Top 50 Machine Learning Interview Questions with Answers

A typical machine learning interview involves multiple rounds of rigorous assessments that test applicants’ technical and theoretical knowledge. In this article, we will attempt to cover a few of the most common machine learning interview questions to help you prepare for you next ML interview. Let’s start!!!

Basic Machine Learning Interview Questions

Q1. How will you classify machine learning algorithms?

Ans. We classify ML algorithms on the presence or absence of target variables.

a. Supervised learning:

Target variables are present in this subtype. Supervised Learning models learn by analyzing data that has been labeled. Before running predictions on new data, the model trains on a labeled dataset.
Algorithms that fall under this category include Naive Bayes, Logistic Regression, K Nearest Neighbors etc

b. Unsupervised learning:

There are no target variables in this subtype. Unsupervised learning models train on unlabeled data with no targets or instructions. They automatically identify patterns and trends in the data and create clusters. Singular Value Decomposition, Principal Component Analysis and similar algorithms fall into this bracket

c. Reinforcement Learning:

The model gains knowledge via trial and error. Here, models interact with their environment to make decisions and get feedback on these decisions, thus helping to improve accuracy and efficiency in the long run.

Q2. What are the steps involved in implementing a machine learning project?

Ans. We can outline the steps of machine learning project from ideation to completion in 8 steps. These include:

• Parameter tuning
• Data preparation
• Data collection
• Dataset split
• Training the model
• Model evaluation
• Result Prediction
• Result Validation

Q3. What does the term “instance-based learning” mean?

Ans. Instance Based Learning is a suite of regression and classification techniques that predict class labels based on commonalities to the closest neighbors in the training data set. These algorithms simply gather all of the data and provide a response when requested. Simply put, they are a set of procedures for solving new problems that are based on previous solutions to problems that are similar to the current problem.

Q4. Are AI, ML and DL different terms for the same entity?

Ans. Artificial intelligence (AI) is the study of the development of sophisticated computers. Machine learning (ML) refers to technologies capable of learning from experience (training data), while deep learning refers to systems that can learn from experiences on large data sets (DL).

AI can be viewed as a subset of machine learning. Deep Learning (DL) is comparable to machine learning (ML), except it works better with large data sets.. The relationship between AI, ML, and DL is broadly depicted in the diagram below:

Q5. What is the best way to apply machine learning to hardware?

Ans. In order to implement ML concepts in actual, physical hardware systems, one must first design ML algorithms in System Verilog, which is a hardware-specific development language, and then convert the said algorithm into an FPGA program.

Q6. Establish a distinction between regression and classification.

Ans. Under the umbrella of supervised machine learning, regression and classification are grouped together. The distinction is that the regression output variable is numerical, whereas the classification output variable is categorical (. For example, forecasting the exact temperature of a location is a regression problem, but predicting whether the day will be sunny, overcast, or rainy is a classification problem.

Q7. When do we pick classification over regression?

Ans. Prediction falls under both classification and regression. The process of classification entails identifying values or things that belong to a given category. On the other hand, the regression approach comprises predicting a response value from a series of outcomes.

When the output of the model has to produce the belongingness of data points in a dataset to a certain category, classification is preferred over regression.

We have some fruits, for example. We’re not interested in learning how apples relate to oranges. Rather, we would look to see if each fruit belongs in the apple or orange categories.

Q8. Explain Logistic Regression in a few words.

Ans. A classification procedure called logistic regression predicts a binary result for a collection of independent factors.

The result of logistic regression is a 0 or 1, with a threshold value of about 0.5. Any number more than 0.5 is a 1, while any value less than 0.5 is a 0.

Q9. Why does logistic regression fall under classification rather than regression?

Ans. Given the qualitative aspect of the target field, linear regression generates an unusual function that must be wrapped in a log function in order to use regression as a classifier. Therefore, it is a classification rather than a regression technique that can be derived from the cost function

Q10. What is Machine Learning Overfitting and how can you avoid it?

And. When a machine attempts to learn from an insufficient dataset, it is said to be overfitting. As a result, overfitting is proportional to the amount of data.

We use the cross-validation approach to avoid overfitting in small datasets. We will partition the dataset into two portions using this method. Testing and training sets will be included in these two areas. The training dataset trains the model, and the testing dataset tests it for new inputs.

Q11. Explain the difference between a validation set and a test set?

Ans. While building a model, we divide the data into three categories:

The training set is what we use to create the model and tweak the model’s variables. However, we cannot rely on the model built on top of the training set to be right. When fresh inputs are fed into the model, it may produce inaccurate results.

Validation set: We use a validation set to look at the model’s response when there are samples in the training dataset that don’t exist. Then, using the estimated benchmark of the validation data, we’ll modify hyperparameters.

When we use the validation set to evaluate the model’s response, we are inadvertently training the model using the validation set. This may result in the model being overfitted to specific data. As a result, our model will be unable to provide the necessary reaction to real-world data.

The test set is a subset of the actual dataset that the model hasn’t seen yet. This dataset is unknown to the model. The test dataset assesses the model’s performance.

Q12. Explain decision trees and how they work?

Ans. A decision tree is a diagram that depicts the steps to follow in order to achieve the desired result. It’s a diagram that illustrates the actions in a hierarchical order.

On the basis of the hierarchy of actions that we’ve established, we may design a decision tree algorithm.

Q13. Differentiate between information gain and entropy.

Ans. Entropy measures how scrambled your data is. It decreases as you get closer to the leaf node.
The decrease in entropy when a dataset is divided on an attribute is used to calculate the Information Gain. It increases as you get closer to the leaf node.

Q14. What do ROC curves indicate?

Ans. The term “ROC” stands for “Receiver Operating Characteristic.” The ROC curve graphically shows the trade-off between True and False positive rates.

The AUC (Area Under the Curve) in ROC offers us an estimate of the model’s accuracy.

An ROC curve is seen in the graph above. The greater the Area Under the Curve, the better the model’s performance.

Q15. How do you handle datasets with missing or corrupted data?

Ans. Dropping certain rows or columns or replacing them totally with another value is one of the simplest methods to deal with missing or incorrect data.

In Pandas, there are two helpful methods:

• IsNull() and dropna() will assist you in locating and dropping missing data columns/rows.
• Fillna() will use a placeholder value to replace the incorrect values.

Q16. What does ‘naive’ mean in the context of the Naive Bayes Classifier?

Ans. Because it makes assumptions that may or may not be right, the classifier is “naive.”

Given the class variable, the method assumes that the existence of one feature of a class is unrelated to the presence of any other feature (absolute independence of features).

For example, a vegetable can be classified as a tomato if it is red in color and has a round form, independent of other characteristics. This assumption might be correct or incorrect (as an apple also matches the description).

Q17. Which machine learning algorithm should you use for your classification problem?

Ans. While there are no hard and fast rules for selecting an algorithm for a classification task, you can use the following guidelines:

• Test alternative algorithms and cross-validate them if accuracy is an issue.
• Use models with low variance and high bias if the training dataset is small.
• Use models with high variance and low bias if the training dataset is big.

Q18. Define the terms “precision” and “recall.”

Ans. Precision – The ratio of several events you can accurately recollect to the whole number of events you recall is called precision (mix of correct and wrong recalls).

Recall – The ratio of the number of events you can recall to the total number of occurrences is called a recall.

Q19. What does selection bias mean ?

Ans. Statistical inaccuracies cause bias in the sampling component of an experiment. Because of the inaccuracy, the model chooses one sample group more frequently than the other groups in the experiment.

In case detection of selection bias doesn’t happen, it may lead to an incorrect conclusion.

Q20. What is Semi-supervised Machine Learning, and how does it work?

Ans. Unsupervised learning utilizes no training data whereas supervised learning uses fully labeled data.

In semi-supervised learning, there is a small quantity of labeled data and a big amount of unlabeled data in the training set.

Q21. With a simple example, explain false negative, false positive, true negative, and true positive.

Ans. Consider a cancer diagnosis scenario:

• True Positive: If a tumor exists and is detected.The system’s prediction is correct.
• False Positive: If a tumor is diagnosed but it doesn’t exist, it is a false positive.
• False Negative: No tumor is diagnosed even though it exists. The system did not find a tumor, which was incorrect
• True Negative: If there was no tumor and no diagnosis.

Q22. Explain Confusion Matrix in a machine learning context.

Ans. A confusion matrix or error matrix, is a table that summarizes the results of a classification process.

Consider the following table:

• TN stands for True Negative.
• TP stands for True Positive.
• FN stands for False Negative.
• FP stands for False Positive.

Q23. Is it preferable to have a large number of false positives or a large number of false negatives? Explain.

Ans. It relies on the topic as well as the domain for which the problem is in. If you’re employing Machine Learning in the field of medical testing, a false negative is a big concern, because the report won’t reveal any health issues even if the individual is sick. Similarly, in identifying spam, a false positive is extremely dangerous since the system may mistakenly label a critical email as spam.

Q24. What is the difference between collinearity and multicollinearity?

Ans. Collinearity occurs when two predictors (a and b for example) in a multiple linear regression show some connection.
Multicollinearity occurs when more than two predictor variables (a, b and c for example) are connected to each other.

Q25. How do you handle a model with low bias and a large variance?

Ans. When the model’s predicted outcome value is exceptionally close to the actual output or result expected, it is said to have low bias. In this case, we can use bagging techniques such as the random forest.

Q26. Distinguish between the random forest and the gradient boosting algorithms.

Ans. Random Forest uses bagging techniques, whereas GBM uses boosting techniques. Random forests minimize variance, whereas GBM reduces both bias and variance in a model.

Q27. What are some popular algorithms of Machine Learning?

Ans. Some popular ML Algorithms are:

• Decision Trees
• Neural Networks (back propagation)
• Probabilistic networks
• Nearest Neighbor
• Support vector machines(SVM)

Q28. In a supervised learning issue, what do you mean by Cost Function? How does it assist you?

Ans. The cost function calculates the average difference between all of the hypothesis’s outcomes with inputs from x’s and real outputs from y’s, and assists in determining the best straight line to our data.

Q29. What are the advantages of regularization?

Ans. Tuning the model’s complexity via regularization is one technique to discover a reasonable bias-variance balance. Regularization is a powerful tool for dealing with collinearity (a high degree of correlation between features), filtering out distortion from data, and avoiding overfitting. Regularization plays on the idea of adding more information (bias) to punish excessive parameter weights.

Q30. Classify learning problems.

Ans. The learning problem is a regression issue when the variable that we’re predicting is continuous.

When the target variable can only take on a tiny number of values, its learning issue is a classification task when y can really only assume a restricted number of discrete values.

Advanced Machine Learning Interview Questions with Answers

Q31. What are the benefits of using Naive Bayes?

Ans. Because the Naive Bayes classifier converges faster than discriminative models like logistic regression, less training data is required. The key benefit is that it is incapable of learning feature interactions.

Q32. What is inductive machine learning?

Ans. Inductive machine learning is the process of learning by example, in which a program attempts to infer an universal rule from a set of known cases.

Q33. Compare and contrast between Supervised, Unsupervised, and Semi-Supervised Machine Learning?

Ans. Below is the difference between Supervised, Unsupervised, and Semi-Supervised Machine Learning:

• Supervised Learning: We train models on labeled data, and then it makes predictions taking clues from previously labeled data. Labels act as the supervisor to train the data. E.g., text classification.
• Unsupervised Learning: We train models on unlabeled data. Models try to find patterns, relationships in the data and classify the classes according to that. We don’t have any labeled data.
• Semi-Supervised Learning: It uses labeled data and unlabeled data to train the model. Through this, the model aims to classify unlabeled data taking clues from labeled data.

Q34. In machine learning, what is model selection?

Ans. Model selection refers to the process of choosing a model from among several mathematical models that represent the same data collection. In the domains of statistics, machine learning, and data mining, model selection is important.

Q35. What are the various kinds of data in Machine Learning?

Ans. There are two different types of data. Data that is structured and unstructured.

• Structured Data: Before being placed in a data storage, this sort of data is predetermined, labeled, and well-formatted. Table of Student Records, for example.
• Unstructured Data: Unstructured data is stored in its original format and is not processed until models need it. Text, audio, video, emails, and so on.

Q36. Explain the distinctions between causation and correlation.

Ans. Causality refers to situations in which an act, like X, leads to an end, like Y, while correlation merely pertains to the connection between one act (X) and another response (Y), despite the fact that X does not necessarily result in Y.

Q37. What exactly are outliers, and how can we deal with them in Machine Learning?

Ans. Outliers are data points or samples that are uncommon in comparison to the remainder of the dataset. They have a big influence on the model’s performance. We deal with them in the following ways:.

• Get rid of all the outliers.
• Substitute a reasonable value for the outliers (Like 3rd deviation)
• Make use of a different algorithm that is independent of outliers.

Q38. Explain A/B Testing.

Ans. A/B Testing is a statistical hypothesis test to randomize two-variable tests. Its most important use is to evaluate two models with different predictor variables to see which one best matches the data.

Assume you construct two models that propose things to customers in a real-world setting. It is possible to compare two models using A/B testing to see which one provides the best suggestions.

Q39. What is Machine Learning Cross-Validation?

Ans. It’s a method for improving model performance by feeding several samples of data from a dataset into the model. The data is broken down into smaller pieces with the same amount of rows for the sampling procedure. We choose one component at random for the exam, and another for train sets. It consists of the following methods:

• Cross-validation using k-folds
• Method of stalling
• Stratified Cross-validation using k-folds
• Disable Cross-validation with p-out.

Q40. What is a pipeline?

Ans. Machine learning workflows are automated using a pipeline. We define a pipeline as a series of procedures in the training of a model. Because these pipelines are iterative, each step is performed multiple times to increase the model’s accuracy.

The pipeline is mostly applicable in natural language processing. On the one hand, one component of the pipeline is responsible for cleaning and vectorization, while another is responsible for model training and validation.

Q41. What is PCA and how can it help you?

Ans. PCA (Principal Component Analysis) is a dimensionality-reduction technique for reducing the size of huge data sets. We frequently encounter datasets with huge dimensions in real life, which makes displaying and interpreting them challenging. By eliminating extraneous dimensions from the dataset, PCA can assist to decrease its dimensionality.

Q42. Why is it required to scale and alter features?

Ans. Feature transformation is a technique for changing the representation of features. Feature scaling, on the other hand, is a method of transforming all of a feature’s values into the same range.

Sometimes in our dataset, we have columns with various units, for example one column for age and another for the person’s wage. The age column in this scenario spans from 0 to 100, and the salary column from 0 to 10000. Because the values of both columns differ so much, the column with the bigger values will have a greater impact on the outcome. As a result, the model will underperform. As a result, we must perform feature scaling and transformation.

Q43. How do you deal with a dataset that is unbalanced?

Ans. In unbalanced data, samples within each class will differ dramatically. One class, for example, may include 1000 samples. Another class, on the other hand, could only include 200–300 samples. In such situations, we must first address the data imbalance before proceeding. There are a lot of different approaches to consider:

• When we have a vast amount of data, we can use oversampling.
• Use undersampling.
• Attempt a different algorithm.

Q44. What Does K-Means Mean?

Ans. The basic unsupervised learning approach is a K-means clustering. This is a method of data classification that employs a collection of clusters called K clusters. It organizes data in order to uncover patterns of resemblance. It entails defining K centers, one for each cluster.

We divide the clusters into K groups, where K is a fixed number. Cluster centers are chosen at random from the K points. The items are assigned to the cluster center that is closest to them. The objects in a cluster are as similar as possible to one another and as different as feasible from the objects in other clusters. For huge data sets, K-means clustering works quite well.

Q45. What are the steps to creating a decision tree?

Ans. Follow the below steps to create a decision tree:

• Assume that the complete data set is input.
• Look for a split that optimizes the class distinction. Any test that splits data into two groups is a split. Apply the split to the data you’ve already had (divide step)
• Apply steps 1–2 to the split data once more.
• When you reach a certain point, you must come to a halt. Pruning is the term for this process.
• When you’ve gone too far with the splits, clean up the tree.

Q46. Why is data cleansing so important in analysis?

Ans. Cleaning data from multiple sources to transform it into a layout that data scientists or analysts can use is a time-consuming process because the time it would take to clean the data increases in proportion to the number of references and the amount of information produced in these inputs. Cleaning data can take up to 80% of the time, making it a crucial element of the analytical process.

Q47. What is Root Cause Analysis and how does it work?

Ans. The goal of root cause analysis is to investigate industrial accidents, but it is now widely employed in a variety of fields. It is essentially a problem-solving approach for determining the fundamental causes of flaws or difficulties. If removing an element from the problem-fault-sequence prevents the ultimate unwanted event from occurring, it is called a root cause.

Q48. What is Dimensionality Reduction, and how does it work?

Ans. On top of features and parameters, we develop Machine Learning models in the actual world. These characteristics might be multi-dimensional and many. The characteristics may be irrelevant at times, making it difficult to visualize them.

With the aid of primary variables, we apply dimensionality reduction to reduce the number of unnecessary and duplicated features. These primary variables are a subset of the parent variables that keep the parent variables’ characteristics.

Q49. Explain the technique of ensemble learning in Machine Learning.

Ans. Ensemble learning is a method for creating many Machine Learning models that we then integrate to provide more precise results. To create a broad Machine Learning model, we use the full training data set. In Ensemble Learning, on the other hand, we divide the training data set into numerous subsets, each of which is utilized to develop a different model. After training, we integrate the models to predict a result in such a way that the output variance is minimized.

Q50. What is the difference between bias and variance in machine learning?

Ans. The discrepancy between our model’s average forecast and the correct value is known as bias. When the bias value is large, the model’s forecast is inaccurate. As a result, in order to achieve the required predictions, the bias value should be as low as feasible.

Variance is a quantity that represents the difference between a training set’s predicted value and the expected value of other training sets. A lot of volatility might lead to a lot of variation in the result. As a result, the output of the model should have a low variance.

Conclusion

We have gone over 50 of the most important questions to prepare ahead of any machine learning interview. Good luck for your interview!!