使用sklearn RandomForestClassifi进行分类

Question

我使用Scikit RandomForestClassifier对不平衡数据进行分类。目标类数据为“1”或“0”（99%的值为0）。在

我想分配一个权重。我怎么能做到呢。在

我在文献中发现：

sample_weight : array-like, shape = [n_samples] or None

Sample weights. If None, then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node. In the case of classification, splits are also ignored if they would result in any single class carrying a negative weight in either child node.

我需要增加“1”的影响力

我应该这样做吗：

s_weight = np.array([100 if i == 1 else 1 for i in y_train]) 

或者这样：

^{pr2}$

一。在

clf.fit(X_train, y_train, sample_weight=s_weights)

由于我没有得到预期的结果，谁能确认一下？在

Answer 1

从技术上讲

s_weight = np.array([100 if i == 1 else 1 for i in y_train]) 

是正确的，尽管在RF中加权并不像支持向量机那样简单。您必须交叉验证才能找到最佳权重（可能比100小得多）。在