3.4.8.10. Plot fitting a 9th order polynomialΒΆ

Fits data generated from a 9th order polynomial with model of 4th order and 9th order polynomials, to demonstrate that often simpler models are to be preferred

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn import linear_model
# Create color maps for 3-class classification problem, as with iris
cmap_light = ListedColormap(["#FFAAAA", "#AAFFAA", "#AAAAFF"])
cmap_bold = ListedColormap(["#FF0000", "#00FF00", "#0000FF"])
rng = np.random.default_rng(27446968)
x = 2 * rng.random(100) - 1
f = lambda t: 1.2 * t**2 + 0.1 * t**3 - 0.4 * t**5 - 0.5 * t**9
y = f(x) + 0.4 * rng.normal(size=100)
x_test = np.linspace(-1, 1, 100)

The data

plt.figure(figsize=(6, 4))
plt.scatter(x, y, s=4)
plot polynomial regression
<matplotlib.collections.PathCollection object at 0x7f78d1ccf310>

Fitting 4th and 9th order polynomials

For this we need to engineer features: the n_th powers of x:

plt.figure(figsize=(6, 4))
plt.scatter(x, y, s=4)
X = np.array([x**i for i in range(5)]).T
X_test = np.array([x_test**i for i in range(5)]).T
regr = linear_model.LinearRegression()
regr.fit(X, y)
plt.plot(x_test, regr.predict(X_test), label="4th order")
X = np.array([x**i for i in range(10)]).T
X_test = np.array([x_test**i for i in range(10)]).T
regr = linear_model.LinearRegression()
regr.fit(X, y)
plt.plot(x_test, regr.predict(X_test), label="9th order")
plt.legend(loc="best")
plt.axis("tight")
plt.title("Fitting a 4th and a 9th order polynomial")
Fitting a 4th and a 9th order polynomial
Text(0.5, 1.0, 'Fitting a 4th and a 9th order polynomial')

Ground truth

plt.figure(figsize=(6, 4))
plt.scatter(x, y, s=4)
plt.plot(x_test, f(x_test), label="truth")
plt.axis("tight")
plt.title("Ground truth (9th order polynomial)")
plt.show()
Ground truth (9th order polynomial)

Total running time of the script: (0 minutes 0.226 seconds)

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