Python 如何使用线性支持向量机的拟合模型进行人工预测?
scikit.learn函数。从库线性预测RSVC使用测试样本执行预测Python 如何使用线性支持向量机的拟合模型进行人工预测?,python,classification,svm,prediction,Python,Classification,Svm,Prediction,scikit.learn函数。从库线性预测RSVC使用测试样本执行预测 LinearSVM_cl.fit(X_train , Y_train) 预测结果与实际情况相符 Y_pred_LinearSVM = LinearSVM_cl.predict(X_test) 但是,我需要知道拟合模型中的哪些参数用于预测测试样本.coef_2;。拦截 该模型的数据集为20000行8列,包含8个类: .coef-> array([[-1.20185887, -0.62510767, -0.92739
LinearSVM_cl.fit(X_train , Y_train)
预测结果与实际情况相符
Y_pred_LinearSVM = LinearSVM_cl.predict(X_test)
但是,我需要知道拟合模型中的哪些参数用于预测测试样本.coef_2;。拦截
该模型的数据集为20000行8列,包含8个类:
.coef->
array([[-1.20185887, -0.62510767, -0.92739275, -0.08900084, -1.11164502,
-0.56442702, 1.92045989, -0.56706939],
[ 0.75386897, 0.9672828 , -2.10451063, 0.53552943, -0.10476675,
0.32058617, -0.30133408, -1.01478727],
[ 0.35032536, -0.38405342, 0.25462054, 0.47577302, -0.55000734,
0.01134098, -0.14534849, 1.14597475],
[-0.08888566, -0.08272116, 0.84141105, 0.22040919, 0.27763948,
0.57907834, -0.70631803, -0.1017982 ],
[ 0.14319018, 0.03329494, 1.52575489, 0.58355648, 1.24454465,
-0.92758526, 1.01315744, -0.51935599],
[-0.33712774, -0.7826993 , -1.00810522, 0.20346304, 3.67215014,
0.93187058, -0.26441527, -0.5351838 ],
[-0.70416157, -2.38388785, -1.24720653, 0.43291862, 3.91473792,
2.7596399 , -0.63503461, -0.43277051],
[-0.14921538, -0.03871313, -0.19896247, 0.08522851, 0.29347373,
0.1332059 , -0.10875692, -0.01503476]])
.截取->
array([-0.43454897, 0.05659295, -0.95980815, -1.36353241, -3.05042133,
-2.93684622, -3.35757856, -1.14034588])
并以试验样品为例进行了分析
0.7622999 0.514543 0.2195486 0.453202 0.2585706 0.6295224 0.4999675 0.1960128
如何手动预测测试样本(无需使用库中的build.predict函数)。请注意您的
coef
为$W$,您的intercept
为$b$,您的新数据点为$x$。你的班级预测很简单:
$c=\arg\max_i{W_i\cdot x+b}$
所以你只需要应用矩阵乘法,加上偏差向量,然后选择最大项的索引