Machine Learning: Master The Three Types Of Machine Learning by Robert Keane

Author:Robert Keane [Keane, Robert]
Language: eng
Format: azw3
Published: 2017-10-21T04:00:00+00:00

Random forests

The next kind of algorithm that you can use is known as a random forest. These are popular algorithms to use in the world of data science so if you plan to learn more about this field, you need to spend time learning how to use the random forests. Since random forests are so well-known and popular, it is common to see them in a lot of the problems that you will use within data science problems. If you want to work with an exploration task on your data, like dealing with missing values or treating the outliers of information, you will find that random forests are the perfect tool to get this done.

There are several ways that the random forests are going to work to give you the results that you would like:

First, when you create your training sets, you will find that each object inside of that set is randomly generated and it can be replaced if the random tree thinks it is necessary.

Also, if there are M input variable amounts, then m<M is going to be specified from the beginning and it will be held constant. This is important because it also means that every tree will be able to randomly pick out their own variables from M.

The goal of each random tree is to find the best split for variable m.

As the tree grows, all of them will end up getting as large as possible. Random trees also don't prune themselves so keep this in mind.

The forest is great because it will be able to predict certain outcomes. It can do this because it takes all of the predictions from each tree that is created in order to select the average for regression or the majority for consensus during classification.

Random forests can be a great tool to use when working on data science and there are many advantages to picking this one instead of one of the other options. First, these random forests are able to deal with both classification and regression problems, which most other algorithms aren’t able to do. In addition, random forests can be great for handling larger amounts of data and you can add in thousands or more variables and this algorithm can handle it just fine.

One thing to keep in mind with this one is that while the random trees can work with regression problems, they are not able to make predictions that go past the ranges that you place into the training data. This can help you out with some predictions, but they are going to be limited since it won’t go past the ranges that you can provide so your accuracy is going to be lower.

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