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集成学习和随机森林

🕗 发布于 2024-10-10 08:42 集成学习  随机森林  机器学习  

集成学习

生活中的集成学习：

买东西找别推荐

import numpy as np
import matplotlib.pyplot as plt

from sklearn import datasets

X, y = datasets.make_moons(n_samples=500, noise=0.3, random_state=42)

plt.scatter(X[y==0,0], X[y==0,1])
plt.scatter(X[y==1,0], X[y==1,1])
plt.show()


from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)

from sklearn.linear_model import LogisticRegression

log_clf = LogisticRegression()
log_clf.fit(X_train, y_train)
log_clf.score(X_test, y_test)


from sklearn.svm import SVC

svm_clf = SVC()
svm_clf.fit(X_train, y_train)
svm_clf.score(X_test, y_test)


from sklearn.tree import DecisionTreeClassifier

dt_clf = DecisionTreeClassifier(random_state=666)
dt_clf.fit(X_train, y_train)
dt_clf.score(X_test, y_test)


y_predict1 = log_clf.predict(X_test)
y_predict2 = svm_clf.predict(X_test)
y_predict3 = dt_clf.predict(X_test)

y_predict = np.array((y_predict1 + y_predict2 + y_predict3) >= 2, dtype='int')

y_predict[:10]


from sklearn.metrics import accuracy_score

accuracy_score(y_test, y_predict)

使用Voting Classifier

from sklearn.ensemble import VotingClassifier

voting_clf = VotingClassifier(estimators=[
    ('log_clf', LogisticRegression()), 
    ('svm_clf', SVC()),
    ('dt_clf', DecisionTreeClassifier(random_state=666))],
                             voting='hard')

voting_clf.fit(X_train, y_train)
voting_clf.score(X_test, y_test)

Soft Voting

Voting Classifier

更合理的投票，应该有权值

要求集合的每一个模型都能估计概率

import numpy as np
import matplotlib.pyplot as plt

from sklearn import datasets

X, y = datasets.make_moons(n_samples=500, noise=0.3, random_state=42)

plt.scatter(X[y==0,0], X[y==0,1])
plt.scatter(X[y==1,0], X[y==1,1])
plt.show()


from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)

使用 Hard Voting Classifier

from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import VotingClassifier

voting_clf = VotingClassifier(estimators=[
    ('log_clf', LogisticRegression()), 
    ('svm_clf', SVC()),
    ('dt_clf', DecisionTreeClassifier(random_state=666))],
                             voting='hard')

voting_clf.fit(X_train, y_train)
voting_clf.score(X_test, y_test)

使用 Soft Voting Classifier

voting_clf2 = VotingClassifier(estimators=[
    ('log_clf', LogisticRegression()), 
    ('svm_clf', SVC(probability=True)),
    ('dt_clf', DecisionTreeClassifier(random_state=666))],
                             voting='soft')

voting_clf2.fit(X_train, y_train)
voting_clf2.score(X_test, y_test)

集成学习

虽然有很多机器学习方法，但是从投票的角度看，仍然不够多

创建更多的子模型!集成更多的子模型的意见。

子模型之间不能一致!子模型之间要有差异性

如何创建差异性?

每个子模型只看样本数据的一部分。

例如:一共有500个样本数据;每个子模型只看100个样本数据每个子模型不需要太高的准确率

Bagging 和 Pasting

取样:放回取样，不放回取样

放回取样:Bagging 不放回取样:Pasting

Bagging 更常用

import numpy as np
import matplotlib.pyplot as plt

from sklearn import datasets

X, y = datasets.make_moons(n_samples=500, noise=0.3, random_state=42)

plt.scatter(X[y==0,0], X[y==0,1])
plt.scatter(X[y==1,0], X[y==1,1])
plt.show()


from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)

使用 Bagging

from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import BaggingClassifier

bagging_clf = BaggingClassifier(DecisionTreeClassifier(),
                           n_estimators=500, max_samples=100,
                           bootstrap=True)
bagging_clf.fit(X_train, y_train)
bagging_clf.score(X_test, y_test)


from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import BaggingClassifier

bagging_clf = BaggingClassifier(DecisionTreeClassifier(),
                           n_estimators=5000, max_samples=100,
                           bootstrap=True)
bagging_clf.fit(X_train, y_train)
bagging_clf.score(X_test, y_test)

OOB Out-of-Bag

放回取样导致一部分样本很有可能没有取到

平均大约有37%的样本没有取到。

不使用测试数据集，而使用这部分没有取到的样本做测试/验证

生成数据

import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets

X, y = datasets.make_moons(n_samples=500, noise=0.3, random_state=42) 
plt.scatter(X[y==0,0], X[y==0,1])
plt.scatter(X[y==1,0], X[y==1,1])
plt.show()

oob

from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import BaggingClassifier

bagging_clf = BaggingClassifier(DecisionTreeClassifier(),
                               n_estimators=500, max_samples=100,
                               bootstrap=True, oob_score=True)
bagging_clf.fit(X, y)

bagging_clf.oob_score_

Bagging的思路极易并行化处理

%%time
bagging_clf = BaggingClassifier(DecisionTreeClassifier(),
                               n_estimators=500, max_samples=100,
                               bootstrap=True, oob_score=True)
bagging_clf.fit(X, y)


%%time
bagging_clf = BaggingClassifier(DecisionTreeClassifier(),
                               n_estimators=500, max_samples=100,
                               bootstrap=True, oob_score=True,
                               n_jobs=-1)
bagging_clf.fit(X, y)

bootstrap_features

random_subspaces_clf = BaggingClassifier(DecisionTreeClassifier(),
                               n_estimators=500, max_samples=500,
                               bootstrap=True, oob_score=True,
                               max_features=1, bootstrap_features=True)
random_subspaces_clf.fit(X, y)
random_subspaces_clf.oob_score_


random_patches_clf = BaggingClassifier(DecisionTreeClassifier(),
                               n_estimators=500, max_samples=100,
                               bootstrap=True, oob_score=True,
                               max_features=1, bootstrap_features=True)
random_patches_clf.fit(X, y)
random_patches_clf.oob_score_

随机森林

Bagging

Base Estimator: Decision Tree

决策树在节点划分上，在随机的特征子集上寻找最优划分特征

import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets

X, y = datasets.make_moons(n_samples=500, noise=0.3, random_state=666)
plt.scatter(X[y==0,0], X[y==0,1])
plt.scatter(X[y==1,0], X[y==1,1])
plt.show()

随机森林

from sklearn.ensemble import RandomForestClassifier

rf_clf = RandomForestClassifier(n_estimators=500, oob_score=True, random_state=666, n_jobs=-1)
rf_clf.fit(X, y)

rf_clf.oob_score_


rf_clf2 = RandomForestClassifier(n_estimators=500, max_leaf_nodes=16, oob_score=True, random_state=666, n_jobs=-1)
rf_clf2.fit(X, y)
rf_clf2.oob_score_

Extra-Trees

Bagging

Base Estimator: Decision Tree

决策树在节点划分上，使用随机的特征和随机的阈值

提供额外的随机性，抑制过拟合，但增大了bias

更快的训练速度

from sklearn.ensemble import ExtraTreesClassifier

et_clf = ExtraTreesClassifier(n_estimators=500, bootstrap=True, oob_score=True, random_state=666, n_jobs=-1)
et_clf.fit(X, y)

et_clf.oob_score_

Boosting

集成多个模型

每个模型都在尝试增强(Boosting)整体的效果

Ada Boosting

import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets

X, y = datasets.make_moons(n_samples=500, noise=0.3, random_state=666) 
plt.scatter(X[y==0,0], X[y==0,1])
plt.scatter(X[y==1,0], X[y==1,1])
plt.show()


from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=666)

AdaBoosting

from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import AdaBoostClassifier

ada_clf = AdaBoostClassifier(
    DecisionTreeClassifier(max_depth=2), n_estimators=500)
ada_clf.fit(X_train, y_train)

ada_clf.score(X_test, y_test)

Gradient Boosting

训练一个模型m1,产生错误e1

针对e1训练第二个模型m2，产生错误e2

针对e2训练第三个模型m3，产生错误e3…

最终预测结果是:m1+m2+m3+…

from sklearn.ensemble import GradientBoostingClassifier

gb_clf = GradientBoostingClassifier(max_depth=2, n_estimators=30)
gb_clf.fit(X_train, y_train)

gb_clf.score(X_test, y_test)

Stacking


原文地址：https://blog.csdn.net/weixin_42403632/article/details/142797217

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