第1个回答 2023-01-30
以下是一个使用scikit-learn库实现向量机多元回归预测的代码示例:
import numpy as np
import pandas as pd
from sklearn.svm import SVR
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
# load data
data = pd.read_csv("data.csv")
X = data.iloc[:, :-1].values
y = data.iloc[:, -1].values
# split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
# fit SVM regression model to training data
regressor = SVR(kernel='linear')
regressor.fit(X_train, y_train)
# predict on test data
y_pred = regressor.predict(X_test)
# evaluate the model
mse = mean_squared_error(y_test, y_pred)
print("Mean Squared Error:", mse)
第2个回答 2023-01-29
Python 代码示例,使用 scikit-learn 库中的 SVR 类实现多元回归预测:
from sklearn.svm import SVR
import numpy as np
# 构造训练数据
X = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
y = np.array([1, 2, 3])
# 创建模型并训练
clf = SVR(kernel='linear')
clf.fit(X, y)
# 进行预测
predictions = clf.predict(X)
print(predictions)
请注意,以上代码仅供参考,可能需要根据实际情况进行修改。