Decision Tree vs. Random Forest for Classification Problems

Decision trees are part of the Supervised Classification Algorithm family. On classification issues, they work very well, the decisional route is reasonably easy to understand, and the algorithm is fast and straightforward. Random Forest is the ensemble variant of Decision Trees. Random forest is a versatile, easy-to-use machine learning algorithm that produces, most of the time, a great result even without hyper-parameter tuning. Thanks to its simplicity and variety, it is also one of the most used algorithms (it can be applied for classification and regression tasks).

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Blog Contents

  • Introduction to Decision Trees
  • What is a Random Forest?
  • So Which One Should You Choose?
  • EndNote


In this article, we compare Decision Trees vs. Random Forests. So, without further ado, let’s go!


Introduction to Decision Trees

A decision tree is a basic structure created by nodes and branches that is tree-like. Depending on some of the input features, data is split at each node, producing two or more branches as output. This iterative process increases the number of branches produced, and the original data is partitioned. This continues until a node is created where all or nearly all of the data belongs to the same class, and it is no longer possible to break or branch further.


A tree-like structure is created by this whole method. The root node is considered the first dividing node. The end nodes are called leaves and have a classmark associated with them. The paths from the root to the leaf create the rules of classification.


The Pros

  • Quick to interpret
  • Good handling of both categorical and continuous results.
  • Works on a broad dataset well.
  • Not perceptible to outliers.
  • In Nature, non-parametric.


The Cons


  • These are vulnerable to overfitting.
  • It can be considerably large, thus making pruning necessary.
  • Can’t guarantee optimal trees.
  • Compared to other machine learning algorithms, it offers low prediction precision for a dataset.
  • When there are a lot of class variables, calculations can become complicated.
  • High Variance(With a change in training data, the model can change quickly)


What is a Random Forest?

It is effortless to comprehend and perceive the decision tree algorithm. But sometimes, for the development of successful results, a single tree is not necessary. This is where the algorithm for the Random Forest comes into the picture. Random Forest is a machine learning algorithm based on a tree that leverages multiple decision trees’ power to make choices. As the name implies, it is a “forest” of trees!

But why are we calling this a ‘random’ forest? That’s because it’s a randomly generated decision-tree forest. To measure the output, each node in the decision tree operates on a random subset of features. To construct the final output, the random forest then combines the output of individual decision trees.

Machine learning experts say in plain words that to produce the final output, the Random Forest Algorithm mixes the output of multiple (randomly created) Decision Trees.


The Pros:

  • Robust against outliers.
  • Works fine with non-linear details.
  • Lower overfitting risk.
  • Runs effectively on a big dataset.
  • Better accuracy than other algorithms for classification.


The Cons:

  • When dealing with categorical variables, random forests were considered to be biased.
  • Slow Training.
  • Not sufficient for linear techniques with a lot of sparse features

So Which One Should You Choose?

For circumstances where we have a huge dataset, Random Forest is ideal, and interpretability is not a major concern.


Decision trees are often simpler to view and identify. It becomes more challenging to interpret because a random forest incorporates multiple decision trees. Here’s the good news; a random forest is not difficult to read.

Random Forest also has a greater time for preparation than a single decision tree. This should be taken into the record since the time taken to train and of them often increases as we increase the number of trees in a random forest. When you work on a machine learning project with a tight deadline, that can also be crucial.


Decision trees are beneficial, despite uncertainty and reliance on a specific set of features, since they are simpler to understand and faster to train. Anyone with minimal data science experience can still use decision trees to make fast decisions guided by data.



In the decision tree vs. random forest debate, the basics are what you need to remember. When you’re new to machine learning, it can get complicated, but this article should explain the differences and similarities. Random forest is a great algorithm to practice, to see how it operates early in the model creation process. Its simplicity makes the creation of a low random forest a problematic proposition. The algorithm is also an excellent choice for someone who wants to create a model quickly, suggest machine learning experts.

On top of that, it gives a reasonably clear indication of the significance it assigns to your characteristics. Performance-wise, random forests are also very difficult to beat. Of course, a model that can perform better can always be found, such as a neural network, for example, but these typically take more time to create. However, they can handle many different types of features, such as binary, categorical, and numerical.


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