如何为 sklearn svm 塑造训练和测试数据答案

【问题标题】：How to shape train and test data for sklearn svm如何为 sklearn svm 塑造训练和测试数据
【发布时间】：2018-05-17 09:05:04
【问题描述】：

我正在使用 pandas 库来提取数据并使用它来提供 svc 分类器，如下所示：

from sklearn.svm import SVC
import pandas as pd

train = pd.read_csv('train.csv')
X_train = train['FunctionalWordPercent']
Y_train  = train['openness']

test = pd.read_csv('test.csv')
X_test = test['FunctionalWordPercent']
Y_test  = test['openness']

clf = SVC()
clf.fit(X_train, Y_train) 
SVC(kernel="linear", c=1.0)

print(clf.score(X_test,Y_test))

但我不断收到以下错误：

Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.

【问题讨论】：

标签： pandas scikit-learn svm

【解决方案1】：

看起来您正在使用x的单个列（功能）。对于此代码来工作，您需要执行以下操作：

X_train = train['FunctionalWordPercent']
X_train = X_train.reshape(-1,1)


X_test = test['FunctionalWordPercent']
X_test = X_test.reshape(-1,1)

【讨论】：