sklearn.datasets.make_classification( n_samples=100,# 样本个数n_features=20,# 特征个数n_informative=2,# 有效特征个数n_redundant=2,# 冗余特征个数(有效特征的随机组合)n_repeated=0,# 重复特征个数(有效特征和冗余特征的随机组合)n_classes=2,# 样本类别n_clusters_per_class=2,# 蔟的个数weights...
This illustrates the `datasets.make_multilabel_classification` dataset generator. Each sample consists of counts of two features (up to 50 in total), which are differently distributed in each of two classes.Points are labeled as follows, where Y means the class is present: 【数据集生成器“data...
deftest_normalize_option_multilabel_classification():# Test in the multilabel casen_classes =4n_samples =100_, y_true =make_multilabel_classification(n_features=1, n_classes=n_classes, random_state=0, n_samples=n_samples) _, y_pred =make_multilabel_classification(n_features=1, n_classes...
x, y = make_multilabel_classification(n_samples=1000, n_features=10, n_classes=3, n_labels=1, random_state=0) y = y.sum(axis=1) x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=0, test_size=0.33) ...
多标签多分类(Multi-Label Classification)问题,指一个样本(一个图片或者一个检测框)中含有多个物体或者多个label。在深度学习中,使用多个Logistic输出是一种性价比很高的做法。 YOLOv3将YOLOv2的单标签分类改进为多标签分类,Head侧将用于单标签分类的Softmax分类器改成多个独立的用于多标签分类的Logistic分类器,取消了...
示例6: test_label_spreading_closed_form ▲点赞 6▼ # 需要导入模块: from sklearn import datasets [as 别名]# 或者: from sklearn.datasets importmake_classification[as 别名]deftest_label_spreading_closed_form():n_classes =2X, y =make_classification(n_classes=n_classes, n_samples=200, ...
make_classification:可以在模拟数据中添加噪声 输入参数: sklearn.datasets.samples_generator.make_classification(n_samples=100, n_features=20, n_informative=2, n_redundant=2, n_repeated=0, n_classes=2, n_clusters_per_class=2, weights=None, flip_y=0.01, class_sep=1.0, hypercube=True, shift=...
多标签多分类(Multi-Label Classification)问题,指一个样本(一个图片或者一个检测框)中含有多个物体或者多个label。在深度学习中,使用多个Logistic输出是一种性价比很高的做法。 YOLOv3将YOLOv2的单标签分类改进为多标签分类,Head侧将用于单标签分类的Softmax分类器改成多个独立的用于多标签分类的Logistic分类器,取消了...
多标签多分类(Multi-Label Classification)问题,指一个样本(一个图片或者一个检测框)中含有多个物体或者多个label。在深度学习中,使用多个Logistic输出是一种性价比很高的做法。 YOLOv3将YOLOv2的单标签分类改进为多标签分类,Head侧将用于单标签分类的Softmax分类器改成多个独立的用于多标签分类的Logistic分类器,取消了...
(X)# We need to make sure that we have non negative data, for things# like NMFX -= X.min() -.1y_names = np.array(["one","two","three"])[y]fory_namesin[y_names, y_names.astype('O')]:ifnamein["LabelPropagation","LabelSpreading"]:# TODO some complication with -1 labely...