Question

dataset name = faces

faces.data = independent variables

faces.target = dependent variable

from sklearn.svm import SVC
from sklearn.decomposition import PCA
from sklearn.pipeline import make_pipeline

pca = PCA(n_components=150, whiten=True, random_state=42)
svc = SVC(kernel="rbf", class_weight="balanced")
model = make_pipeline(pca, svc)

# spliting data from faces dataset. data is x and target is y
from sklearn.model_selection import train_test_split
Xtrain, Xtest, ytrain, ytest = train_test_split(faces.data, faces.target, random_state=42)

我已经为PCA和SVC创建了一个管道，然后将数据分为训练集和测试集。

# explore combinations of paramters
from sklearn.model_selection import GridSearchCV

param_grid = {'svc_C':[1,5,10,50],
             'svc_gamma':[0.0001, 0.0005, 0.001, 0.005]}
# instantiate grid of GridSearchCV class
# model uses pca to extract meaningful features then svc to find support vector

grid = GridSearchCV(model, param_grid)

grid.fit(Xtrain,ytrain)

当我尝试通过PCA和SVC后使用GridSearchCV训练数据时，它给我一个错误，提示"ValueError: Invalid parameter svc_C for estimator Pipeline"

任何提示？

Answer 1

我敢打赌，您的参数应该包括双底线，例如：

param_grid = {'svc__C':[1,5,10,50],
             'svc__gamma':[0.0001, 0.0005, 0.001, 0.005]}

使用GridSearchCV训练数据可以给我带来ValueError，Sci-kit学习

1 个答案: