Random Forest Vs Decision Tree: Difference Between Random Forest and Decision Tree

Recent advancements have paved the growth of multiple algorithms. These new and blazing algorithms have set the data on fire. They help in handling data and making decisions with them effectively. Since the world is dealing with an internet spree. Almost everything is on the internet. To handle such data, we need rigorous algorithms to make decisions and interpretations. Now, in the presence of a wide list of algorithms, it’s a hefty task to choose the best suited.

Decision-making algorithms are widely used by most organizations. They have to make trivial and big decisions every other hour. From analyzing which material to choose to get high gross areas, a decision is happening in the backend. The recent python and ML advancements have pushed the bar for handling data. Thus, data is present in huge bulks. The threshold depends on the organization. There are 2 major decision algorithms widely used. Decision Tree and Random Forest- Sounds familiar, right?

Trees and forests!

Let’s explore this with an easy example.

Suppose you have to buy a packet of Rs. 10 sweet biscuits. Now, you have to decide one among several biscuits’ brands.

You choose a decision tree algorithm. Now, it will check the Rs. 10 packet, which is sweet. It will choose probably the most sold biscuits. You will decide to go for Rs. 10 chocolate biscuits. You are happy!

But your friend used the Random forest algorithm. Now, he has made several decisions. Further, choosing the majority decision. He chooses among various strawberry, vanilla, blueberry, and orange flavors. He checks that a particular Rs. 10 packet served 3 units more than the original one. It was served in vanilla chocolate. He bought that vanilla choco biscuit. He is the happiest, while you are left to regret your decision.

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What is the difference between the Decision Tree and Random Forest?

1. Decision Tree

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Decision Tree is a supervised learning algorithm used in machine learning. It operated in both classification and regression algorithms. As the name suggests, it is like a tree with nodes. The branches depend on the number of criteria. It splits data into branches like these till it achieves a threshold unit. A decision tree has root nodes, children nodes, and leaf nodes.

Recursion is used for traversing through the nodes. You need no other algorithm. It handles data accurately and works best for a linear pattern. It handles large data easily and takes less time.

How does it work?

1. Splitting

Data, when provided to the decision tree, undergoes splitting into various categories under branches.

Must Read: Naive Bayes Classifier: Pros & Cons, Applications & Types Explained

2. Pruning

Pruning is shredding of those branches furthermore. It works as a classification to subsidize the data in a better way. Like, the same way we say pruning of excess parts, it works the same. The leaf node is reached, and pruning ends. It’s a very important part of decision trees.

3. Selection of trees

Now, you have to choose the best tree that can work with your data smoothly.

Here are the factors that need to be considered:

4. Entropy

To check the homogeneity of trees, entropy needs to be inferred. If the entropy is zero, it’s homogenous; else not.

5. Knowledge gain

Once the entropy is decreased, the information is gained. This information helps to split the branches further.

• You need to calculate the entropy.
• Split the data on the basis of different criteria
• Choose the best information.

Tree depth is an important aspect. The depth informs us of the number of decisions one needs to make before we come up with a conclusion. Shallow depth trees perform better with decision tree algorithms.

1. Easy
2. Transparent process
3. Handle both numerical and categorical data
4. Larger the data, the better the result
5. Speed

1. May overfit
2. Pruning process large
3. Optimization unguaranteed
4. Complex calculations
5. Deflection high

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2. Random Forest

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It is also used for supervised learning but is very powerful. It is very widely used. The basic difference being it does not rely on a singular decision. It assembles randomized decisions based on several decisions and makes the final decision based on the majority.

It does not search for the best prediction. Instead, it makes multiple random predictions. Thus, more diversity is attached, and prediction becomes much smoother.

You can infer Random forest to be a collection of multiple decision trees!

Bagging is the process of establishing random forests while decisions work parallelly.

1. Bagging

• Take some training data set
• Make a decision tree
• Repeat the process for a definite period
• Now take the major vote. The one that wins is your decision to take.

2. Bootstrapping

Bootstrapping is randomly choosing samples from training data. This is a random procedure.

STEP by STEP

• Random choose conditions
• Calculate the root node
• Split
• Repeat
• You get a forest

Read : Naive Bayes Explained

1. Powerful and highly accurate
2. No need to normalizing
3. Can handle several features at once
4. Run trees in parallel ways
1. They are biased to certain features sometimes
2. Slow
3. Can not be used for linear methods
4. Worse for high dimensional data

Conclusion

Decision trees are very easy as compared to the random forest. A decision tree combines some decisions, whereas a random forest combines several decision trees. Thus, it is a long process, yet slow.

Whereas, a decision tree is fast and operates easily on large data sets, especially the linear one. The random forest model needs rigorous training. When you are trying to put up a project, you might need more than one model. Thus, a large number of random forests, more the time.

It depends on your requirements. If you have less time to work on a model, you are bound to choose a decision tree. However, stability and reliable predictions are in the basket of random forests.

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How is random forest different from a normal decision tree?

In machine learning, a Decision Tree is a supervised learning technique. It is capable of working with both classification and regression techniques. It resembles a tree with nodes, as the name implies. The amount of criteria determines the branches. It divides data into these branches until it reaches a threshold unit. There are root nodes, child nodes, and leaf nodes in a decision tree. Random forest is also used for supervised learning, although it has a lot of power. It's quite popular. The main distinction is that it does not rely on a single decision. It assembles randomized decisions based on many decisions and then creates a final decision depending on the majority.

What are the main advantages of using a random forest versus a single decision tree?

In an ideal world, we'd like to reduce both bias-related and variance-related errors. This issue is well-addressed by random forests. A random forest is nothing more than a series of decision trees with their findings combined into a single final result. They are so powerful because of their capability to reduce overfitting without massively increasing error due to bias. Random forests, on the other hand, are a powerful modelling tool that is far more resilient than a single decision tree. They combine numerous decision trees to reduce overfitting and bias-related inaccuracy, and hence produce usable results.

What is a limitation of decision trees?

One of decision trees' drawbacks is that they are very unstable when compared to other choice predictors. A slight change in the data might cause a significant change in the structure of the decision tree, resulting in a result that differs from what consumers would expect in a typical event. Furthermore, when the main purpose is to forecast the result of a continuous variable, decision trees are less helpful in making predictions.