print() function in randomForest.py

MJGao · Jun 6, 2018 · b5225b0 · b5225b0
1 parent 91bf612
commit b5225b0
Showing 1 changed file with 6 additions and 6 deletions.
diff --git a/src/py3.x/ML/7.RandomForest/randomForest.py b/src/py3.x/ML/7.RandomForest/randomForest.py
@@ -102,7 +102,7 @@ def get_split(dataset, n_features):
             # 左右两边的数量越一样，说明数据区分度不高，gini系数越大
             if gini < b_score:
                 b_index, b_value, b_score, b_groups = index, row[index], gini, groups  # 最后得到最优的分类特征 b_index,分类特征值 b_value,分类结果 b_groups。b_value 为分错的代价成本
-    # print b_score
+    # print(b_score)
     return {'index': b_index, 'value': b_value, 'groups': b_groups}
 
 
@@ -303,7 +303,7 @@ def evaluate_algorithm(dataset, algorithm, n_folds, *args):
 
     # 加载数据
     dataset = loadDataSet('input/7.RandomForest/sonar-all-data.txt')
-    # print dataset
+    # print(dataset)
 
     n_folds = 5        # 分成5份数据，进行交叉验证
     max_depth = 20     # 调参（自己修改） #决策树深度不能太深，不然容易导致过拟合
@@ -315,7 +315,7 @@ def evaluate_algorithm(dataset, algorithm, n_folds, *args):
         scores = evaluate_algorithm(dataset, random_forest, n_folds, max_depth, min_size, sample_size, n_trees, n_features)
         # 每一次执行本文件时都能产生同一个随机数
         seed(1)
-        print 'random=', random()
-        print 'Trees: %d' % n_trees
-        print 'Scores: %s' % scores
-        print 'Mean Accuracy: %.3f%%' % (sum(scores)/float(len(scores)))
+        print('random=', random())
+        print('Trees: %d' % n_trees)
+        print('Scores: %s' % scores)
+        print('Mean Accuracy: %.3f%%' % (sum(scores)/float(len(scores))))