python - ValueError:在实现 sklearn 时无法将字符串转换为浮点数
问题描述
我是新来的,我遇到了这样的错误;
File "C:\Users\Himanshu\Desktop\Project\ML\Pract\mlp1.py", line 268, in
<module> cv_results = model_selection.cross_val_score(model, X_train, Y_train,
cv=kfold, scoring=scoring)
File "C:\Users\Himanshu\AppData\Roaming\Python\Python27\site-
packages\sklearn\model_selection\_validation.py", line 402, in cross_val_score
error_score=error_score)
.
.
etc..like above)
ValueError: could not convert string to float: transact
我使用的数据集的形状是 (30,216)
array = dataset.values
X = array[:,0:215]
Y = array[:,215]
validation_size = 0.20
seed = 7
X_train, X_validation, Y_train, Y_validation =
model_selection.train_test_split(X, Y, test_size=validation_size,
random_state=seed)
我想知道,我是否正确拆分它。有人可以建议为什么会发生此错误。
编辑: 我正在添加其余代码:
scoring = 'accuracy'
# Spot Check Algorithms
models = []
models.append(('LR', LogisticRegression()))
models.append(('LDA', LinearDiscriminantAnalysis()))
models.append(('KNN', KNeighborsClassifier()))
models.append(('CART', DecisionTreeClassifier()))
models.append(('NB', GaussianNB()))
models.append(('SVM', SVC()))
# evaluate each model in turn
results = []
names = []
for name, model in models:
kfold = model_selection.KFold(n_splits=10, random_state=seed)
cv_results = model_selection.cross_val_score(model, X_train, Y_train,
cv=kfold, scoring=scoring)
results.append(cv_results)
names.append(name)
msg = "%s: %f (%f)" % (name, cv_results.mean(), cv_results.std())
print(msg)
解决方案
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