首先我们使用sklearn里的compute_sample_weight函数来计算sample_weight: sw = compute_sample_weight(class_weight='balanced',y=y_true) sw是一个和ytrue的shape相同的数据,每一个数代表该样本所在的sample_weight。它的具体计算方法是总样本数/(类数*每个类的个数),比如一个值为-1的样本,它的sample_weight...
首先我们使用sklearn里的compute_sample_weight函数来计算sample_weight: sw = compute_sample_weight(class_weight='balanced',y=y_true) sw是一个和ytrue的shape相同的数据,每一个数代表该样本所在的sample_weight。它的具体计算方法是总样本数/(类数*每个类的个数),比如一个值为-1的样本,它的sample_weight...
utils.check_random_state(种子) 将种子转换为np.random.RandomState实例 utils.class_weight.compute_class_weight(...) 估计不平衡数据集的类权重。 utils.class_weight.compute_sample_weight(...) 对于不平衡的数据集,按类别估算样本权重。 utils.deprecated([额外]) 装饰器,用于将功能或类标记为不推荐使用。
class_weights = class_weight.compute_class_weight( class_weight ='balanced', classes =np.unique(y_train), y =y_train.flatten()) Type: module String form: <module 'sklearn.utils.class_weight' from '/home/software/anaconda3/envs/tf115/lib/python3.7/site-packages/sklearn/utils/class_weigh...
compute_class_weight这个函数的作用是对于输入的样本,平衡类别之间的权重,下面写段测试代码测试这个函数: # coding:utf-8 from sklearn.utils.class_weight import compute_class_weight class_weight = 'balanced' label = [0] * 9 + [1]*1 + [2, 2] print(label) # [0, 0, 0, 0, 0, 0, 0,...
from .class_weight import compute_class_weight, compute_sample_weight File "C:\Python27\lib\site-packages\sklearn\utils\class_weight.py", line 7, in <module> from ..utils.fixes import in1d File "C:\Python27\lib\site-packages\sklearn\utils\fixes.py", line 318, in <module> from scipy...
18deffit\(self, X, y, sample\_weight=None\): 19#训练L1逻辑回归 20super\(LR, self\).fit\(X, y, sample\_weight=sample\_weight\)21self.coef\_old\_ = self.coef\_.copy\(\) 22#训练L2逻辑回归 23self.l2.fit\(X, y, sample\_weight=sample\_weight\) ...
Per-sample weights. Rescale C per sample. Higher weights force the classifier to put more emphasis on these points.1. 在：from sklearn.utils.class_weight import compute_class_weight ⾥⾯可以看到计算的源代码。2. 除了通过字典形式传⼊权重参数，还可以设置的是：class_weight = 'balanced'，例如...
20 super\(LR, self\).fit\(X, y, sample\_weight=sample\_weight\) 21 self.coef\_old\_ = self.coef\_.copy\(\) 22#训练L2逻辑回归 23 self.l2.fit\(X, y, sample\_weight=sample\_weight\) 24 25 cntOfRow, cntOfCol = self.coef\_.sha...
sklearn里的逻辑回归给每一个样本赋权是作用在“损失函数”上,在计算log_logistic(yz)时乘以sampleweighs使得每个样本赋予上相应的权重,最后进行加总求和。同时在计算梯度时,也会用到sample_weight,梯度本质上是多元函数求偏导,其中safe_sparse_dot(X.T, z0)计算此时的梯度。