RF
所属分类：人工智能/神经网络/深度学习
开发工具：matlab
文件大小：7KB
下载次数：1
上传日期：2020-05-07 16:01:15
上 传 者：
GHao
说明： 输入：
trainX训练数据
训练标签
testX测试数据
测试标签
模型参数模型参数
输出：
测试准确率
训练时间
测试时间
模型训练模型
(Inputs:
trainX Training data
trainY Training labels
testX Testing data
testY Testing labels
ModelParameters Model parameters
Outputs:
TestAcc Testing Accuracy
TrainingTime Time taken for training
TestingTime Time taken for testing
Model Trained model)
文件列表:
RF\axis_parallel_cut.m (895, 2016-10-21)
RF\cartree_predict.m (968, 2016-10-21)
RF\cartree_train.m (3439, 2020-04-04)
RF\getargs.m (3471, 2016-08-01)
RF\gini_impurity.m (2389, 2016-10-21)
RF\RF.m (558, 2020-04-05)
RF\RF_predict.m (1492, 2020-04-04)
RF\RF_train.m (733, 2020-04-04)
RF\TEST_RF.m (2553, 2020-04-05)
RF\weighted_hist.m (589, 2016-10-21)
RF (0, 2020-04-21)
# Random Forests
This set of codes implements Random Forests algorithm to classify data.
You should do some research on your own to understand the basic priciples on how Random Forests Works.
To get started, you should refer to 'TEST_RF.m'.
#### Parameters
nTree: Number of trees (Needs to be positive integer)
mtry: Number of features in a subset (Needs to be positive integer)
#### Usage
The main function to train and test Random Forests is:
"""
[TestAcc,TrainingTime,TestingTime,Model] = RF(trainX,trainY,testX,testY,ModelParameters);
"""
Inputs:
trainX Training data
trainY Training labels
testX Testing data
testY Testing labels
ModelParameters Model parameters
Outputs:
TestAcc Testing Accuracy
TrainingTime Time taken for training
TestingTime Time taken for testing
Model Trained model
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