Random Forest (c++)
Introduction
An implementation of Random Forests, which is written in C++. The speed and perfomance is similar to sklearn-version.
build
I just uses the standard library without any third-class library.
cd build
cmake ../
make -j4
Load Data
The format of train data file is like data/debug.txt which is a sparse matrix. The format of test
data is like data/debug_test.txt.
Train
Parameters:
* @param(int) nEstimators The number of trees in the forest. Default: 10
* @param(string) criterion The function to measure the quality of a split.
* Supported criteria are “gini” for the Gini impurity and “entropy” for
* the information gain. Default: gini
* @param(string) maxFeatures
* * 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.
* Default: "log2"
* @param(int) maxDepth The maximum depth of the tree. -1 means any depth. Default: -1
* @param(int) minSamplesSplit The minimum number of samples required to split
* an internal node. Default:2
* @param(int) minSamplesLeaf The minimum number of samples required to be at
* a leaf node. Default:1
* @param(int) nJobs The number of jobs to run in parallel for both fit and
* predict. Default:1
RandomForest randomForest(100, "gini", "log2", -1, 150, 1, 1000000, 8);
randomForest.fit(trainData);Test
auto results = randomForest.predictProba(testData);
writeDataToCSV(results, testData, "../results/trainResults.csv", false);
Performance
For a dataset which has about ~1.7 million samples, the performance of my implementation is similar to sklearn-version.
