范例01：
//只给y_true和y_pred，其他默认
y_true := Array(0, 1, 2, 0, 1, 2);
y_pred := Array(0, 2, 1, 0, 0, 1);
return fbeta_score(y_true,y_pred);//array(0.8,0.0,0.0)
范例02：
//指定beta参数： y_true := Array(0, 1, 2, 0, 1, 2);
y_pred := Array(0, 2, 1, 0, 0, 1);
beta := 2;
return fbeta_score(y_true,y_pred,beta);
//array(0.9090,0.0,0.0)
范例03：
//指定labels
y_true := Array(0, 1, 2, 0, 1, 2);
y_pred := Array(0, 2, 1, 0, 0, 1);
labels := Array(2,1,0);
return fbeta_score(y_true,y_pred,nil,nil,labels);//array(0.0,0.0,0.8)
范例04：
//单标签二分类，指定pos_label，并且指定average为"binary"，
//只计算正例的precision
y_true := Array(0, 1, 1, 0, 1, 1);
y_pred := Array(0, 0, 1, 0, 0, 1);
pos_label := 0;
average := "binary";
return fbeta_score(y_true,y_pred,nil,average,nil,pos_label);
//0.6666
范例05：
//指定average，指定计算precision的平均方式
y_true := Array(0, 1, 2, 0, 1, 2);
y_pred := Array(0, 2, 1, 0, 0, 1);
average := "macro"; //宏平均，先计算各标签的precision再取平均
return fbeta_score(y_true,y_pred,nil,average);
//0.2666
范例06：
//指定sample_weight
y_true := Array(0, 1, 2, 0, 1, 2);
y_pred := Array(0, 2, 1, 0, 0, 1);
sample_weight := array(2.5,2,3,1,1.5,2);
return fbeta_score(y_true,y_pred,nil,nil,nil,nil,sample_weight);
// array(0.8235,0.0,0.0)