擅长:python、mysql、java
<p>鉴于这里的信息,不可能重建您的问题。但我很确定,这与数据的预处理/缩放有关。运行<code>SVR</code>的示例代码段可能如下所示(请随意调整以适应您的需要):</p>
<pre><code>from sklearn.svm import SVR
from sklearn.datasets import load_boston
from sklearn.preprocessing import StandardScaler
from sklearn.cross_validation import train_test_split
from sklearn.metrics import mean_squared_error
# replace this parth with your data, e.g. TrainingIn/TrainingOut
boston = load_boston()
X, y = boston.data, boston.target
X1, X2, y1, y2 = train_test_split(X, y)
svr = SVR(C=80)
scaler = StandardScaler()
svr.fit(scaler.fit_transform(X1), y1)
y_pred = svr.predict(scaler.transform(X2))
print mean_squared_error(y2, y_pred)
</code></pre>