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计算基尼不纯度，代码里的逻辑是这样的，见fitness.cpp代码：

162 for (size_t j = unknown; j < len-1; ++j) {
163 s = data_copy[j]->target * data_copy[j]->weight;
164 ss = Squared(data_copy[j]->target) * data_copy[j]->weight;
165 c = data_copy[j]->weight;
166
167 ls += s;
168 lss += ss;
169 lc += c;
170
171 rs -= s;
172 rss -= ss;
173 rc -= c;
174
175 ValueType f1 = data_copy[j]->feature[index];
176 ValueType f2 = data_copy[j+1]->feature[index];
177 if (AlmostEqual(f1, f2))
178 continue;
179
180 fitness1 = lc > 1? (lss - lsls/lc) : 0;
181 if (fitness1 < 0) {
182 // std::cerr << "fitness1 < 0: " << fitness1 << std::endl;
183 fitness1 = 0;
184 }
185
186 fitness2 = rc > 1? (rss - rsrs/rc) : 0;
187 if (fitness2 < 0) {
188 // std::cerr << "fitness2 < 0: " << fitness2 << std::endl;
189 fitness2 = 0;
190 }
191
192 double fitness = fitness0 + fitness1 + fitness2;
193
194 if (g_conf.feature_costs && g_conf.enable_feature_tunning) {
195 fitness *= g_conf.feature_costs[index];
196 }
197
198 if (*impurity > fitness) {
199 *impurity = fitness;
200 *value = (f1+f2)/2;
201 *gain = fitness00 - fitness1 - fitness2;
202 }
203 }
204
205 return *impurity != std::numeric_limits::max();
206 }

计算impurity我的理解是应该计算全量样本的，代码的逻辑貌似是循环每一条非空特征的样本，然后取最小值。所以我的疑问是198-202行代码是不是应该在for循环之外呢，还是我的理解有问题？求解答

Functions for gradient boosting classifier make me confused.

1. Why use 'Pr(y=1) = 1/(1+exp(-2F(x)))' instead of 'Pr(y=1) = 1/(1+exp(-F(x)))'?
2. Why use 'L(y, F(x)) = log(1+2exp(-2yF(x)))' instead of L(y, F(x)) = log(1+exp(-yF(x)))'
   Thank you.

模型在处理missing value有点问题，麻烦看一下，谢谢！
这是我构造的训练集A，20条样本，训练集上的auc=0.55
0 1 0:1
0 1 0:1
0 1 0:1
0 1 0:1
0 1 0:1
0 1 0:1
0 1 0:1
0 1 0:1
0 1 0:1
0 1 0:1
1 1 1:1
1 1 1:1
1 1 1:1
1 1 1:1
1 1 1:1
1 1 1:1
1 1 1:1
1 1 1:1
1 1 1:1
1 1 1:1

训练集合B，对A补全了特征，20条样本，训练集上auc=1
0 1 0:1 1:0
0 1 0:1 1:0
0 1 0:1 1:0
0 1 0:1 1:0
0 1 0:1 1:0
0 1 0:1 1:0
0 1 0:1 1:0
0 1 0:1 1:0
0 1 0:1 1:0
0 1 0:1 1:0
1 1 0:0 1:1
1 1 0:0 1:1
1 1 0:0 1:1
1 1 0:0 1:1
1 1 0:0 1:1
1 1 0:0 1:1
1 1 0:0 1:1
1 1 0:0 1:1
1 1 0:0 1:1

11

1、enable_initial_guess这个大概是啥意思？
2、gbdt为啥要加一个bias呢？