The Random Forest is an esemble of Decision Trees. A single Decision Tree can be easily visualized in several different ways. In this post I will show you, how to visualize a Decision Tree from the Random Forest.
MLJAR's Blog
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How to visualize a single Decision Tree from the Random Forest in Scikit-Learn (Python)?
June 29, 2020 by Piotr Płoński Random forest
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Random Forest Feature Importance Computed in 3 Ways with Python
June 29, 2020 by Piotr Płoński Random forest
The feature importance (variable importance) describes which features are relevant. It can help with better understanding of the solved problem and sometimes lead to model improvements by employing the feature selection. In this post, I will present 3 ways (with code examples) how to compute feature importance for the Random Forest algorithm from scikit-learnpackage (in Python). -
How to save and load Random Forest from Scikit-Learn in Python?
June 24, 2020 by Piotr Płoński Random forest
In this post I will show you how to save and load Random Forest model trained with scikit-learn in Python. The method presented here can be applied to any algorithm from sckit-learn (this is amazing about scikit-learn!).
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How to reduce memory used by Random Forest from Scikit-Learn in Python?
June 24, 2020 by Piotr Płoński Random forest
The Random Forest algorithm from scikit-learn package can sometimes consume too much memory:
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Visualize a Decision Tree in 4 Ways with Scikit-Learn and Python
June 22, 2020 by Piotr Płoński Decision tree
A Decision Tree is a supervised algorithm used in machine learning. It is using a binary tree graph (each node has two children) to assign for each data sample a target value. The target values are presented in the tree leaves. To reach to the leaf, the sample is propagated through nodes, starting at the root node. In each node a decision is made, to which descendant node it should go. A decision is made based on the selected sample’s feature. Decision Tree learning is a process of finding the optimal rules in each internal tree node according to the selected metric. -
Compare MLJAR with Google AutoML Tables
May 17, 2019 by Piotr Płoński Compare
Recently, Google has released AutoML service for structured datasets. It is called AutoML Tables and is currently available in Beta. I’ve decided to compare my open-source solution with Google AutoML Tables.
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AutoML software and services
May 14, 2019 by Piotr Płoński Automl
Automated Machine Learning is the end-to-end process of applying machine learning in an automatic way.
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Random Forest vs Neural Network (classification, tabular data)
May 10, 2019 by Piotr Płoński Random forest Neural network
Which is better: Random Forest or Neural Network? This is a common question, with a very easy answer: it depends :) I will try to show you when it is good to use Random Forest and when to use Neural Network.
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Random Forest vs AutoML (with python code)
May 07, 2019 by Piotr Płoński Random forest Automl
Random Forest versus AutoML you say. Hmmm…, it’s obvious that the performance of AutoML will be better. You will check many models and then ensemble them. This is true, but I would like to show you other advantages of AutoML, that will help you deal with dirty, real-life data.
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Does Random Forest overfit?
April 05, 2019 by Piotr Płoński Random forest
When I first saw this question I was a little surprised. The first thought is, of course, they do! Any complex machine learning algorithm can overfit. I’ve trained hundreds of Random Forest (RF) models and many times observed they overfit. The second thought, wait, why people are asking such a question? Let’s dig more and do some research. After quick googling, I’ve found the following paragraph on Leo Breiman (the creator of the Random Forest algorithm) website: