数据挖掘 - task 3: 各类分类算法

深藏阁楼爱情的钟 2021-10-18 10:16 331阅读 0赞

### 前言 ###

用逻辑回归、svm和决策树；随机森林和XGBoost进行模型构建，评分方式任意，如准确率等。

### 决策树 ###

import pandas as pd
    import numpy as np
    from sklearn.model_selection import train_test_split
    from sklearn.tree import DecisionTreeClassifier
    from sklearn.model_selection import cross_val_score
    
    
    dataPath = 'D:\\Data\\dataAnalyse\\'
    dataFile = 'dataProcessByTwoTask.csv'
    data = pd.read_csv(dataPath+dataFile,encoding='gbk')
    
    train_data, test_data = train_test_split(data, test_size=0.3, random_state=2018)
    print("train_data",train_data.shape)
    print("test_data",test_data.shape)
    
    
    y_train = train_data['status']
    x_train = train_data.drop(['status'],axis =1)
    
    y_test = test_data['status']
    x_test = test_data.drop(['status'],axis =1)
    
    clf = DecisionTreeClassifier(max_depth=None, min_samples_split=2,random_state=0)
     # 训练样本
    scores_train = cross_val_score(clf, x_train, y_train)
    print('测试数据集准确率=',scores_train.mean())
    
    scores_test = cross_val_score(clf, x_test, y_test)
    print('测试数据集准确率=',scores_test.mean())

输出结果：

测试数据集准确率= 0.7012350276158706
    测试数据集准确率= 0.6691776554344605

### 随机森林 ###

import pandas as pd
    import numpy as np
    from sklearn.model_selection import train_test_split
    from sklearn.ensemble import RandomForestClassifier
    from sklearn.model_selection import cross_val_score
    
    
    dataPath = 'D:\\Data\\dataAnalyse\\'
    dataFile = 'dataProcessByTwoTask.csv'
    data = pd.read_csv(dataPath+dataFile,encoding='gbk')
    
    train_data, test_data = train_test_split(data, test_size=0.3, random_state=2018)
    print("train_data",train_data.shape)
    print("test_data",test_data.shape)
    
    
    y_train = train_data['status']
    x_train = train_data.drop(['status'],axis =1)
    
    y_test = test_data['status']
    x_test = test_data.drop(['status'],axis =1)
    
    clf = RandomForestClassifier(n_jobs=2)
     # 训练样本
    clf.fit(x_train, y_train)
    
    clf.predict(x_test)
    scores_train = cross_val_score(clf, x_train, y_train)
    print('测试数据集准确率=',scores_train.mean())
    
    scores_test = cross_val_score(clf, x_test, y_test)
    print('测试数据集准确率=',scores_test.mean())

输出结果：

测试数据集准确率= 0.7787475721945868
    测试数据集准确率= 0.7520794391727867

### XGBoost ###

import matplotlib.pyplot as plt
    from sklearn import datasets
    from sklearn.metrics import accuracy_score
    from xgboost import XGBClassifier
    from xgboost import plot_importance
    from sklearn import metrics
    
    model = XGBClassifier(learning_rate=0.01,
                          n_estimators=10,           # 树的个数-10棵树建立xgboost
                          max_depth=4,               # 树的深度
                          min_child_weight = 1,      # 叶子节点最小权重
                          gamma=0.,                  # 惩罚项中叶子结点个数前的参数
                          subsample=1,               # 所有样本建立决策树
                          colsample_btree=1,         # 所有特征建立决策树
                          scale_pos_weight=1,        # 解决样本个数不平衡的问题
                          random_state=27,           # 随机数
                          slient = 0
                          )
    model.fit(x_train,y_train)
    
    
    #预测
    y_pred = model.predict(x_test)
    print("Accuracy : %.4g" % metrics.accuracy_score(y_test, y_pred)) 
    y_train_proba = model.predict_proba(x_train)[:,1]
    print("AUC Score (Train): %f" % metrics.roc_auc_score(y_train, y_train_proba))
    y_proba = model.predict_proba(x_test)[:,1]
    print("AUC Score (Test): %f" % metrics.roc_auc_score(y_test, y_proba))

结果：

Accuracy : 0.7551
    AUC Score (Train): 0.807798
    AUC Score (Test): 0.756977

### 参考博客 ###

[XGBoost：在Python中使用XGBoost][XGBoost_Python_XGBoost]

[XGBoost_Python_XGBoost]: https://blog.csdn.net/zc02051126/article/details/46771793