决策树判断图像是否为广告

在数据挖掘中，决策树是最常见的基础模型。很多优秀的模型都是基于决策树改进而来。决策树的原理网上有很多的介绍，不在此赘述。

从http://archive.ics.uci.edu/ml/datasets/Internet+Advertisements下载Advertisements（广告）数据集

。数据集所描述的是网上的图像，目标是确定图像是不是广告。

前三 个特征分别指图像的高度、宽度和宽高比。最后一列是数据的类别，1表示是广告，0表示不是广告 。在前面的文章《python核密度估计(KernelDensity)》点击打开链接。我们已经对数据集的分布进行了分析。下面依然把处理的代码放上来。

#--coding:utf-8import warningswarnings.filterwarnings("ignore")import numpy as npimport pandas as pdimport osfrom sklearn.neighbors import KernelDensityimport matplotlib.pyplot as pltimport mpl_toolkits.mplot3dfilepath = os.path.join("../dataset/Internet Advertisements/Data Folder","ad.data")#对DataFrame的列做数据转换def Converter_number(x):    try:        return np.float64(x)    except ValueError:        return np.nan#字典推导式converters = {key:Converter_number for key in range(1558)}converters[1558] = lambda x: 1 if x.strip()=='ad.' else 0ads = pd.read_csv(filepath,header=None,converters=converters)print(ads[:5])#      0      1       2     3     4     5     6     7     8     9     ...   1549  \# 0  125.0  125.0  1.0000   1.0   0.0   0.0   0.0   0.0   0.0   0.0  ...    0.0# 1   57.0  468.0  8.2105   1.0   0.0   0.0   0.0   0.0   0.0   0.0  ...    0.0# 2   33.0  230.0  6.9696   1.0   0.0   0.0   0.0   0.0   0.0   0.0  ...    0.0# 3   60.0  468.0  7.8000   1.0   0.0   0.0   0.0   0.0   0.0   0.0  ...    0.0# 4   60.0  468.0  7.8000   1.0   0.0   0.0   0.0   0.0   0.0   0.0  ...    0.0##    1550  1551  1552  1553  1554  1555  1556  1557  1558# 0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0     1# 1   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0     1# 2   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0     1# 3   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0     1# 4   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0     1## [5 rows x 1559 columns]#首先去掉空值，并查看数据的统计ads = ads.dropna(axis=0)print(ads.describe())#我们可以看到，大量的特征的25%分位数显示，特征分布#              0            1            2            3            4     \# count  2359.000000  2359.000000  2359.000000  2359.000000  2359.000000# mean     63.912251   155.631624     3.912982     0.759644     0.002120# std      54.881130   130.237867     6.047220     0.427390     0.045999# min       1.000000     1.000000     0.001500     0.000000     0.000000# 25%      25.000000    80.500000     1.033450     1.000000     0.000000# 50%      51.000000   110.000000     2.111100     1.000000     0.000000# 75%      84.000000   184.000000     5.333300     1.000000     0.000000# max     640.000000   640.000000    60.000000     1.000000     1.000000##          5            6            7            8            9     \# count  2359.0  2359.000000  2359.000000  2359.000000  2359.000000# mean      0.0     0.006359     0.004663     0.004663     0.014837# std       0.0     0.079504     0.068141     0.068141     0.120925# min       0.0     0.000000     0.000000     0.000000     0.000000# 25%       0.0     0.000000     0.000000     0.000000     0.000000# 50%       0.0     0.000000     0.000000     0.000000     0.000000# 75%       0.0     0.000000     0.000000     0.000000     0.000000# max       0.0     1.000000     1.000000     1.000000     1.000000##           ...              1549         1550         1551         1552  \# count     ...       2359.000000  2359.000000  2359.000000  2359.000000# mean      ...          0.003815     0.001272     0.002120     0.002543# std       ...          0.061662     0.035646     0.045999     0.050379# min       ...          0.000000     0.000000     0.000000     0.000000# 25%       ...          0.000000     0.000000     0.000000     0.000000# 50%       ...          0.000000     0.000000     0.000000     0.000000# 75%       ...          0.000000     0.000000     0.000000     0.000000# max       ...          1.000000     1.000000     1.000000     1.000000##               1553         1554         1555        1556         1557  \# count  2359.000000  2359.000000  2359.000000  2359.00000  2359.000000# mean      0.008478     0.013989     0.014837     0.00975     0.000848# std       0.091705     0.117470     0.120925     0.09828     0.029111# min       0.000000     0.000000     0.000000     0.00000     0.000000# 25%       0.000000     0.000000     0.000000     0.00000     0.000000# 50%       0.000000     0.000000     0.000000     0.00000     0.000000# 75%       0.000000     0.000000     0.000000     0.00000     0.000000# max       1.000000     1.000000     1.000000     1.00000     1.000000##               1558# count  2359.000000# mean      0.161509# std       0.368078# min       0.000000# 25%       0.000000# 50%       0.000000# 75%       0.000000# max       1.000000## [8 rows x 1559 columns]
df_all = ads.valuesX = df_all[:,:-1]y = df_all[:,-1]from sklearn.tree import DecisionTreeClassifierfrom sklearn.cross_validation import cross_val_scoreclf = DecisionTreeClassifier(random_state=14)#交叉验证scores = cross_val_score(clf,X,y,scoring='accuracy')
print("Accuracy:{0:.1f}%".format(np.mean(scores)*100))   #Accuracy:93.3%
if __name__ == "__main__":    print("OK")

  简单的利用python自带的决策树，我们就得到了93.3%的准确率。后面我们会尝试使用多种方法来提升准确率。包括特征选择，变换模型等。