Students Benchmark（仅使用students_reached作为特征）

1. 特征: stucents_reached
2. 分类器: randomforest
3. 森林大小: 1000棵树
4. train-validate比例为8:2
5. cross-validate结果：
   * true-positive: 0
   * true-negative 116436
   * false-positive: 3
   * false-negative 7399
   * Precision: 0.0
   * Recall: 0.0
平衡训练样例中正反例的数量

1. 改善训练集中数据的分布: 取等量的正反例进行训练，避免训练器过拟合于常数（将所有样例分类为反例）
2. 细化log:
   * positive sample amount (total) in training set:	29313
   * negative sample amount (total) in training set:	466031
   * positive sample amount (used) in training set:	29313
   * negative sample amount (used) in training set:	29313
   * positive sample amount (total) in validation set:	7387
   * positive sample amount (total) in validation set:	116451
   * positive sample amount (used) in validation set:	7387
   * negative sample amount (used) in validation set:	116451
   * true-positive: 3755
   * true-negative 61444
   * false-positive: 55007
   * false-negative 3632
   * Precision: 0.063901841326
   * Recall: 0.508325436578
3. 改用多线程，利用多核CPU（将分类器的参数n_job设为-1使其自动使用所有CPU核心进行计算）
Students Benchmark（仅使用students_reached作为特征）

1. 特征: char_length(essay)
2. 分类器: randomforest
3. 森林大小: 1000棵树
4. train-validate比例为8:2
5. cross-validate结果：
   * true-positive: 4316
   * true-negative: 58844
   * false-positive: 57683
   * false-negative: 3024
   * Precision: 0.0696140260327
   * Recall: 0.588010899183
   * positive sample amount (total) in training set:	29370
   * negative sample amount (total) in training set:	466086
   * positive sample amount (used) in training set:	29370
   * negative sample amount (used) in training set:	29370
   * positive sample amount (total) in validation set:	7340
   * positive sample amount (total) in validation set:	116527
   * positive sample amount (used) in validation set:	7340
   * negative sample amount (used) in validation set:	116527
修改提交信息

1. 将提交结果中的预测值改为概率
2. roc_curve -> kdd-2014/csv/roc.csv
3. roc_auc_score
development调参

1. develop.py
2. develop.sh
3. develop时使用的分类器换成SVM（初试特征只有两维，RandomFrest出现强烈不可逆转的过拟合，所以暂时切换为SVM）
4. 为SVM.调参时遇到段错误【未解决】
5. 将train和test的特征换为(students_reached, char_len(essay))
6. 将train和test的随机森林大小缩减为10棵树（因为过拟合，后来发现这样也过拟合，所以在develop尝试SVM，不过遇到了内存段错误）
7. 在train中仅使用roc_auc不再使用precision和recall
Development RandomForest using:

+ parameters
  * n_estimators :: range(5, 1000 + 1, 5)
  * min_samples_split :: [10]
  * max_depth :: range(1, 100, 1)
+ features
  * students_reached
  * fulfillment_labor_materials
  * total_price_excluding_optional_support
  * total_price_including_optional_support
  * school_latitude
  * school_longitude
  * char_length(essay)
  * char_length(short_description)
Look backward and forward

1. changing developing structure (look backward 9 and forward 4)
2. changing training structure (look backward 9 and forward 4)
3. changing testing structure (look backward 9 and forward 4)