如何在使用sklearn库时，在逻辑回归模型中使用核函数？

logreg = LogisticRegression()logreg.fit(X_train, y_train)y_pred = logreg.predict(X_test)print(y_pred)print(confusion_matrix(y_test,y_pred))print(classification_report(y_test,y_pred))predicted= logreg.predict(predict)print("Accuracy:",metrics.accuracy_score(y_test, y_pred))

回答：

这个问题问得很好，但目前scikit-learn不支持核逻辑回归和ANOVA核函数。

不过，您可以自己实现它。

ANOVA核函数的示例1：

import numpy as npfrom sklearn.metrics.pairwise import check_pairwise_arraysfrom scipy.linalg import choleskyfrom sklearn.linear_model import LogisticRegressiondef anova_kernel(X, Y=None, gamma=None, p=1):    X, Y = check_pairwise_arrays(X, Y)    if gamma is None:        gamma = 1. / X.shape[1]    diff = X[:, None, :] - Y[None, :, :]    diff **= 2    diff *= -gamma    np.exp(diff, out=diff)    K = diff.sum(axis=2)    K **= p    return K# 基于所有数据点的X矩阵的核矩阵K = anova_kernel(X)R = cholesky(K, lower=False)# 定义模型clf = LogisticRegression()# 假设您已经分割了数据，这里train是训练集的索引clf.fit(R[train], y_train)preds = clf.predict(R[test])¨

Nyström方法的示例2：

from sklearn.kernel_approximation import Nystroemfrom sklearn.linear_model import LogisticRegressionfrom sklearn.pipeline import PipelineK_train = anova_kernel(X_train)clf = Pipeline([    ('nys', Nystroem(kernel='precomputed', n_components=100)),    ('lr', LogisticRegression())])clf.fit(K_train, y_train)K_test = anova_kernel(X_test, X_train)preds = clf.predict(K_test)