Question

在训练我的SVM分类器之前，我已经对我的数据集使用了一个热编码。这使得训练中的特征数量增加到982个。但是在期间测试数据集的预测有7个功能我得到错误＆＃34; X有7 每个样本的特征;期待982＆＃34;。我不明白如何增加测试数据集中的功能数量。

My code is:
df = pd.read_csv('train.csv',header=None);
features = df.iloc[:,:-1].values
labels = df.iloc[:,-1].values
encode = LabelEncoder()
features[:,2] = encode.fit_transform(features[:,2])
features[:,3] = encode.fit_transform(features[:,3])
features[:,4] = encode.fit_transform(features[:,4])
features[:,5] = encode.fit_transform(features[:,5])

df1 = pd.DataFrame(features)
#--------------------------- ONE HOT ENCODING --------------------------------#

hotencode = OneHotEncoder(categorical_features=[2])
features = hotencode.fit_transform(features).toarray()
hotencode = OneHotEncoder(categorical_features=[14])
features = hotencode.fit_transform(features).toarray()
hotencode = OneHotEncoder(categorical_features=[37])
features = hotencode.fit_transform(features).toarray()
hotencode = OneHotEncoder(categorical_features=[466])
features = hotencode.fit_transform(features).toarray()
X = np.array(features)
y = np.array(labels)

clf = svm.LinearSVC()
clf.fit(X,y)
d_test = pd.read_csv('query.csv')
Z_test =np.array(d_test)
confidence = clf.predict(Z_test)
print("The query image belongs to Class ")
print(confidence)

######################### test dataset
query.csv
1   0.076   1   3232236298  2886732679  3128    60604

Answer 1

简短回答：您需要在测试集上应用相同的OHE变换（或者在您的情况下为LE + OHE）。

要获得好的建议，请参阅Scikit Learn OneHotEncoder fit and transform Error: ValueError: X has different shape than during fitting或How to deal with imputation and hot one encoding in pandas?

一次热编码后svm分类器中的预测错误

1 个答案: