集成模型python实现，随机森林，梯度提升决策树

最新推荐文章于 2024-07-15 15:30:12 发布

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最新推荐文章于 2024-07-15 15:30:12 发布 · 3.3k 阅读

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这篇博客介绍了如何使用Python进行集成模型的实现，具体包括随机森林和梯度提升决策树。首先，从Titanic数据集中加载数据并进行预处理，然后利用DictVectorizer转换特征。接着，训练了决策树模型，再分别训练随机森林和梯度提升决策树模型，并评估了它们在测试集上的预测准确性。最后，通过classification_report展示了各个模型的性能指标。

import pandas as pd;

titanic = pd.read_csv('http://biostat.mc.vanderbilt.edu/wiki/pub/Main/DataSets/titanic.txt')

X = titanic[['pclass', 'age', 'sex']]
y = titanic['survived']

X['age'].fillna(X['age'].mean(), inplace = True)

from sklearn.cross_validation import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 33)

from sklearn.feature_extraction import DictVectorizer

vec = DictVectorizer(sparse = False)

X_train = vec.fit_transform(X_train.to_dict(orient = 'record'))
X_test = vec.transform(X_test.to_dict(orient = 'record'))

from sklearn.tree import DecisionTreeClassifier

dtc = DecisionTreeClassifier()
dtc.fit(X_train, y_train)
dtc_y_pred = dtc.predict(X_test)

from sklearn.ensemble import RandomForestClassifier

rfc = RandomForestClassifier()
rfc.fit(X_train, y_train)
rfc_y_pred = rfc.predict(X_test)

from sklearn.ensemble import GradientBoostingClassifier

gbc = GradientBoostingClassi