在Iris數據集上繪制樹集成的決策曲面?
使用Iris數據集的一對特征訓練隨機樹的森林， 并繪制決策界面。
此圖比較了決策樹分類器(第一列)、隨機森林分類器(第二列)、極端樹分類器(第三列)和AdaBoost分類器(第四列)學習的決策面。
在第一行中，分類器只使用萼片寬度和萼片長度特征，第二行僅使用花瓣長度和萼片長度，第三行僅使用花瓣寬度和花瓣長度。
按照得分的降序，當使用30個估計器對所有4個特性進行訓練(在本示例之外)并使用10倍交叉驗證得分時，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(但平均分數沒有提高)。
有關每個模型的更多細節，請參見控制臺的輸出。
在本例中，您可以嘗試：
對于 DecisionTreeClassifier和AdaBoostClassifier, 可以改變max_depth， 或許可以嘗試對于DecisionTreeClassifiermax_depth=3, 而對于AdaBoostClassifier可以是max_depth=None。改變 n_estimators。
值得注意的是，RandomForests和ExtraTrees可以在許多內核上并行訓練，因為每棵樹都是獨立于其他樹構建的。AdaBoost的樹是按順序構建的，因此不要使用多個核。
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 0.8533333333333334
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 0.9933333333333333
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn.datasets import load_iris
from sklearn.ensemble import (RandomForestClassifier, ExtraTreesClassifier,
AdaBoostClassifier)
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()
腳本的總運行時間：(0分9.465秒)