Other Topics — Machine Learning Interview Questions
Introduction
Not interested in background? Skip to the questions here.
Sometimes life demands endeavors that contribute to society. In this day and age, to be very honest, one such endeavor is that of AI, and interestingly enough, it’s at a stage where demand is very much in line with the supply, if not greater. On top of that, it is an endeavor that most of us would love to pursue, as it is so, so intriguing. Machine Learning, where a computer learns to solve problems on its own by using the data we supply to it and gives meaningful results time and again, is that not wonderful?
Let us not stop there. Let us take steps to be a part of that very endeavor. Let us take the requisite benefit from something that everyone is benefitting from in the present-day world. That is precisely why today, we shall be learning and looking into an algorithm that is pristine and highly logical in how it works and performs. Now, it may sound random, but ‘Random Forests’ are anything but random in terms of their results!
Article Overview
- What is a Random Forest?
- How does Random Forest work?
- Why do we need Random Forest?
- Random Forest Machine Learning Interview Questions
- Wrap Up
What is the Random Forest Algorithm?
A random forest algorithm is an ensemble learning method, which means it stacks together many classifiers to optimize the performance of a model. Therefore, a random forest utilizes multiple decision trees (Classification and Regression Tree) models to work out the output based on the input data. The decision trees employed by it are created using randomly selected features (variables) as well as samples, a form of bagging.
Now, when a random forest is used to predict the output of a classification problem, the mode (most frequently occurring) output amongst all the decision trees is chosen as the one to proceed with.
However, if the purpose is to predict the output of a regression problem, then the mean (average) of all decision trees’ outputs is calculated to proceed forth.
How does a Random Forest Work?
Well, in terms of getting the desired output, a random forest works in the steps as shown below:
- Firstly, we decide how many decision trees we want to be constructed for the random forest to operate on.
- Next, the random forest algorithm automatically and randomly selects samples from the data set given.
- Then, based on the number of decision trees we desire and the random data samples selected from the data set, the random forest method builds the required decision trees.
- Now, the prediction for each decision tree is made.
- Finally, voting or a calculation will be done based on either mode or mean depending on the type of problem, classification, or regression to get the final output.
Why do we need to Use a Random Forest?
An excellent question, to which the answer is straightforward. A random forest can be used for either classification or regressions tasks whilst producing results with greater accuracy as it employs cross-validation. It even handles any incomplete values and can handle a more extensive data set with many features (dimensions). Most importantly, it does all this with reduced over-fitting since it chooses random features and generalizes well over the given data. That makes it a much more accurate and hence better algorithm than the usual decision tree method, which lacks most of the qualities mentioned above.
Random Forest ML Interview Questions/Answers
Ok, so you may be wondering all the above is cool, but come on now, the title was about questions that are likely to come in an interview related to a random forest. Do not worry, let’s get to those very questions straightaway!
What is a random forest?
The random forest is a supervised learning algorithm in Machine Learning. It is called random since the data samples it creates for making the decision trees are randomly selected (a form of bagging). It is also called a forest since it creates many decision trees and, based on ensemble learning, it gives the final output.
Why do we need the random forest algorithm?
A random forest does not overfit and generalizes well over even larger datasets than a decision tree, resulting in a more accurate result. Hence, it’s second nature to utilize it in the appropriate situations.
What are the advantages of the random forest methodology?
The advantages of using the random forest algorithm are as follows:
- Random forest reduces the problem of overfitting by averaging or combining the outputs of several decision trees.
- From the above, it can be seen that they work well for an extensive data set compared to a single decision tree.
- It, therefore, has less variance as compared to a decision tree.
- Random forests exhibit flexibility and are very highly accurate.
- When it comes to using the random forest algorithm, there is no requirement of scaling the data as it establishes a good accuracy either way.
- Even when a lot of data is missing, the random forest method shows excellent accuracy.
What are the disadvantages of the random forest methodology?
The disadvantages of the random forest method are as stated below:
- It’s pretty complex compared to other methods
- More time is consumed during the construction of the various decision trees in a random forest algorithm.
- Due to the above, greater computational power is required to implement a random forest algorithm.
- Its intuitiveness sees a drop once the number of decision trees is greater.
How do random forests maintain accuracy when data is missing?
They do this by usually be making use of two methods:
- Dropping/removing the data points where values are not present but is not a preferred method since the available information is not being made use of.
- By filling in/completing the missing values with median if the values are numerical, and mode if the values are categorical. However, even this has its drawbacks since sometimes the accurate picture cannot be depicted if a lot of the data is missing.
For what applications are random forests used?
In finance, healthcare, and e-commerce, it can be used to make suggestions and recommendations for the above.
Why are random forests popular?
Due to the fact that it has become a potent weapon for modern data scientists to refine their predictive models since there are very few assumptions attached to it so data preparation is easier which causes a decrease in time consumption.
Can random forests be used both for Continuous and Categorical Target Variables?
Yes, they can be used for either.
Wrap Up
All in all, random forests prove themselves to be an excellent algorithm that gets rid of the problems brought forth by decision trees and increases the accuracy of the result that you desire. A huge benefit is that since the bagging technique is used and multiple decision trees are created, it is a very explainable algorithm and one that can be used to convince those who have asked for a problem on which the method applies. Explainability is a huge thing nowadays, and not only that, but random forests also reduce the overfitting of the data, which is a massive issue in Machine Learning!
