Random Forest with Python


Introduction

In the article about decision tree we've talked about it's drawbacks of being sensitive to small variations or noise in the data. Today we'll see how to deal with them by introducing a random forest. It belongs to a larger class of machine learning algorithms called ensemble methods, which use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms. So which models does random forest aggregate? You might already know the answer - the decision trees. It fits a number of decision tree classifiers on various sub-samples of the dataset and use averaging to improve the predictive accuracy and control over-fitting.

Implementation

Scikit-learn provides us with two classes RandomForestClassifier and RandomForestRegressor for classification and regression problems respectively. Let's use the code from the previous example and see how the result will different, using random forest with 100 trees. 

...
clf = RandomForestClassifier(n_estimators=100).fit(X, y)
...

As you can see the edges are much smoother, that is less overfitted, than using a single decision tree.

Conclusion

Despite it's relative simplicity, random forest performs the job remarkably well. According to empirical comparisons, even better than SVMs.

Comments

Post a Comment

Popular posts from this blog

CAP Theorem and blockchain

Image
In theoretical computer science, the CAP theorem states that it is impossible for a distributed data store (such as a blockchain network) to simultaneously provide more than two out of the three guarantees: Consistency, Availability & Partition tolerance. CAP Theorem – the parts C: Consistency – At any given time, all nodes in the network have exactly the same (most recent) value. A: Availability – Every request to the network receives a response, though without any guarantee that returned data is the most recent. P: Partition tolerance – The network continues to operate, even if an arbitrary number of nodes are failing. Due to the nature of distributed data stores (such as blockchain), Partition tolerance is a given fact; there will always be failing/unreachable nodes in the network (not least because of the unstable nature of the internet). CAP Theorem states that one has to choose between C (Consistency) or A (Availability) when in the presence of P (Partition): Av...
Read more

Length extension attack

Image
What is length extension? When a Merkle–Damgård based hash is misused as a message authentication code with construction H(secret ‖ message), and message and the length of secret is known, a length extension attack allows anyone to include extra information at the end of the message and produce a valid hash without knowing the secret. Quick sidebar, before you freak out: Since HMAC does not use this construction, HMAC hashes are not prone to length extension attacks. So, a length extension attack is a type of attack where an attacker can use Hash(message1) and the length of message1 to calculate Hash(message1 ‖ message2) for an attacker-controlled message2, without needing to know the content of message1. Algorithms like MD5, SHA-1 and most of SHA-2 that are based on the Merkle–Damgård construction are susceptible to this kind of attack. Truncated versions of SHA-2, including SHA-384 and SHA-512/256 are not susceptible, nor is the SHA-3 algorithm. A bit more details ...
Read more

Contract upgrade anti-patterns

Image
A popular trend in smart contract design is to promote the development of upgradable contracts . To be fair, existing techniques to upgrade contracts have flaws, increase the complexity of the contract significantly, and ultimately introduce bugs - even in the Zeppelin contract upgrade strategy. In this article, we are going to detail our analysis of existing smart contract upgrade strategies, describe the weaknesses we have observed in practice, and provide recommendations for contracts that require upgrades. In a follow-up blog post, we will detail a method, contract migration, that achieves the same benefits with few of the downsides. An overview of upgradable contracts Two ‘families’ of patterns have emerged for upgradable smart contracts: Data separation, where logic and data are kept in separate contracts. The logic contract owns and calls the data contract. Delegatecall-based proxies, where logic and data are kept in separate contracts, also, but the data contr...
Read more