Uncover the workings of random forest classifiers, a powerful machine learning technique leveraging diverse decision trees for highly accurate predictions. Learn how randomness and feature diversity help handle complex datasets effectively.
Key Insights
- Random forest classifiers enhance prediction accuracy by creating numerous diverse decision trees, each analyzing random subsets of data and input features to prevent dominance by a single characteristic like passenger class.
- This method efficiently manages both small and large datasets and effectively handles data outliers, making it particularly suitable for datasets with irregularities such as the Titanic dataset.
- Users can optimize random forest models by adjusting hyperparameters such as the criterion (commonly "entropy"), the number of decision trees (estimators), and random state, which ensures reproducibility of random processes.
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Let's talk about random forest classifiers. Let's load this image in first. These are decision trees.
This is tree 1, tree 2, a bunch of other trees, trees 600. Each of these trees takes data and it splits it up bit by bit. It says for this piece of data, was it split this way, was it male or female, was it first class or second class, and then it makes a prediction.
For each one of these. And it has a different method each time of doing so. A random forest classifier takes this decision tree idea, and as the name would imply, forest is a collection of trees, it has many, many, many trees.
And what a random forest classifier does is it classifies something as, you know, survived or didn't, for example. And it does it by looking at lots of different possibilities, lots of different methods, and averaging them all together. So how is this helpful? How is this a helpful way for it to work? Well, it's looking at random subsets of the data.
And that means that each tree is diverse. They're looking at lots and lots of different pieces of the data. So there's a lot of diversity of ideas here.
If you can call this, you know, computer model an idea. And it also has random features, random inputs. So, for example, this one might consider age and fare.
And this one might consider class and port of embarkation. And so, ultimately, that prevents, you know, some dominant feature like class, which is probably the most important thing in the data, as being the only thing in the data. Random forest classifiers look at a diverse group of features and tries to combine all of them as opposed to saying, like, okay, class seems like the thing to look at.
So you get a high amount of accuracy because of this robust randomness. And it works with large datasets, small datasets. It also handles outliers really well.
And there's definitely some strange outliers in this data. So a random forest classifier is a perfect thing to do, a perfect thing to use for titanic data. Now, these are hyperparameters we'll use.
Hyperparameters means they're not the parameters in the data. They're kind of like the metadata. They're the parameters of training the model.
Criterion, number of estimators, and random state. We'll use 10 decision trees. There's a couple different criteria for splitting our data.
Entropy is a good one. And it's the most common one these days, more than the other one, gene impurity. And giving it a random state will allow it to generate randomness, but have it be reproducible.
So it's random, but it will always be random starting with the same number. So these hyperparameters are things we can tune later if we decide, hey, this model could use some work. What happens if we increase the number of trees or change the criteria? Changing the random state shouldn't matter.
But these two definitely could. We'll stick with these hyperparameters for now, but tuning hyperparameters is a big part of working with random forest classifiers and other ones like that. Other models.
Or model types, rather. All right. We'll start creating a random forest classifier, which of course is not going to be that much code.
And we're going to see how it does.