Simple and crude understanding and implementation of integrated machine learning Learning (II): Bagging integration principle, random forest construction process, random forest api reports, random forest predict cases, bagging integration advantages

Integrated learning

learning target

• Understand two core tasks of solving major integrated learning
• You know the principles of integrated bagging
• We know the process of establishing a random decision tree forest
• Why do I need to know random with replacement (Bootstrap) sampling
• Random Forest algorithm to achieve application RandomForestClassifie
• We know the principle of boosting integration
• Know the difference between bagging and boosting the
• Learn gbdt implementation process
  

5.2 Bagging

1 Bagging integration principle

Goal: The following classification circle and square

Implementation process:

1. Sampling different data sets

2. Training classifier

3. equal rights to vote, to obtain the final result

4. Summary The main implementation process

2 random forest construction process

In machine learning, a random forest classifier comprising a plurality of decision trees , and the output categories are categories of the mode output from the individual tree may be.

Random Forests = Bagging + tree

For example, if you trained five trees, where the results for four of the tree is True, the result of a tree is False, then the final vote result is True

Random Forests be a critical step in the manufacturing process (represented by N by the number of training cases (samples), M is the number of features):

1) a randomly selected sample, sampling with replacement, repeated N times (possible duplicate samples appear)

2) to randomly select the m feature, m << M, the decision tree

• Think

  • 1. Why a random sample of the training set?

    If you are not a random sample, each tree training set are the same, then the final training a tree classification result is exactly the same

  • 2. Why do you need replacement, sampling?

    如果不是有放回的抽样，那么每棵树的训练样本都是不同的，都是没有交集的，这样每棵树都是“有偏的”，都是绝对“片面的”（当然这样说可能不对），也就是说每棵树训练出来都是有很大的差异的；而随机森林最后分类取决于多棵树（弱分类器）的投票表决。

3 随机森林api介绍

• sklearn.ensemble.RandomForestClassifier(n_estimators=10, criterion=’gini’, max_depth=None, bootstrap=True, random_state=None, min_samples_split=2)
  • n_estimators：integer，optional（default = 10）森林里的树木数量120,200,300,500,800,1200
  • Criterion：string，可选（default =“gini”）分割特征的测量方法
  • max_depth：integer或None，可选（默认=无）树的最大深度 5,8,15,25,30
  • max_features="auto”,每个决策树的最大特征数量
    • If “auto”, then max_features=sqrt(n_features).
    • If “sqrt”, then max_features=sqrt(n_features)(same as “auto”).
    • If “log2”, then max_features=log2(n_features).
    • If None, then max_features=n_features.
  • bootstrap：boolean，optional（default = True）是否在构建树时使用放回抽样
  • min_samples_split:节点划分最少样本数
  • min_samples_leaf:叶子节点的最小样本数
• 超参数：n_estimator, max_depth, min_samples_split,min_samples_leaf

4 随机森林预测案例

• 实例化随机森林

# 随机森林去进行预测
rf = RandomForestClassifier()

• Select list defines the parameters over

param = {"n_estimators": [120,200,300,500,800,1200], "max_depth": [5, 8, 15, 25, 30]}

• Use GridSearchCV mesh search

# 超参数调优
gc = GridSearchCV(rf, param_grid=param, cv=2)

gc.fit(x_train, y_train)

print("随机森林预测的准确率为：", gc.score(x_test, y_test))

note

• The process of establishing a random forest
• Depth, number of trees and other trees need to be tuned over the parameter

5 bagging integration advantages

Bagging + tree / linear regression / logistic regression / deep learning ensemble learning ... = bagging

After the above integrated learning mode consisting of:

1. Can improve the generalization accuracy rate of about 2% in the original algorithm
2. Simple, convenient, versatile