10 Frequently Asked Machine Learning Interview Questions and Answers

Multisoft Virtual Academy | May 16, 2018 | Interview & Answer, Interview Question & Answer, machine learning online course, Training And Certification | No Comments

Machine Learning (ML) is a fast developing technological field today. It is basically the application of Artificial Intelligence (AI) that enables the computer systems to learn from their experience, just like human beings, and make sensible decisions based on this experiential knowledge. It offers a sort of cognitive ability to the computer-based machines and systems, through advanced algorithms, that enables them to perform certain intelligent tasks on their own, without any explicit command or input.

Though a lot of work has been done in the ML field, there is still a lot to be done. There is immense scope for research and development in this field. It is a career field that is growing at an increasing rate and giving rise to many rewarding and promising job opportunities for aspiring candidates.

If you are also seeking a job in this field and going to face an interview soon, you must be prepared well. We have given below the ten frequently asked machine learning interview questions and answers that will help you in facing the interview confidently:

Table of Contents

Q1. What is ‘Overfitting’ in Machine learning?

Ans. In machine learning, overfitting happens when a model learns the detail and noise in the training data to the extent that it begins to adversely impact the performance of the model on new data. This signifies that the noise or random fluctuations in the training data are considered and learned as concepts by the model. The problem occurs when such concepts do not apply to a new set of data and negatively impact the model’s capability to generalize. Overfitting is generally witnessed in a highly complex model.

Q2. Differentiate between Data Mining and Machine learning?

Ans. Machine learning has to do with the study, design and development of the algorithms that lend computers the ability to learn through experience, without the need for any external input. On the other hand, data mining can be defined as the process of finding patterns in a large data set in order to discover meaningful patterns. This process makes use of machine learning algorithms.

Q3. What’s the difference between inductive and deductive machine learning?

Ans. In inductive machine learning, the model learns with the aid of the examples obtained from a set of observed instances to draw generalized conclusions. On the contrary, in deductive machine learning, several statements are linked in a logical format according to certain predefined rules to obtain new statements. Actually, inductive learning is based on instructions and deductive learning is based on experience.

Q4. Can you define parametric models and cite some examples of them?

Ans. Parametric models are basically the models with a limited number of parameters. In such models, you are only required to know the parameters of the model to predict new data. There are many examples of parametric models such as logistic regression, linear regression, and linear SVMs.

Q5. Define the “Curse of Dimensionality.”

Ans. “Curse of Dimensionality” refers to the difficulty of searching through a space with multiple dimensions; more dimensions lead to more difficulty. For example, it would be easier to scan a linear pathway, rather than a rectangular field. If we discuss this term, essentially in the context of machine learning, it indicates towards the difficulty associated with non-intuitive properties of data observed when working in a high-dimensional space.

Q6. What are the popular machine learning algorithms in use today?

Ans. There are many machine learning algorithms in use today, but the most popular ones are:

Decision Trees
Naive Bayes Classification
Ordinary Least Squares Regression
Logistic Regression
Support vector machines
Ensemble Methods
Clustering Algorithms
Principal Component Analysis
Singular Value Decomposition
Independent Component Analysis

Q7. Why is “Naïve Bayes,” called naive?

Ans. Though “Naïve Bayes” find its application in various areas like in text mining, this algorithm is called or considered naïve because it makes certain assumptions that may or may not hold true or sensible in the real life. It works on an individual probability method that signifies absolute independence of features; such a condition is seldom met in real life. To cite an amusing example from Quora, a user commented that a Naive Bayes classifier that figured out that you liked pickles and ice cream would perhaps ingenuously recommend you to go for a pickle ice cream.

Q8. How will you choose a machine learning algorithm that is most pertinent to your classification problem?

If accuracy is our top priority, then the best way to choose the most pertinent machine learning algorithm would be to test a couple of different algorithms at random (try different parameters within each algorithm) and choose the one that best serves the purpose. As a rule of thumb, choose a machine learning algorithm for your classification based on the size of your training set. In case of small training sets, low variance/high bias classifiers like Naïve Bayes can be used effectively, while for large training sets, high variance/low bias classifiers like k-nearest would prove to be the best choice.

Q9. Can you define a neural network and tell some of its advantages and disadvantages?

Ans. In the field of Information Technology (IT), a neural network is basically a system of hardware and/or software similar to the pattern of neurons in the human brain; it is also said to be an important part of deep learning. The biggest advantage delivered by neural networks is that they lead to the performance breakthroughs for unstructured datasets like images, audio, and video.

Their unmatched flexibility enables them to learn the patterns which cannot be achieved by any other ML algorithm. However, these networks are also not free from defects; the biggest downside with neural networks is that they require a massive volume of training data to work optimally. Also, it is not easy to select the right architecture for these networks due to their incomprehensible internal layers.