What Is Random Forest in Machine Learning?

What Is Random Forest in Machine Learning?

In our world of evolving technology, machines have made it much easier to learn
from the possibilities of the future. These possibilities are called predictions, and the
way machines learn about these occurrences is through algorithms. There exists an
almost infinite number of algorithms that are carefully planned and executed through
computer programs for any given reason or industry. One of these algorithms is
called random forest.

Let’s go into a deeper dive to better understand what the random forest algorithm is
and how it can benefit businesses.

What is machine learning?

Machine learning is one of the many methods in which data is automated to build
models and metrics of any collected data. It’s a subdivision of artificial intelligence (AI) in which systems “learn” from data, identify patterns and make decisions based
on their findings without requiring much human intervention. The more these
machines “learn” or analyze the gathered data, the more and “smarter” decisions
they can make. This is where smart devices come from.

What is “random forest”?

When debating on which algorithms to use, the term “random forest” may appear as
one of the options. This is one of the simplest and most diverse algorithms machines
can use to produce results when analyzing their collected data. These random
forests are built with decision trees that basically decide through binary answers
such as “yes” and “no” to come to a conclusion or make a prediction. Just as there
are many leafy trees in a forest ecosystem, there are many decision tree models in a
random forest algorithm.

How is the random forest technique used?

In order to use a random tree algorithm, certain samples of data are primarily chosen
from a given dataset. From each dataset or sample, a decision tree will be built to
produce predictions. Each decision tree will produce a decision and perform a vote
for any of the predicted results. The machine will then proceed to choose the
prediction with the highest number of votes as its final prediction.

Although it seems like a fairly simple process, many things are taken into
consideration and programmed to get any of the wanted results. For instance,
missing data points need to be pointed out as well as any noticeable anomalies prior
to “training” the machine learning model. The machine will choose the most popular
results to make the best prediction possible.

What are some of the advantages?

Like all things in life, the random forest algorithm has its pros and cons. Some of its
advantages include its ability to be used for regression and classification tasks as
well as the ease of use and readability. Moreover, these models can produce good
predictions no matter the size of the datasets, and they can provide high levels of
accuracy when predicting outcomes.

What are some disadvantages?

Likewise, random forest trees can present disadvantages depending on the
organization it’s used by. For instance, when using the random forest algorithm, more resources and time are needed to process the computations when compared
to a decision tree. Additionally, these models can be very volatile as one small
change in the data can cause a ripple effect and essentially affect the final result.

What are some examples where the random forest algorithm is

A simple example of this model can be a nutrition mobile app’s ability to choose a
single fruit from a large list that can benefit your current dietary needs. A more
complex but common example would be an AI’s predictions of a given country’s
election results.

How can businesses benefit from this technique?

The random forest algorithm can enable businesses to make more strategic and accurate
predictions for their business needs as well as reducing the use of overfitting datasets, which
can be overwhelming for the organization.

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