- A loss function $L(y, f(x))$ such as the mean squared error.

- A regularizer $R : \mathcal{M} \to \mathbb{R}$ that penalizes models that are overly complex.

Another closely related approach to regularization is to penalize the size of the weights using the L1 norm.

In the context of linear models $f(x) = \theta^\top x$, L1 regularization yields the following objective: $$ J(\theta) = \frac{1}{n} \sum_{i=1}^n L(y^{(i)}, \theta^\top x^{(i)}) + \lambda \cdot ||\theta||_1. $$

Let's dissect the components of this objective. $$ J(\theta) = \frac{1}{n} \sum_{i=1}^n L(y^{(i)}, \theta^\top x^{(i)}) + \lambda \cdot ||\theta||_1. $$

- The regularizer $R : \mathcal{M} \to \mathbb{R}$ is the function $R(\theta) = ||\theta||_1 = \sum_{j=1}^d |\theta_j|.$ This is also known as the L1 norm of $\theta$.

- The regularizer also penalizes large weights. It also forces more weights to decay to zero, as opposed to just being small.

L1-regularized linear regression is also known as the Lasso (least absolute shrinkage and selection operator).

**Type**: Supervised learning (regression)**Model family**: Linear models**Objective function**: L1-regularized mean squared error

**Optimizer**: gradient descent, coordinate descent, least angle regression (LARS) and others

Consider regularized problem with a penalty term: $$ \min_{\theta \in \Theta} L(\theta) + \lambda \cdot R(\theta). $$

We may also enforce an explicit constraint on the complexity of the model: \begin{align*} \min_{\theta \in \Theta} \; & L(\theta) \\ \text{such that } \; & R(\theta) \leq \lambda' \end{align*} We will not prove this, but solving this problem is equivalent so solving the penalized problem for some $\lambda > 0$ that's different from $\lambda'$.

In other words,

- We can regularize by explicitly enforcing $R(\theta)$ to be less than a value instead of penalizing it.
- For each value of $\lambda$, we are implicitly setting a constraint of $R(\theta)$.

This is what it looks like for a linear model: \begin{align*} \min_{\theta \in \Theta} \; & \frac{1}{2n} \sum_{i=1}^n \left( y^{(i)} - \theta^\top x^{(i)} \right)^2 \\ \text{such that } \; & ||\theta|| \leq \lambda' \end{align*} where $||\cdot||$ can either be the L1 or L2 norm.

The following image by Divakar Kapil and Hastie et al. explains the difference between the two norms.

A vector is said to be sparse if a large fraction of its entires is zero.

L1-regularized linear regression produces *sparse weights*.

- This is makes the model more interpretable
- It also makes it computationally more tractable in very large dimensions.

To better understand sparsity, we will fit L2-regularized linear models to the UCI diabetes dataset and observe the magnitude of each weight (colored lines) as a function of the regularization parameter.

In [15]:

```
# based on https://scikit-learn.org/stable/auto_examples/linear_model/plot_ridge_path.html
from sklearn.datasets import load_diabetes
from sklearn.linear_model import Ridge
from matplotlib import pyplot as plt
X, y = load_diabetes(return_X_y=True)
# create ridge coefficients
alphas = np.logspace(-5, 2, )
ridge_coefs = []
for a in alphas:
ridge = Ridge(alpha=a, fit_intercept=False)
ridge.fit(X, y)
ridge_coefs.append(ridge.coef_)
# plot ridge coefficients
plt.figure(figsize=(14, 5))
plt.plot(alphas, ridge_coefs)
plt.xscale('log')
plt.xlabel('Regularization parameter (lambda)')
plt.ylabel('Magnitude of model parameters')
plt.title('Ridge coefficients as a function of the regularization')
plt.axis('tight')
```

Out[15]:

(4.466835921509635e-06, 223.872113856834, -868.4051623855127, 828.0533448059361)