将字符串数据转换为浮点数据,然后传递到支持向量机分类器

我的数据集如下:

X_data = 

BankNum   |  ID | 

00987772  | AB123 | 
00987772  | AB123 |
00987772  | AB123 |
00987772  | ED245 |
00982123  | GH564 |

y_data =

ID  | Labels

AB123 | High
ED245 | Low
GH564 | Low

我正在做以下工作:

from sklearn import svm
from sklearn import metrics
import numpy as np

clf = svm.SVC(gamma=0.001, C=100., probability=True)
X_train, X_test, y_train, y_test = train_test_split(X_data, y_data, test_size=0.20, random_state=42)
clf.fit(X_train, y_train)

predicted = clf.predict(X_test)

X_data 在我之前漂浮 clf.fit() ? 我能用吗 DictVectorizer 在这种情况下?如果是,那么我该如何使用它?

还有,我要通过 和 y_data 通过 train_test_split 为了找出预测的准确性,但它会被正确地分割吗?正如在采取正确的 Label ID 在里面 X_数据 从…起 y_数据

有人能告诉我我是否做得正确吗?

new_df = pd.merge(df, df3, on="ID")
columns = ['BankNum', 'ID']
labels = new_df['Labels']
le = LabelEncoder()
labels = le.fit_transform(labels)
X_train, X_test, y_train, y_test = train_test_split(new_df[columns], labels, test_size=0.25, random_state=42)
X_train.fillna( 'NA', inplace = True )
X_test.fillna( 'NA', inplace = True )
x_cat_train = X_train.to_dict( orient = 'records' )
x_cat_test = X_test.to_dict( orient = 'records' )
vectorizer = DictVectorizer( sparse = False )
vec_x_cat_train = vectorizer.fit_transform( x_cat_train )
vec_x_cat_test = vectorizer.transform( x_cat_test )
x_train = vec_x_cat_train
x_test = vec_x_cat_test
clf = svm.SVC(gamma=0.001, C=100., probability=True)
clf.fit(x_train, y_train)