MATLAB中混淆矩阵函数的使用如下: plotconfusion(targets,outputs,name) 绘制一个混淆矩阵,并将名称添加到绘图标题的开头。targets为已知的类别标签,outputs是网络的分类结果。name是图片的名称,输出的名字是“name Confusion Matrix”。 plotconfusion(targets1,outputs1,name1,targets2,outputs2,name2,…,targetsn,outpu...
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接下来,我们将数据集划分为训练集和测试集,并使用线性核的SVC分类器进行训练。最后,我们调用plot_decision_boundary函数绘制决策边界。除了plot_decision_boundary函数外,我们还可以使用plt.contourf函数来绘制决策边界。这个函数可以绘制等高线图,通过调整等高线的颜色和透明度,我们可以得到类似于决策边界的效果。具体用法如下...
plot_decision_regions函数的应用 plot_decision_regions函数的应⽤ plot_decision_regions(X, y, clf, feature_index=None, filler_feature_values=None, filler_feature_ranges=None, ax=None,X_highlight=None, res=None, legend=1, hide_spines=True, markers='s^oxv<>',colors='#1f77b4,#ff7f0e,#3...
(X, y, random_state=1) # 训练模型 svm_clf = SVC(random_state=0) svm_clf.fit(X_train, y_train) # 计算ROC曲线的值 fpr, tpr, thresholds = roc_curve(y_test, svm_clf.decision_function(X_test)) # 使用RocCurveDisplay绘制ROC曲线 display = RocCurveDisplay(svm_clf, X_test, y_test)...
指定是使用predict_proba 还是decision_function 作为目标响应。如果设置为‘auto’,则首先尝试predict_proba,如果不存在,则接下来尝试decision_function。 name:str,默认=无 用于标注的 ROC 曲线的名称。如果 None ,使用估计器的名称。 ax:matplotlib 轴,默认=无 要绘制的轴对象。如果 None ,则创建一个新的图形和...
Note while plotting the decision function of classifiers for toy 2D datasets can help get an intuitive understanding of their respective expressive power, be aware that those intuitions don’t always generalize to more realistic high-dimensional problems....
% decision boundary image that is used as the plot background. image_size = size(x1); xy = [x1(:) x2(:)]; % make (x,y) pairs as a bunch of row vectors. % set up the domain over which you want to visualize the decision % boundary % d = []; % for k=1:max(y) % par...
Z = clf.decision_function(np.c_[XX.ravel(), YY.ravel()]) # Put the result into a color plot Z = Z.reshape(XX.shape) plt.pcolormesh(XX, YY, Z > 0, cmap=plt.cm.Paired) plt.contour(XX, YY, Z, colors=['k', 'k', 'k'], linestyles=['--', '-', '--'], levels=[...
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