- A Benchmark Dataset for Time Series Anomaly Detection. ([1])
- Python image processing libraries performance: OpenCV vs Scipy vs Scikit-Image. ([2])
- Exploring Spark MLlib. ([3])
- 7 Python Libraries you should know about ([4])
- Benchmarking random forest implementations. ([5])
- Statistical inference is only mostly wrong. (really?!) ([6])
Showing posts with label random forest. Show all posts
Showing posts with label random forest. Show all posts
Monday, May 25, 2015
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Wednesday, October 22, 2014
How to tune parametes of random forest and gradient boosting tree?
Tune model sometimes refer to change different parameters and check the performance. Tree based models are easier to tune, b/c there are not many parameters to change for tree based models.
There are two main paramters of random forest model. They are depth and tree count.
My current thoughts.
Increase depth will decrease variance and increase bias.
Increase tree count will decrease bias and may increace variance.
Basically you can use small tree count (e.g. 100) to tune depth first. Increase depth to get low variance (maybe high bias). Then increase tree count to reduce bias.
Similar with random forest, GBT mainly has three paramenters. They are tree depth, iterator and learning rate.
- Random forest model
There are two main paramters of random forest model. They are depth and tree count.
My current thoughts.
Increase depth will decrease variance and increase bias.
Increase tree count will decrease bias and may increace variance.
Basically you can use small tree count (e.g. 100) to tune depth first. Increase depth to get low variance (maybe high bias). Then increase tree count to reduce bias.
- Gradient boosting tree
Similar with random forest, GBT mainly has three paramenters. They are tree depth, iterator and learning rate.
My current thoughts.
Increase depth will be learning faster (easier to converge) and maybe jump around when close to converge.
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