モデルの性能評価
交差検証(cross-validation)を用いてモデルを評価し、検証セットでの平均二乗誤差(Mean Squared Error, MSE)を計算します。
degrees = [1, 4, 15]
plt.figure(figsize=(14, 5))
for i in range(len(degrees)):
ax = plt.subplot(1, len(degrees), i + 1)
plt.setp(ax, xticks=(), yticks=())
polynomial_features = PolynomialFeatures(degree=degrees[i], include_bias=False)
linear_regression = LinearRegression()
pipeline = Pipeline(
[
("polynomial_features", polynomial_features),
("linear_regression", linear_regression),
]
)
pipeline.fit(X[:, np.newaxis], y)
## Evaluate the models using crossvalidation
scores = cross_val_score(
pipeline, X[:, np.newaxis], y, scoring="neg_mean_squared_error", cv=10
)
X_test = np.linspace(0, 1, 100)
plt.plot(X_test, pipeline.predict(X_test[:, np.newaxis]), label="Model")
plt.plot(X_test, true_fun(X_test), label="True function")
plt.scatter(X, y, edgecolor="b", s=20, label="Samples")
plt.xlabel("x")
plt.ylabel("y")
plt.xlim((0, 1))
plt.ylim((-2, 2))
plt.legend(loc="best")
plt.title(
"Degree {}\nMSE = {:.2e}(+/- {:.2e})".format(
degrees[i], -scores.mean(), scores.std()
)
)
plt.show()