决策树、随机森林 用泰坦尼克号事件的数据集练习一下决策树和随机森林的API

# sklearn-DecisionTree 决策树、随机森林 用泰坦尼克号事件的数据集练习一下决策树和随机森林的API。 # 分析数据集信息 先读入数据集，看看有哪些特征： ``` import pandas as pd titanic = pd.read_csv("titanic.csv") # 分析数据集信息 print("*" * 30 + " info " + "*" * 30) print(titanic.info()) print(titanic.head()) ``` 输出： ``` ****************************** info ****************************** <class 'pandas.core.frame.DataFrame'> RangeIndex: 891 entries, 0 to 890 Data columns (total 12 columns): PassengerId 891 non-null int64 Survived 891 non-null int64 Pclass 891 non-null int64 Name 891 non-null object Sex 891 non-null object Age 714 non-null float64 SibSp 891 non-null int64 Parch 891 non-null int64 Ticket 891 non-null object Fare 891 non-null float64 Cabin 204 non-null object Embarked 889 non-null object dtypes: float64(2), int64(5), object(5) memory usage: 83.6+ KB None PassengerId Survived Pclass ... Fare Cabin Embarked 0 1 0 3 ... 7.2500 NaN S 1 2 1 1 ... 71.2833 C85 C 2 3 1 3 ... 7.9250 NaN S 3 4 1 1 ... 53.1000 C123 S 4 5 0 3 ... 8.0500 NaN S [5 rows x 12 columns] ``` `survived`字段表示是否生存，我们以此作为预测目标。 ``` y = titanic['survived'] print(y.head()) ``` 输出： ``` 0 0 1 1 2 1 3 1 4 0 Name: survived, dtype: int64 ``` 我们取其中三个特征做分析演示，分别是： - pclass：1-一等舱，2-二等舱，3-三等舱 - age年龄 - sex性别 ``` x = titanic[['pclass', 'age', 'sex']] print(x.info()) print(x.head()) ``` 输出： ``` <class 'pandas.core.frame.DataFrame'> RangeIndex: 891 entries, 0 to 890 Data columns (total 3 columns): Pclass 891 non-null int64 Age 714 non-null float64 Sex 891 non-null object dtypes: float64(1), int64(1), object(1) memory usage: 21.0+ KB None Pclass Age Sex 0 3 22.0 male 1 1 38.0 female 2 3 26.0 female 3 1 35.0 female 4 3 35.0 male ``` # 缺失值处理 age字段存在缺失，用均值填充： ``` age_mean = x['age'].mean() print("*" * 30 + " age_mean " + "*" * 30) print(age_mean) x['age'].fillna(age_mean, inplace=True) print("*" * 30 + " 处理age缺失值后 " + "*" * 30) print(x.info()) ``` 输出： ``` ****************************** age_mean ****************************** 29.69911764705882 ****************************** 处理age缺失值后 ****************************** <class 'pandas.core.frame.DataFrame'> RangeIndex: 891 entries, 0 to 890 Data columns (total 3 columns): Pclass 891 non-null int64 Age 891 non-null float64 Sex 891 non-null object dtypes: float64(1), int64(1), object(1) memory usage: 21.0+ KB ``` # 特征抽取 - onehot编码 为了方便使用字典特征抽取，构造字典列表： ``` x_dict_list = x.to_dict(orient='records') print("*" * 30 + " train_dict " + "*" * 30) print(pd.Series(x_dict_list[:5])) dict_vec = DictVectorizer(sparse=False) x = dict_vec.fit_transform(x_dict_list) print("*" * 30 + " onehot编码 " + "*" * 30) print(dict_vec.get_feature_names()) print(x[:5]) ``` ``` ****************************** train_dict ****************************** 0 {'Pclass': 3, 'Age': 22.0, 'Sex': 'male'} 1 {'Pclass': 1, 'Age': 38.0, 'Sex': 'female'} 2 {'Pclass': 3, 'Age': 26.0, 'Sex': 'female'} 3 {'Pclass': 1, 'Age': 35.0, 'Sex': 'female'} 4 {'Pclass': 3, 'Age': 35.0, 'Sex': 'male'} dtype: object ****************************** onehot编码 ****************************** ['Age', 'Pclass', 'Sex=female', 'Sex=male'] [[22. 3. 0. 1.] [38. 1. 1. 0.] [26. 3. 1. 0.] [35. 1. 1. 0.] [35. 3. 0. 1.]] ``` # 划分训练集和测试集 ``` x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25) ``` # 决策树分类器 ``` dec_tree = DecisionTreeClassifier() dec_tree.fit(x_train, y_train) print("*" * 30 + " 准确率 " + "*" * 30) print(dec_tree.score(x_test, y_test)) ``` 输出： ``` ****************************** 准确率 ****************************** 0.7892376681614349 ``` # 随机森林分类器 - n_jobs: -1表示设置为核心数量 - n_estimators: 决策树数目 - max_depth: 树最大深度 同时使用网格搜索最优超参数： ``` rf = RandomForestClassifier(n_jobs=-1) param = { "n_estimators": [120, 200, 300, 500, 800, 1200], "max_depth": [5, 8, 15, 25, 30] } # 2折交叉验证 search = GridSearchCV(rf, param_grid=param, cv=2) print("*" * 30 + " 超参数网格搜索 " + "*" * 30) start_time = time.time() search.fit(x_train, y_train) print("耗时：{}".format(time.time() - start_time)) print("最优参数：{}".format(search.best_params_)) print("*" * 30 + " 准确率 " + "*" * 30) print(search.score(x_test, y_test)) ``` 输出： ``` ****************************** 超参数网格搜索 ****************************** 耗时：66.85670185089111 最优参数：{'max_depth': 5, 'n_estimators': 120} ****************************** 准确率 ****************************** 0.7847533632286996 ``` 最优的参数是`{'max_depth': 5, 'n_estimators': 120}`。 在我的2015款MacBookPro上，仅2折的交叉验证就跑了66秒。- -|