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Open In Colab Now that we have introduced somewhat more formally the learning problem and its notation lets us study a simple but instructive regression problem that is known in the statistics literature as shrinkage. Suppose that we are given the training set x={x1,...,xm}\mathbf{x} = \{x_1,...,x_m\} together with their labels, the vectors y\mathbf{y}. We need to construct a model such that a suitably chosen loss function is minimized for a different set of input data, the so-called test set. The ability to correctly predict when observing the test set, is called generalization. Training dataset and Target Unknown Function Training Dataset (m=10) for the Regression Model. The green curve is the uknown target function. Please replace the label axis denoted by tt with yy to make it compatible with our notation. Since the output yy is a continuous variable then the supervised learning problem is called a regression problem (otherwise its a classification problem). The synthetic dataset is generated by the function sin⁡(2πx)+ϵ\sin(2 \pi x) + ϵ where xx is a uniformly distributed random variable and ϵϵ is N(μ=0.0,σ2=0.3)N(\mu=0.0, \sigma^2=0.3). This target function is completely unknown to us - we just mention it here just to provide a visual clue of what the hypothesis set needs to aspire to. 
# !pip install git+https://github.com/pantelis-classes/PRML.git#egg=prml

import seaborn as sns

# Apply the default theme
sns.set_theme()

import numpy as np
from scipy.stats import multivariate_normal
import matplotlib.pyplot as plt

%matplotlib inline

from prml.preprocess import GaussianFeature, PolynomialFeature, SigmoidalFeature
from prml.linear import BayesianRegression, EmpiricalBayesRegression, LinearRegression, RidgeRegression

np.random.seed(1234)

def create_toy_data(func, sample_size, std, domain=[0, 1]):
    x = np.linspace(domain[0], domain[1], sample_size)
    np.random.shuffle(x)
    y = func(x) + np.random.normal(scale=std, size=x.shape)
    return x, y


def sinusoidal(x):
    return np.sin(2 * np.pi * x)


x_train, y_train = create_toy_data(sinusoidal, 10, 0.25)
x_test = np.linspace(0, 1, 100)
y_test = sinusoidal(x_test)

plt.figure(figsize=[10, 8])
plt.scatter(x_train, y_train, facecolor="none", edgecolor="b", s=50, label="training data")
plt.plot(x_test, y_test, label="$\sin(2\pi x)$")
plt.plot(x_test, y_test, "-g", label="Target Function")
plt.legend()
plt.show()
Let us now pick the hypothesis set that corresponds in general to the following sum, g(w,x)=w0+∑j=1M−1wjϕj(x)=wTϕ(x)g(\mathbf{w},\mathbf{x}) = w_0 + \sum_{j=1}^{M-1}w_j \phi_j(\mathbf x) = \mathbf w^T \mathbf \phi(\mathbf x) where ϕj(x)\phi_j(\mathbf x) are know as basis functions. A set of Polynomial, Gaussian and Sigmoidal basis functions are plotted below. 
x = np.linspace(-1, 1, 100)
X_polynomial = PolynomialFeature(11).transform(x[:, None])
X_gaussian = GaussianFeature(np.linspace(-1, 1, 11), 0.1).transform(x)
X_sigmoidal = SigmoidalFeature(np.linspace(-1, 1, 11), 10).transform(x)

plt.figure(figsize=(20, 5))
for i, X in enumerate([X_polynomial, X_gaussian, X_sigmoidal]):
    plt.subplot(1, 3, i + 1)
    for j in range(12):
        plt.plot(x, X[:, j])
txt = "Polynomial, Gaussian and Sigmoidal basis functions"
plt.figtext(0.5, 0.01, txt, wrap=True, horizontalalignment="center", fontsize=12)
Our job is to find w\mathbf{w} such that the polynomial expansion above fits the data we are given - as we will see there are multiple hypothesis that can satisfy this requirement. Consistent with the block diagram we need to define a metric, an figure of merit or loss function in fact, that is also a common metric in regression problems of this nature. This is the Mean Squared Error (MSE) function. L(w)=1m∑i=1m{(g(w,xi)−yi)}2L(\mathbf{w}) = \frac{1}{m} \sum_{i=1}^m \{(g(\mathbf{w},x_i)-y_i)\}^2 . Loss Function The loss function chosen for this regression problem, corresponds to the sum of the squares of the displacements of each data point and our hypothesis. The sum of squares in the case of Gaussian errors gives raise to an (unbiased) Maximum Likelihood estimate of the model parameters. Contrast this to sum of absolute differences. Now our job has become to choose two things: the weight vector w∗\mathbf{w^*} and MM the order of the polynomial. Both define our hypothesis. If you think about it, the order MM defines the model complexity in the sense that the larger MM becomes the more the number of weights we need to estimate and store. Obviously this is a trivial example and storage is not a concern here but treat this example as instructive for that it applies in far for complicated settings. M=0 M=1 M=3 M=9 Various final hypotheses. Obviously you can reduce the training error to almost zero by selecting a model that is complicated enough (M=9) to perfectly fit the training data (if m is small). Loss Function But this is not what you want to do. Because when met with test data, the model will perform far worse than a less complicated model that is closer to the true model (e.g. M=3). This is a central observation in statistical learning called overfitting. In addition, you may not have the time to iterate over M (very important in online learning settings). 
M = 9
# Pick one of the three features below
feature = PolynomialFeature(M)
# feature = GaussianFeature(np.linspace(0, 1, M), 0.1)
# feature = SigmoidalFeature(np.linspace(0, 1, M), 10)

X_train = feature.transform(x_train)
X_test = feature.transform(x_test)
model = LinearRegression()
model.fit(X_train, y_train)

plt.figure(figsize=[10, 8])
plt.plot(model.w)
plt.xlabel("index of $w$")
plt.ylabel("$w$")

y, y_std = model.predict(X_test, return_std=True)

plt.figure(figsize=[10, 8])

plt.scatter(x_train, y_train, facecolor="none", edgecolor="b", s=50, label="training data")
plt.plot(x_test, y, label="mean")
plt.fill_between(x_test, y - y_std, y + y_std, color="orange", alpha=0.5, label="std.")
plt.legend()
plt.xlabel("$x$")
plt.ylabel("$y$")

plt.show()
To avoid overfitting we have multiple strategies. One straightforward one is evident by observing the wild oscillations of the w\mathbf{w} elements as the model complexity increases. We can penalize such oscillations by introducing the l2l_2 norm of w\mathbf{w} in our loss function. L(w)=12∑i=1m{(g(w,xi)−yi)}2+λ2∣∣w∣∣2L(\mathbf{w}) = \frac{1}{2} \sum_{i=1}^m \{(g(\mathbf{w},x_i)-y_i)\}^2 + \frac{\lambda}{2} ||\mathbf{w}||^2 This type of solution is called regularization and because we effectively shrink the weight dynamic range it is also called in statistics shrinkage or ridge regression. We have introduced a new parameter λ\lambda that regulates the relative importance of the penalty term as compared to the MSE. This parameter together with the polynomial order is what we call hyperparameters and we need to optimize them as both are needed for the determination of our final hypothesis gg. The graph below show the results of each search iteration on the λ\lambda hyperparameter. Loss Function 
model = RidgeRegression(alpha=1e-3)
model.fit(X_train, y_train)
y = model.predict(X_test)

plt.figure(figsize=[10, 8])
plt.scatter(x_train, y_train, facecolor="none", edgecolor="b", s=50, label="training data")
plt.plot(x_test, y, label="Regularized Final Hypothesis")
plt.legend()
plt.show()

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Bias and Variance Decomposition during the training process

Apart from the composition of the generalization error for various model capacities, it is interesting to make some general comments regarding the decomposition of the generalization error (also known as empirical risk) during training. Early in training the bias is large because the network output is far from the design function. The variance is very small because the data has had little influence yet. Late in training the bias is small because the network has learned the underlying function. However if train for too long then the network will also have learned the noise specific to the dataset (overfitting). In such case the variance will be large because the noise varies between training and test datasets.
This notebook makes extensive use of the Pattern Recognition for Machine Learning python package.
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