机器学习实践 -- Seeds数据集

机器学习实践

数据获取

该数据可以从uci中下载，通过pandas将其读入

path = 'seeds_dataset.txt'
feature_names = ['area','perimeter','compactness','length-kernel','width-kernel','asymmetry-coefficient','length-kernel-groove']
seeds = pd.read_table(path, header=None, names=feature_names + ['class'], sep='\s+')
class_names = ['Kama', 'Rosa', 'Canadian']
seeds = pd.concat([seeds[feature_names], pd.DataFrame([class_names[i-1] for i in list(seeds['class'])],  columns=['class'])],axis=1)

清理分析

area    perimeter   compactness length-kernel   width-kernel    asymmetry-coefficient   length-kernel-groove
count   210.000000  210.000000  210.000000  210.000000  210.000000  210.000000  210.000000
mean    14.847524   14.559286   0.870999    5.628533    3.258605    3.700201    5.408071
std 2.909699    1.305959    0.023629    0.443063    0.377714    1.503557    0.491480
min 10.590000   12.410000   0.808100    4.899000    2.630000    0.765100    4.519000
25% 12.270000   13.450000   0.856900    5.262250    2.944000    2.561500    5.045000
50% 14.355000   14.320000   0.873450    5.523500    3.237000    3.599000    5.223000
75% 17.305000   15.715000   0.887775    5.979750    3.561750    4.768750    5.877000
max 21.180000   17.250000   0.918300    6.675000    4.033000    8.456000    6.550000

无缺失数据

可视化

violinplot

从图上未发现异常。

for column_index, column in enumerate(feature_names):
    if column_index%4 == 0:
        plt.figure()
    plt.subplot(2, 2, column_index%4 + 1)
    sb.violinplot(x='class', y=column, data=seeds)

pairplot

从数据中可以看到有几个维度是完全相关的。

类别分布

三类数据样本个数一样。

降维

PCA

PCA降维为2后的图如下

能量分布如下

ISOMap

ISO降维分布如下

分类训练

KNN

求解结果为

(0.90476190476190477,
 KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
            metric_params=None, n_jobs=1, n_neighbors=10, p=2,
            weights='distance'),
 {'n_neighbors': 10, 'weights': 'distance'})

决策树

求解结果为

(0.9285714285714286,
 DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=30,
             max_features=None, max_leaf_nodes=None, min_samples_leaf=2,
             min_samples_split=8, min_weight_fraction_leaf=0.0,
             presort=False, random_state=None, splitter='best'),
 {'criterion': 'gini',
  'max_depth': 30,
  'min_samples_leaf': 2,
  'min_samples_split': 8})

逻辑回归

求解结果为

(0.94761904761904758,
 LogisticRegression(C=10, class_weight=None, dual=False, fit_intercept=True,
           intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
           penalty='l2', random_state=None, solver='liblinear', tol=0.0001,
           verbose=0, warm_start=False),
 {'C': 10})

SVM

求解最佳结果为

(0.94761904761904758, SVC(C=1, cache_size=200, class_weight=None, coef0=0.0,
   decision_function_shape=None, degree=3, gamma='auto', kernel='poly',
   max_iter=-1, probability=False, random_state=None, shrinking=True,
   tol=0.001, verbose=False), {'C': 1, 'kernel': 'poly'})

随机森林

求解结果为

(0.92380952380952386,
 RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
             max_depth=None, max_features='auto', max_leaf_nodes=None,
             min_samples_leaf=1, min_samples_split=2,
             min_weight_fraction_leaf=0.0, n_estimators=150, n_jobs=1,
             oob_score=False, random_state=0, verbose=0, warm_start=False),
 {'n_estimators': 150})

汇总

从汇总数据上看，模型切合最好的是逻辑回归。

针对单次测试，可看到如下混淆矩阵，Kama和Rosa之间有误差。具体出现错误和样本选择划分测试集有关。

代码

Github： https://github.com/spiritwiki/codes/tree/master/data_seeds