注意
前往結尾以下載完整的範例程式碼。或者透過 JupyterLite 或 Binder 在您的瀏覽器中執行此範例
繪製樹集合在 Iris 資料集上的決策曲面#
繪製在 Iris 資料集特徵對上訓練的隨機樹森林的決策曲面。
此圖比較了決策樹分類器（第一欄）、隨機森林分類器（第二欄）、極端隨機樹分類器（第三欄）和 AdaBoost 分類器（第四欄）所學習的決策曲面。
在第一行中，分類器僅使用花萼寬度和花萼長度特徵建立，在第二行中僅使用花瓣長度和花萼長度，在第三行中僅使用花瓣寬度和花瓣長度。
當（在此範例之外）使用所有 4 個特徵、30 個估計器進行訓練，並使用 10 折交叉驗證進行評分時，依品質降序排列，我們看到
ExtraTreesClassifier() # 0.95 score
RandomForestClassifier() # 0.94 score
AdaBoost(DecisionTree(max_depth=3)) # 0.94 score
DecisionTree(max_depth=None) # 0.94 score
增加 AdaBoost 的 max_depth 會降低分數的標準差（但平均分數不會提高）。
請參閱主控台的輸出，以取得每個模型的更多詳細資訊。
在此範例中，您可以嘗試
為
DecisionTreeClassifier和AdaBoostClassifier更改max_depth，也許為DecisionTreeClassifier嘗試max_depth=3或為AdaBoostClassifier嘗試max_depth=None變更
n_estimators
值得注意的是，RandomForests 和 ExtraTrees 可以並行在多個核心上進行擬合，因為每棵樹都是獨立於其他樹建立的。AdaBoost 的樣本是依序建立的，因此不使用多個核心。
DecisionTree with features [0, 1] has a score of 0.9266666666666666
RandomForest with 30 estimators with features [0, 1] has a score of 0.9266666666666666
ExtraTrees with 30 estimators with features [0, 1] has a score of 0.9266666666666666
AdaBoost with 30 estimators with features [0, 1] has a score of 0.82
DecisionTree with features [0, 2] has a score of 0.9933333333333333
RandomForest with 30 estimators with features [0, 2] has a score of 0.9933333333333333
ExtraTrees with 30 estimators with features [0, 2] has a score of 0.9933333333333333
AdaBoost with 30 estimators with features [0, 2] has a score of 0.9933333333333333
DecisionTree with features [2, 3] has a score of 0.9933333333333333
RandomForest with 30 estimators with features [2, 3] has a score of 0.9933333333333333
ExtraTrees with 30 estimators with features [2, 3] has a score of 0.9933333333333333
AdaBoost with 30 estimators with features [2, 3] has a score of 0.9866666666666667
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.colors import ListedColormap
from sklearn.datasets import load_iris
from sklearn.ensemble import (
AdaBoostClassifier,
ExtraTreesClassifier,
RandomForestClassifier,
)
from sklearn.tree import DecisionTreeClassifier
# Parameters
n_classes = 3
n_estimators = 30
cmap = plt.cm.RdYlBu
plot_step = 0.02 # fine step width for decision surface contours
plot_step_coarser = 0.5 # step widths for coarse classifier guesses
RANDOM_SEED = 13 # fix the seed on each iteration
# Load data
iris = load_iris()
plot_idx = 1
models = [
DecisionTreeClassifier(max_depth=None),
RandomForestClassifier(n_estimators=n_estimators),
ExtraTreesClassifier(n_estimators=n_estimators),
AdaBoostClassifier(DecisionTreeClassifier(max_depth=3), n_estimators=n_estimators),
]
for pair in ([0, 1], [0, 2], [2, 3]):
for model in models:
# We only take the two corresponding features
X = iris.data[:, pair]
y = iris.target
# Shuffle
idx = np.arange(X.shape[0])
np.random.seed(RANDOM_SEED)
np.random.shuffle(idx)
X = X[idx]
y = y[idx]
# Standardize
mean = X.mean(axis=0)
std = X.std(axis=0)
X = (X - mean) / std
# Train
model.fit(X, y)
scores = model.score(X, y)
# Create a title for each column and the console by using str() and
# slicing away useless parts of the string
model_title = str(type(model)).split(".")[-1][:-2][: -len("Classifier")]
model_details = model_title
if hasattr(model, "estimators_"):
model_details += " with {} estimators".format(len(model.estimators_))
print(model_details + " with features", pair, "has a score of", scores)
plt.subplot(3, 4, plot_idx)
if plot_idx <= len(models):
# Add a title at the top of each column
plt.title(model_title, fontsize=9)
# Now plot the decision boundary using a fine mesh as input to a
# filled contour plot
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
xx, yy = np.meshgrid(
np.arange(x_min, x_max, plot_step), np.arange(y_min, y_max, plot_step)
)
# Plot either a single DecisionTreeClassifier or alpha blend the
# decision surfaces of the ensemble of classifiers
if isinstance(model, DecisionTreeClassifier):
Z = model.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
cs = plt.contourf(xx, yy, Z, cmap=cmap)
else:
# Choose alpha blend level with respect to the number
# of estimators
# that are in use (noting that AdaBoost can use fewer estimators
# than its maximum if it achieves a good enough fit early on)
estimator_alpha = 1.0 / len(model.estimators_)
for tree in model.estimators_:
Z = tree.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
cs = plt.contourf(xx, yy, Z, alpha=estimator_alpha, cmap=cmap)
# Build a coarser grid to plot a set of ensemble classifications
# to show how these are different to what we see in the decision
# surfaces. These points are regularly space and do not have a
# black outline
xx_coarser, yy_coarser = np.meshgrid(
np.arange(x_min, x_max, plot_step_coarser),
np.arange(y_min, y_max, plot_step_coarser),
)
Z_points_coarser = model.predict(
np.c_[xx_coarser.ravel(), yy_coarser.ravel()]
).reshape(xx_coarser.shape)
cs_points = plt.scatter(
xx_coarser,
yy_coarser,
s=15,
c=Z_points_coarser,
cmap=cmap,
edgecolors="none",
)
# Plot the training points, these are clustered together and have a
# black outline
plt.scatter(
X[:, 0],
X[:, 1],
c=y,
cmap=ListedColormap(["r", "y", "b"]),
edgecolor="k",
s=20,
)
plot_idx += 1 # move on to the next plot in sequence
plt.suptitle("Classifiers on feature subsets of the Iris dataset", fontsize=12)
plt.axis("tight")
plt.tight_layout(h_pad=0.2, w_pad=0.2, pad=2.5)
plt.show()
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