机器学习|机器学习项目实战——11集成学习算法之泰坦尼克号船员获救预测机器学习|机器学习

数据集采用的是kaggle比赛中公开的数据集——泰坦尼克号
对之前的机器学习方法分别进行了预测。
包括：逻辑回归0.7901234567901234、
神经网络0.7878787878787877、
KNN0.8125701459034792、
决策树0.8080808080808081、
随机森林0.7991021324354657、0.8181818181818182
Bagging0.8282828282828283、和随机森林做集成
Adaboost???????0.8181818181818182、和bagging做集成
Stacking0.8125701459034792

整体代码：

import pandastitanic = pandas.read_csv("titanic_train.csv")# 空余的age填充整体age的中值 titanic["Age"] = titanic["Age"].fillna(titanic["Age"].median()) print(titanic.describe())print(titanic["Sex"].unique())# 把male变成0，把female变成1 titanic.loc[titanic["Sex"] == "male", "Sex"] = 0 titanic.loc[titanic["Sex"] == "female", "Sex"] = 1print(titanic["Embarked"].unique()) # 数据填充 titanic["Embarked"] = titanic["Embarked"].fillna('S') # 把类别变成数字 titanic.loc[titanic["Embarked"] == "S", "Embarked"] = 0 titanic.loc[titanic["Embarked"] == "C", "Embarked"] = 1 titanic.loc[titanic["Embarked"] == "Q", "Embarked"] = 2from sklearn.preprocessing import StandardScaler# 选定特征 predictors = ["Pclass", "Sex", "Age", "SibSp", "Parch", "Fare", "Embarked"] x_data = https://www.it610.com/article/titanic[predictors] y_data = titanic["Survived"]# 数据标准化 scaler = StandardScaler() x_data = https://www.it610.com/article/scaler.fit_transform(x_data)# 逻辑回归 from sklearn import model_selection from sklearn.linear_model import LogisticRegression # 逻辑回归模型 LR = LogisticRegression() # 计算交叉验证的误差 scores = model_selection.cross_val_score(LR, x_data, y_data, cv=3) # 求平均 print(scores.mean()) # 0.7901234567901234# 神经网络模型 from sklearn.neural_network import MLPClassifier # 建模 mlp = MLPClassifier(hidden_layer_sizes=(20,10),max_iter=1000) # 计算交叉验证的误差 scores = model_selection.cross_val_score(mlp, x_data, y_data, cv=3) # 求平均 print(scores.mean()) # 0.7878787878787877# KNN模型 from sklearn import neighbors knn = neighbors.KNeighborsClassifier(21) # 计算交叉验证的误差 scores = model_selection.cross_val_score(knn, x_data, y_data, cv=3) # 求平均 print(scores.mean()) # 0.8125701459034792# 决策树模型 from sklearn import tree # 决策树模型 dtree = tree.DecisionTreeClassifier(max_depth=5, min_samples_split=4) # 计算交叉验证的误差 scores = model_selection.cross_val_score(dtree, x_data, y_data, cv=3) # 求平均 print(scores.mean()) # 0.8080808080808081# 随机森林模型 from sklearn.ensemble import RandomForestClassifier RF1 = RandomForestClassifier(random_state=1, n_estimators=10, min_samples_split=2) # 计算交叉验证的误差 scores = model_selection.cross_val_score(RF1, x_data, y_data, cv=3) # 求平均 print(scores.mean()) # 0.7991021324354657RF2 = RandomForestClassifier(n_estimators=100, min_samples_split=4) # 计算交叉验证的误差 scores = model_selection.cross_val_score(RF2, x_data, y_data, cv=3) # 求平均 print(scores.mean()) # 0.8181818181818182# Bagging from sklearn.ensemble import BaggingClassifier bagging_clf = BaggingClassifier(RF2, n_estimators=20) # 计算交叉验证的误差 scores = model_selection.cross_val_score(bagging_clf, x_data, y_data, cv=3) # 求平均 print(scores.mean()) # 0.8282828282828283# AdaBoost模型 from sklearn.ensemble import AdaBoostClassifier # AdaBoost模型 adaboost = AdaBoostClassifier(bagging_clf,n_estimators=10) # 计算交叉验证的误差 scores = model_selection.cross_val_score(adaboost, x_data, y_data, cv=3) # 求平均 print(scores.mean()) # 0.8181818181818182# Stacking from sklearn.ensemble import VotingClassifier from mlxtend.classifier import StackingClassifiersclf = StackingClassifier(classifiers=[bagging_clf, mlp, LR], meta_classifier=LogisticRegression())sclf2 = VotingClassifier([('adaboost',adaboost), ('mlp',mlp), ('LR',LR),('knn',knn),('dtree',dtree)])# 计算交叉验证的误差 scores = model_selection.cross_val_score(sclf2, x_data, y_data, cv=3) # 求平均 print(scores.mean()) # 0.8125701459034792

【机器学习|机器学习项目实战——11集成学习算法之泰坦尼克号船员获救预测】