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Top 45 Machine Learning Interview Questions Answered for 2022 Simplilearn

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Research Development And Data Analysis (EDUC 555)

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Home Resources AI & Machine Learning Machine Learning Tutorial: A Step-by-Step Guide for Beginners Top 45 Machine Learning Interview Questions Answered for 2022

By Eshna Verma

358349

Top 45 Machine Learning Interview Questions Answered for 2022

Lesson 31 of 32

Last updated on Jul 18, 2022

Table of Contents

Top Machine Learning Interview Questions

Become Part of the Machine Learning Talent Pool

Companies are striving to make information and services more accessible to people by adopting new-age technologies like articial intelligence (AI) and machine learning. One can witness the growingadoptionofthesetechnologiesinindustrialsectorslikebanking nance retail

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growing adoption of these technologies in industrial sectors like banking, nance, retail, manufacturing, healthcare, and more.

Data scientists, articial intelligence engineers, machine learning engineers, and data analysts are some of the in-demand organizational roles that are embracing AI. If you aspire to apply for these types of jobs, it is crucial to know the kind of machine learning interview questions that recruiters and hiring managers may ask. 

This article takes you through some of the machine learning interview questions and answers, that you9re likely to encounter on your way to achieving your dream job.

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Top Machine Learning Interview Questions

Let's start with some commonly asked machine learning interview questions and answers.

  1. What Are the Different Types of Machine Learning?

There are three types of machine learning:

Supervised Learning

In supervised machine learning, a model makes predictions or decisions based on past or labeled data. Labeled data refers to sets of data that are given tags or labels, and thus made more meaningful.

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  1. What is 8training Set9 and 8test Set9 in a Machine Learning Model? How Much Data Will You Allocate for Your Training, Validation, and Test Sets?

There is a three-step process followed to create a model:

  1. Train the model
  2. Test the model 
  3. Deploy the model

Training Set Test Set

The training set is examples given to the model to analyze and learn 70% of the total data is typically taken as the training dataset This is labeled data used to train the model

The test set is used to test the accuracy of the hypothesis generated by the model Remaining 30% is taken as testing dataset We test without labeled data and then verify results with labels

Consider a case where you have labeled data for 1,000 records. One way to train the model is to expose all 1,000 records during the training process. Then you take a small set of the same data to test the model, which would give good results in this case.

But, this is not an accurate way of testing. So, we set aside a portion of that data called the 8test set before starting the training process. The remaining data is called the 8training set9 that we use for

training the model. The training set passes through the model multiple times until the accuracy is high, and errors are minimized.

Now, we pass the test data to check if the model can accurately predict the values and determine if training is effective. If you get errors, you either need to change your model or retrain it with more data.

Regarding the question of how to split the data into a training set and test set, there is no xed rule, and the ratio can vary based on individual preferences. 

  1. How Do You Handle Missing or Corrupted Data in a Dataset?

One of the easiest ways to handle missing or corrupted data is to drop those rows or columns or replace them entirely with some other value.

There are two useful methods in Pandas:

IsNull() and dropna() will help to nd the columns/rows with missing data and drop them Fillna() will replace the wrong values with a placeholder value

  1. How Can You Choose a Classier Based on a Training Set Data Size?

When the training set is small, a model that has a right bias and low variance seems to work better because they are less likely to overt. 

For example, Naive Bayes works best when the training set is large. Models with low bias and high variance tend to perform better as they work ne with complex relationships.

Articial Intelligence Engineer

For a model to be accurate, the values across the diagonals should be high. The total sum of all the values in the matrix equals the total observations in the test data set. 

For the above matrix, total observations = 12+3+1+9 = 25

Now, accuracy = sum of the values across the diagonal/total dataset

= (12+9) / 25

= 21 / 25

= 84%

  1. What Is a False Positive and False Negative and How Are They Signicant?

False positives are those cases that wrongly get classied as True but are False. 

False negatives are those cases that wrongly get classied as False but are True.

In the term 8False Positive,9 the word 8Positive9 refers to the 8Yes9 row of the predicted value in the confusion matrix. The complete term indicates that the system has predicted it as a positive, but the actual value is negative. 

So, looking at the confusion matrix, we get:

False-positive = 3

True positive = 12

Similarly, in the term 8False Negative,9 the word 8Negative9 refers to the 8No9 row of the predicted value in the confusion matrix. And the complete term indicates that the system has predicted it as negative, but the actual value is positive.

So, looking at the confusion matrix, we get:

False Negative = 1

True Negative = 9

  1. What Are the Three Stages of Building a Model in Machine Learning?

The three stages of building a machine learning model are:

Model Building

Choose a suitable algorithm for the model and train it according to the requirement  Model Testing

Check the accuracy of the model through the test data  Applying the Model

Make the required changes after testing and use the nal model for real-time projects

Here, it9s important to remember that once in a while, the model needs to be checked to make sure it9s working correctly. It should be modied to make sure that it is up-to-date.

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  1. What is Deep Learning?

The Deep learning is a subset of machine learning that involves systems that think and learn like humans using articial neural networks. The term 8deep9 comes from the fact that you can have several layers of neural networks. 

By providing images regarding a disease, a model can be trained to detect if a person is suffering from the disease or not. Sentiment Analysis

This refers to the process of using algorithms to mine documents and determine whether they9re positive, neutral, or negative in sentiment.  Fraud Detection

By training the model to identify suspicious patterns, we can detect instances of possible fraud.

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  1. What is Semi-supervised Machine Learning?

Supervised learning uses data that is completely labeled, whereas unsupervised learning uses no training data.

In the case of semi-supervised learning, the training data contains a small amount of labeled data and a large amount of unlabeled data.

  1. What Are Unsupervised Machine Learning Techniques?

 

There are two techniques used in unsupervised learning: clustering and association.

Clustering

Clustering problems involve data to be divided into subsets. These subsets, also called clusters, contain data that are similar to each other. Different clusters reveal different details about the objects, unlike classication or regression.

Association

Inanassociationproblem weidentifypatternsofassociationsbetweendifferentvariablesoritems

In an association problem, we identify patterns of associations between different variables or items.

For example, an e-commerce website can suggest other items for you to buy, based on the prior purchases that you have made, spending habits, items in your wishlist, other customers9 purchase habits, and so on.

  1. What is the Difference Between Supervised and Unsupervised Machine Learning?

Supervised learning - This model learns from the labeled data and makes a future prediction as output  Unsupervised learning - This model uses unlabeled input data and allows the algorithm to act on that information without guidance.

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  1. What is the Difference Between Inductive Machine Learning and Deductive Machine Learning?

 

Inductive Learning Deductive Learning

It observes instances based on dened principles to draw a conclusion Example: Explaining to a child to keep away from the re by showing a video where re causes damage

It concludes experiences Example: Allow the child to play with re. If he or she gets burned, they will learn that it is dangerous and will refrain from making the same mistake again

Building a machine designed to play such games would require many rules to be specied. 

With reinforced learning, we don9t have to deal with this problem as the learning agent learns by playing the game. It will make a move (decision), check if it9s the right move (feedback), and keep the outcomes in memory for the next step it takes (learning). There is a reward for every correct decision the system takes and punishment for the wrong one. 

  1. How Will You Know Which Machine Learning Algorithm to Choose for Your Classication Problem?

While there is no xed rule to choose an algorithm for a classication problem, you can follow these guidelines:

If accuracy is a concern, test different algorithms and cross-validate them If the training dataset is small, use models that have low variance and high bias If the training dataset is large, use models that have high variance and little bias

  1. How is Amazon Able to Recommend Other Things to Buy? How Does the Recommendation Engine Work?

Once a user buys something from Amazon, Amazon stores that purchase data for future reference and nds products that are most likely also to be bought, it is possible because of the Association algorithm, which can identify patterns in a given dataset. 

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  1. When Will You Use Classication over Regression?

Classication is used when your target is categorical, while regression is used when your target variable is continuous. Both classication and regression belong to the category of supervised machine learning algorithms. 

Examples of classication problems include:

Predicting yes or no Estimating gender Breed of an animal Type of color

Examples of regression problems include:

Estimating sales and price of a product Predicting the score of a team Predicting the amount of rainfall

  1. How Do You Design an Email Spam Filter?

Building a spam lter involves the following process:

The email spam lter will be fed with thousands of emails  Each of these emails already has a label: 8spam9 or 8not spam. The supervised machine learning algorithm will then determine which type of emails are being marked as spam based on spam words like the lottery, free offer, no money, full refund, etc. The next time an email is about to hit your inbox, the spam lter will use statistical analysis and algorithms like Decision Trees and SVM to determine how likely the email is spam If the likelihood is high, it will label it as spam, and the email won9t hit your inbox Based on the accuracy of each model, we will use the algorithm with the highest accuracy after testing all the models

Variance

Variance refers to the amount the target model will change when trained with different training data. For a good model, the variance should be minimized. 

Overtting: High variance can cause an algorithm to model the random noise in the training data rather than the intended outputs.

  1. What is the Trade-off Between Bias and Variance?

The bias-variance decomposition essentially decomposes the learning error from any algorithm by adding the bias, variance, and a bit of irreducible error due to noise in the underlying dataset. 

Necessarily, if you make the model more complex and add more variables, you9ll lose bias but gain variance. To get the optimally-reduced amount of error, you9ll have to trade off bias and variance. Neither high bias nor high variance is desired.

High bias and low variance algorithms train models that are consistent, but inaccurate on average.

High variance and low bias algorithms train models that are accurate but inconsistent. 

  1. Dene Precision and Recall.

Precision

Precision is the ratio of several events you can correctly recall to the total number of events you recall (mix of correct and wrong recalls).

Precision = (True Positive) / (True Positive + False Positive)

Recall

A recall is the ratio of the number of events you can recall the number of total events.

Recall = (True Positive) / (True Positive + False Negative)

  1. What is a Decision Tree Classication?

A decision tree builds classication (or regression) models as a tree structure, with datasets broken up into ever-smaller subsets while developing the decision tree, literally in a tree-like way with branches and nodes. Decision trees can handle both categorical and numerical data. 

  1. What is Pruning in Decision Trees, and How Is It Done?

Pruning is a technique in machine learning that reduces the size of decision trees. It reduces the complexity of the nal classier, and hence improves predictive accuracy by the reduction of overtting. 

Pruning can occur in:

Top-down fashion. It will traverse nodes and trim subtrees starting at the root Bottom-up fashion. It will begin at the leaf nodes

There is a popular pruning algorithm called reduced error pruning, in which:

Starting at the leaves, each node is replaced with its most popular class If the prediction accuracy is not affected, the change is kept There is an advantage of simplicity and speed

  1. Briey Explain Logistic Regression.

Logistic regression is a classication algorithm used to predict a binary outcome for a given set of independent variables. 

The output of logistic regression is either a 0 or 1 with a threshold value of generally 0. Any value above 0 is considered as 1, and any point below 0 is considered as 0.

  1. Explain the K Nearest Neighbor Algorithm.

 

K nearest neighbor algorithm is a classication algorithm that works in a way that a new data point is assigned to a neighboring group to which it is most similar. 

You can reduce dimensionality by combining features with feature engineering, removing collinear features, or using algorithmic dimensionality reduction.

Now that you have gone through these machine learning interview questions, you must have got an idea of your strengths and weaknesses in this domain.

  1. What is Principal Component Analysis?

Principal Component Analysis or PCA is a multivariate statistical technique that is used for analyzing quantitative data. The objective of PCA is to reduce higher dimensional data to lower dimensions, remove noise, and extract crucial information such as features and attributes from large amounts of data.

  1. What do you understand by the F1 score?

The F1 score is a metric that combines both Precision and Recall. It is also the weighted average of precision and recall. 

The F1 score can be calculated using the below formula:

F1 = 2 * (P * R) / (P + R)

The F1 score is one when both Precision and Recall scores are one.

  1. What do you understand by Type I vs Type II error?

Type I Error: Type I error occurs when the null hypothesis is true and we reject it.

Type II Error: Type II error occurs when the null hypothesis is false and we accept it.

  1. Explain Correlation and Covariance?

Correlation: Correlation tells us how strongly two random variables are related to each other. It takes values between -1 to +1. 

Formula to calculate Correlation:

Covariance: Covariance tells us the direction of the linear relationship between two random variables. It can take any value between - ∞ and + ∞.

Formula to calculate Covariance:

  1. What are Support Vectors in SVM?

Support Vectors are data points that are nearest to the hyperplane. It inuences the position and orientation of the hyperplane. Removing the support vectors will alter the position of the hyperplane. The support vectors help us build our support vector machine model.

  1. What is Ensemble learning?

Ensemble learning is a combination of the results obtained from multiple machine learning models to increase the accuracy for improved decision-making. 

Example: A Random Forest with 100 trees can provide much better results than using just one decision tree.

  1. What is Cross-Validation?

Cross-Validation in Machine Learning is a statistical resampling technique that uses different parts of the dataset to train and test a machine learning algorithm on different iterations. The aim of cross-validation is to test the model9s ability to predict a new set of data that was not used to train the model. Cross-validation avoids the overtting of data.

K-Fold Cross Validation is the most popular resampling technique that divides the whole dataset

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Top 45 Machine Learning Interview Questions Answered for 2022 Simplilearn

Course: Research Development And Data Analysis (EDUC 555)

26 Documents
Students shared 26 documents in this course

University: Capital University, Columbus Ohio

Was this document helpful?
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Home Resources AI & Machine Learning Machine Learning Tutorial: A Step-by-Step Guide for Beginners
Top 45 Machine Learning Interview Questions Answered for 2022
By Eshna Verma
358349
Top 45 Machine Learning Interview Questions Answered for 2022
Lesson 31 of 32
Last updated on Jul 18, 2022
Table of Contents
Top Machine Learning Interview Questions
Become Part of the Machine Learning Talent Pool
Companies are striving to make information and services more accessible to people by adopting
new-age technologies like articial intelligence (AI) and machine learning. One can witness the
growing adoption of these technologies in industrial sectors like banking nance retail
NextPrevious
Tutorial Playlist
Video Tutorials Articles Ebooks Free Practice Tests On-demand Webinars Live Webinars
AI & Machine Learning

Students also viewed

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