python – sklearn SVM表现非常糟糕

在How to split into train,test and evaluation sets in sklearn?之后,由于我的数据集是3个项目(id,vector,label)的元组,我这样做：

df = pd.DataFrame(dataset)
train,validate,test = np.split(df.sample(frac=1),[int(.6*len(df)),int(.8*len(df))])

train_labels = construct_labels(train)
train_data = construct_data(train)

test_labels = construct_labels(test)
test_data = construct_data(test)

def predict_labels(test_data,classifier):
    labels = []
    for test_d in test_data:
        labels.append(classifier.predict([test_d]))
    return np.array(labels)

def construct_labels(df):
    labels = []
    for index,row in df.iterrows():
        if row[2] == 'Trump':
            labels.append('Atomium')
        else:
            labels.append('Not Trump')
    return np.array(labels)

def construct_data(df):
    first_row = df.iloc[0]
    data = np.array([first_row[1]])
    for index,row in df.iterrows():
        if first_row[0] != row[0]:
            data = np.concatenate((data,np.array([row[1]])),axis=0)
    return data

然后：

>>> classifier = SVC(verbose=True)
>>> classifier.fit(train_data,train_labels)
[LibSVM].......*..*
optimization finished,#iter = 9565
obj = -2718.376533,rho = 0.132062
nSV = 5497,nBSV = 2550
Total nSV = 5497
SVC(C=1.0,cache_size=200,class_weight=None,coef0=0.0,decision_function_shape=None,degree=3,gamma='auto',kernel='rbf',max_iter=-1,probability=False,random_state=None,shrinking=True,tol=0.001,verbose=True)
>>> predicted_labels = predict_labels(test_data,classifier)
>>> for p,t in zip(predicted_labels,test_labels):
...     if p == t:
...             correct = correct + 1

并且我在1833年只得到正确的943个标签(= len(test_labels)) – > (943 * 100/1843 = 51.4％)

我怀疑我在这里错过了一些重要的时间,也许我应该将parameter设置为分类器以进行更精致的工作或其他什么？

注意：第一次在这里使用SVM,所以你可能认为理所当然,我甚至可能没想到……

尝试：

我去了,并将负面例子的数量减少到4303(与正例相同).这略微提高了准确性.

在答案后编辑：

>>> print(clf.best_estimator_)
SVC(C=1000.0,class_weight='balanced',gamma=0.0001,verbose=False)
>>> classifier = SVC(C=1000.0,...   decision_function_shape=None,...   max_iter=-1,...   tol=0.001,verbose=False)
>>> classifier.fit(train_data,train_labels)
SVC(C=1000.0,verbose=False)

我也尝试了clf.fit(train_data,train_labels),它也执行了相同的操作.

用数据编辑(数据不是随机的)：

>>> train_data[0]
array([  20.21062112,27.924016,137.13815308,130.97432804,... # there are 256 coordinates in total
         67.76352596,56.67798138,104.89566517,10.02616417])
>>> train_labels[0]
'Not Trump'
>>> train_labels[1]
'Trump'