Machine Learning Interview Questions

Machine learning is one of the most sought-after technologies today, with businesses across industries embracing it to gain a competitive edge. As per recent reports, the global machine learning market size is expected to reach USD 117.19 billion by 2027, growing at a CAGR of 39.2% from 2020 to 2027. With such staggering growth potential, it is no surprise that the demand for skilled professionals in machine learning is skyrocketing.

If you are passionate about exploring data-driven solutions and have a strong grasp of statistical concepts, a career in machine learning could be the perfect fit for you. This interview aims to assess your knowledge of machine learning concepts, algorithms, and tools, and evaluate your problem-solving abilities to help you carve a successful career in this exciting field. If you're preparing for a career in machine learning, our comprehensive guide to Machine Learning Interview Questions will help you ace your interview.

We have divided these interview questions into a few Categories:

• Basic Machine Learning Interview Questions for Freshers
• Machine Learning Engineer Interview Questions
• Deep Learning Interview Questions
• Machine Learning Interview Questions for Experienced
• FAQs
• Tips

Let's get started!

Most Frequently asked Machine Learning (ML) Interview Questions How can you handle missing or corrupted data in a dataset? What do you understand by Machine Learning? How is Machine Learning different from Deep Learning? Why was Machine Learning introduced? Explain SVM Algorithm in detail. What is ‘Naive’ in a Naive Bayes? What is Ensemble learning? Define Precision and Recall. How do XML and CSVs compare in terms of size? What is A/B Testing?

Basic Machine Learning Interview Questions for Freshers

Q1) What is the Hypothesis of Machine Learning?

Ans: Machine Learning allows the use of available datasets to understand a specific function that maps input to output in the best possible way. This problem is known as function approximation. Here, approximation is required for the unknown target function that maps all plausible observations based on the given problem in the best way possible. A hypothesis in Machine learning is a model that helps approximate the target function and perform the necessary input-to-output mappings. The choice and configuration of algorithms allows you to define the space of plausible hypotheses that a model represents. In this process, lowercase h (h) is used for a specific hypothesis, while uppercase h (H) is used for the hypothesis space that is being searched. Let us briefly understand these notations: 

Hypothesis (h): A hypothesis is a specific model that helps map the input to output; the mapping can be used further for evaluation and prediction. 

Hypothesis set (H): The hypothesis set consists of a space of hypotheses that can be used to map inputs to outputs, which can search. The general constraints, here include the choice of problem framing, the model, and the model configuration.  

Q2) How can you handle missing or corrupted data in a dataset?

Ans: If you find missing or corrupted data in a dataset, you can drop those rows or columns or replace them with another value.

There are two beneficial methods in Pandas: isnull() and dropna (), which will help you find columns of data with missing or corrupted data and drop those values. You could use the fillna () method to replace the invalid values with a placeholder value (for example, 0).

Q3) What is a Decision Tree Classification?

Ans: A decision tree helps in building classification (or regression) models as a tree structure, with datasets further divided into ever-smaller subsets while developing the decision tree, literally in a tree-like structure with branches and nodes. Decision trees are efficient in handling both categorical and numerical data.

Q4) What are the last machine learning papers you’ve read?

 Ans: Keeping up with the latest scientific literature on machine learning is necessary to demonstrate an interest in a machine learning position. This overview of deep learning in nature by the scions of deep learning itself (from Hinton to Bengio to LeCun) is a good reference paper and an overview of what’s happening in deep learning — and the kind of paper you might want to cite.

Want to acquire industry skills and gain complete knowledge of Machine Learning? Enroll in Instructor-Led live Machine Learning Training to get Job Ready!

Q5) What do you understand by Machine Learning?

Ans: Machine learning is a type of artificial intelligence that focuses on developing computer programs that can access data, learn from it, and make decisions or predictions based on it. It uses algorithms to analyze data, identify patterns and make decisions with minimal human intervention.

Q6) What is Principal Component Analysis?

Ans: Principal Component Analysis, or PCA, is a multivariate statistical technique used to analyze quantitative data. PCA aims to reduce higher dimensional data to lower dimensions, remove noise, and extract crucial information, such as features and attributes, from large amounts of data.

Q7) How is Machine Learning different from Deep Learning?

Ans: Machine learning is all about algorithms that parse data, learn from it, and then apply what they've learned to make sound decisions.

Deep learning is a type of machine learning inspired by the human brain's structure and is particularly useful in feature detection.

Q8) What’s the F1 score? How would you use it?

Ans: The F1 score is a model's performance indicator. It is a weighted average of a model's precision and recall, with results closer to 1 being the best and those closer to 0 being the worst. It is used in classification tests where true negatives are unimportant.

Q9) Why was Machine Learning introduced?

Ans: A straightaway answer to this is to make our lives easier. Many systems used hardcoded rules containing "if" and "else" decisions to process data or adjust user input in the early days of "intelligent" applications. Consider a spam filter whose job is to move relevant incoming email messages to a spam folder.

Q10) How would you explain Machine Learning to a student?

Ans: Assume a friend invites you to his party, where you meet strangers. Because you know nothing about them, you will mentally categorize them based on gender, age group, clothing, etc. Strangers represent unlabelled data in this scenario, and classifying unlabelled data points is nothing more than unsupervised learning. Because you used no prior knowledge about people and classified them on the fly, this is an unsupervised learning problem.

Q11) What are the differences between Supervised and Unsupervised Machine Learning?

Ans: The machine is trained using labeled data in supervised machine learning. The learning model is then fed a new dataset so that the algorithm can provide a positive result by analyzing the labelled data. For example, we must first label the data to train the model before performing classification.

Unsupervised machine learning involves not training the machine with labeled data and allowing the algorithms to make decisions without any corresponding output variables.

 

Machine Learning Engineer Interview Questions

Q12) What is Overfitting, and How Can You Avoid It?

Ans: Overfitting is when a model performs too well on the training data but needs to generalize better to unseen data and can happen when a model is too complex for the data available or when parameters are overly adjusted to fit the training data. To avoid overfitting, you should use regularization techniques such as L1 and L2 regularization, dropout layers, and early stopping. You should also split your data into training, validation, and test sets to ensure the model performs well on unseen data. Additionally, it would help if you used cross-validation to help further evaluate the model's performance on unseen data.

Q13) What are the Different Types of Machine Learning algorithms?

Ans: Machine learning algorithms come in an assortment of flavors. Here is a list of them organized by broad category:

Whether they are trained with or without human supervision (supervised, unsupervised, reinforcement learning), The criteria in the diagram below are not mutually exclusive; we can combine them in any way we see fit.

Q14) Explain SVM Algorithm in detail.

Ans: Support Vector Machines (SVMs) are solid supervised machine learning algorithms for classification and regression tasks. SVMs work on the concept of decision planes to determine decision boundaries. A decision plane separates a set of objects with different class memberships. In SVM, each data item is represented as a point in n-dimensional space (where n is the number of features), with the value of each feature being the value of a particular coordinate.

The next step is to perform classification by finding the hyper-plane that differentiates the two classes properly. The support vectors decide the hyper-plane, simply the coordinates of individual observation. The distance between the hyper-plane and the support vectors is known as the margin. 

The goal is to choose a hyper-plane with the maximum possible margin between the hyper-plane and any of the support vectors. Kernel functions transform the data into a higher-dimensional space and then find an optimal hyper-plane in this higher-dimensional space that maximizes the margin between the support vectors. Once the hyper-plane is established, it can easily classify new data by computing the hyper-plane equation. 

If the distance from the data point to the hyper-plane is less than a threshold, then the data is classified as one class; otherwise, as the other class. SVMs are used for both linear and non-linear data sets and can be used for classification and regression tasks. SVMs are very effective in high-dimensional spaces with many features and are relatively memory efficient.

We have data (x1, y1), ..., (xn, yn), and different features (xii, ..., xip), and y is either 1 or -1.

The equation of the hyperplane H3 is the set of points satisfying:

w. x-b = 0

Where w is the normal vector of the hyperplane. The parameter b||w||determines the offset of the hyperplane from the original along the normal vector w

So, for each I, either x is in the hyperplane of 1 or -1. Basically, xi satisfies:

w. xi - b = 1 or   w. xi - b = -1

Q15) Explain the difference between classification and regression.

Ans: Classification is used to produce discrete results and to categorize data. For instance, separating emails into spam and non-spam categories. On the other hand, regression, works with continuous data. A good example of it will be predicting stock prices at a specific point. Classification is a technique for categorizing output into groups. For instance, will it be hot or cold tomorrow? Regression, on the other hand, is used to predict the relationship that data represents. For instance, what will the temperature be tomorrow?

Q16) What is ‘Naive’ in a Naive Bayes?

Ans: The Naive Bayes method is a supervised learning algorithm, and it is naive since it makes assumptions by applying Bayes’ theorem that all attributes are independent of each other.

Bayes’ theorem states the following relationship, given class variable y and dependent vector x1  through xn:

P(yi | x1,..., xn) =P(yi)P(x1,..., xn | yi)(P(x1,..., xn)

Using the naive conditional independence assumption that each xiis independent: for all I this relationship is simplified to:

P(xi | yi, x1, ..., xi-1, xi+1, ...., xn) = P(xi | yi)

Since, P(x1,..., xn) is a constant given the input, we can use the following classification rule:

P(yi | x1, ..., xn) = P(y) ni=1P(xi | yi)P(x1,...,xn)

and we can also use Maximum A Posteriori (MAP) estimation to estimate P(yi)and P(yi | xi) the former is then the relative frequency of class yin the training set.

P(yi | x1,..., xn)  P(yi) ni=1P(xi | yi)

y = arg max P(yi)ni=1P(xi | yi)

The different naive Bayes classifiers mainly differ by the assumptions they make regarding the distribution of P(yi | xi): can be Bernoulli, binomial, Gaussian, and so on.

Q17) Tell me how you will design an Email Spam Filter.

 Ans: Following are the steps to make a spam filter:

• The spam filter for email will be supplied to thousands of messages.
• All these emails are labelled as spam or not spam.'
• The algorithm for supervised machine learning will determine what kind of email is being flagged as spam based on terms such as lottery, freebie, No money, a complete refund, etc.
• Suppose an email is set to arrive in your inbox. In that case, the spam filter will employ algorithms and statistical analysis like Decision Trees and SVM to determine whether the email is spam.
• If the chance of receiving it is high, the email will mark it as spam and won't reach your inbox.
• In determining the precision of each model, we'll employ the algorithm with the most remarkable accuracy after conducting tests on all models.

Q18) What is Pruning in Decision Trees, and How Is It Done?

Ans: Pruning is a machine-learning technique. It is used for reducing the size of decision trees. It reduces the final classifier's complexity, which improves predictive accuracy by reducing overfitting.

Pruning can take place in the following ways:

Fashion from the top down. Starting at the root, it will traverse nodes and small subtrees.

Bottom-up style. It will start with the leaf nodes.

Reduced error pruning is a popular pruning algorithm in which:

Each node is replaced with its most popular class, beginning at the leaves.

The change is kept if the prediction accuracy is not affected. 

There are benefits to simplicity and speed. 

Q19) Can you explain How a System Can Play a Game of Chess Using Reinforcement Learning?

Ans: Reinforcement learning consists of two components - an environment and an agent. The agent performs some actions to achieve a specific goal. Every time the agent performs a task that takes it towards the goal, it is rewarded. And, every time it takes a step that goes against the goal or in the opposite direction, it is penalized. 

Earlier, chess programs were used to determine the best moves after a lot research on various factors. Building a machine designed to play such games require specific rules.

With reinforced learning, you do not have to deal with this problem. This is because the learning agent learns while playing the game. It will make a move (decision), check if it’s the correct move (feedback), and keep the outcomes in memory for the next step it takes (learning). There is a reward for every correct decision and punishment for the wrong one. 

Q20) Define the Confusion Matrix with Respect to Machine Learning Algorithms.

Ans: A confusion matrix (also known as an error matrix) is a specific table used to analyze the performance of an algorithm. It is mainly used in supervised learning but is also known as the matching matrix in unsupervised learning.

There are two parameters in the confusion matrix:

Actual \Predicted

It also has identical feature sets in both dimensions.

• Take the following as a confusion matrix (binary matrix):

Here,

For actual values:

Total Yes = 12+1 = 13

Total No = 3+9 = 12 

Similarly, for predicted values:

Total Yes = 12+3 = 15

Total No = 1+9 = 10 

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%

Q21) What is Ensemble learning?

Ans: Ensemble learning combines results from multiple machine learning models to improve decision-making accuracy.

For example, a Random Forest with 100 trees can produce far superior results than a single decision tree.

Q22) How will you elaborate Machine Learning to a school-going kid?

Ans: Assume a friend invites you to his party, where you meet strangers. Because you know nothing about them, you will mentally categorize them based on gender, age group, clothing, etc. Strangers represent unlabelled data in this scenario, and classifying unlabelled data points is nothing more than unsupervised learning.

Because you used no prior knowledge about people and classified them on the fly, this is an unsupervised learning problem.

 

Deep Learning Interview Questions

Q23) What do you mean by Precision and Recall?

Ans: Let me show you how to use an analogy:

Imagine that your girlfriend has given you each year a birthday gift for the past ten years. One day your girlfriend comes to you and asks, "Sweetie do you recall all the birthday presents you received from me?

Remember all ten incidents to remain on good terms with your partner. Thus, recall the amount of time you can recall accurately as a percentage of the total amount of events.

If you can remember each of the ten events accurately If you can do, your recall percentage is 1.0 (100 100 percent). If you recall seven events with accuracy, your recall ratio is 0.7 (70 percent)

But you may need to correct some answers.

For example, suppose that you made 15 guesses, of which ten were right and five were incorrect, implying that you could remember all the events but not as precisely.

Thus, accuracy is the proportion of the number of events you can correctly remember to the total number of events you can remember (a mix of incorrect and correct recalls).

In the previous example (10 real-life events, 15 responses, ten correct, five wrong), You will have 100% recall, but the accuracy of your recall is 66.67 percent (10 /15)

Q24) Define the ROC curve and its representation.

Ans: Operating the Receiver, The characteristic curve (or ROC curve) is a fundamental tool for diagnostic test evaluation that plots the actual positive rate (Sensitivity) against the false positive rate (Specificity) for the various diagnostic test cut-off points.

It demonstrates the tension between sensitivity and specificity (any increase in sensitivity will be accompanied by a decrease in specificity).

The more closely the curve follows the ROC space's left-hand and then top borders, the more accurate the test is.

The closer the curve gets to the ROC space's 45-degree diagonal, the less accurate the test is.

The slope of the tangent line gives the likelihood ratio (LR) for that value of the test at a cutpoint. The area under the curve represents test accuracy.

Q25) Tell What’s the difference between Gini Impurity and Entropy in a Decision Tree?

Ans:

• Gini Impurity and Entropy are the metrics for deciding how to split a Decision Tree.
• Gini measurement is the probability of a random sample being classified correctly if you pick a label according to the distribution in the branch at random.
• Entropy in simple words, is used to calculate the lack of information. You calculate the Information Gain (difference in entropies) by doing a split. It helps in reducing the uncertainty about the output label.

Q26) Explain the Ensemble learning technique in Machine Learning.

Ans: Ensemble learning is a technique for combining multiple Machine Learning models to produce more accurate results. The entire training data set builds a general Machine Learning model. In Ensemble Learning, however, the training data set is divided into multiple subsets, with each subset used to build a separate model. After the models have been trained, they are combined to predict an outcome so that the output variance is reduced.

Q27) What evaluation approaches would you work to gauge the effectiveness of a machine learning model?

Ans: To begin, divide the dataset into training and test sets, or use cross-validation techniques to segment the data into composite training and test sets. Then, implement a carefully selected set of performance metrics: here is a fairly comprehensive list. You could employ metrics such as the F1 score, accuracy, and confusion matrix. What matters here is that you show that you understand the nuances of how a model is measured and how to select the appropriate performance measures for the right situations.

Q28) What’s the “kernel trick” and how is it useful?

 Ans: The Kernel trick involves kernel functions that enable higher-dimension spaces without explicitly calculating the coordinates of points within that dimension. Instead, kernel functions compute the inner products between the images of all data pairs in a feature space and allow them the beneficial attribute of calculating the coordinates of higher dimensions while being computationally cheaper than the explicit calculation of said coordinates and can express Many algorithms in terms of inner products. The kernel trick enables us to run algorithms effectively in a high-dimensional space with lower-dimensional data.

Q29) What do you mean by Entropy in Machine Learning?

Ans: Entropy in Machine Learning is used for calculating the randomness in the data for processing. The more entropy in the given data, the more difficult it becomes to draw any valid conclusion. Let us take the example of a coin toss. This act is random because it does not favor either of the sides - heads or tails. Here, the result for any number of tosses cannot be predicted as there is no definite relationship between the action of flipping and the possible outcomes.

Q30) Differentiate between Classification and Regression in Machine Learning.

Ans: Machine Learning has various prediction problems based on supervised and unsupervised learning. They are classification, regression, clustering, and association. Classification and regression can be explained in the following ways:

Classification: A Machine Learning model has been created that assists in differentiating data into separate categories. The data is labelled and categorized according to the input parameters.

For example, predictions have to be made on the churning out of customers for a particular product based on some recorded data. There is a 50-50 probability whether a customer will churn out, or not. So, the labels for this will either be “Yes” and “No.”

Regression is creating a model for distinguishing data into continuous absolute values instead of using classes or discrete values. It identifies the distribution movement based on the historical data. It is also used for predicting the occurrence of an event depending on the degree of association of variables.

For example, the prediction of weather conditions depends on various factors. These include temperature, air pressure, solar radiation, elevation, distance from the sea, etc. The relation among these factors aids in predicting a proper weather condition.

Q31) What is the Variance Inflation Factor?

Ans: The variance inflation factor (VIF) estimates the volume of multicollinearity in a collection of many regression variables.

VIF = Variance of the model / Variance of the model with a single independent variable

This ratio is calculated for every independent variable. If VIF is high, it shows the independent variables' high collinearity.

Q32) Both being Tree-based Algorithms, how is Random Forest different from Gradient Boosting Machine (GBM)?

Ans: The main difference between a random forest and GBM is how the techniques are used. Random forest predictions use a technique called bagging, and thus is a little advanced. On the other hand, GBM make predictions with the help of a technique called boosting.

• Bagging: In bagging, we apply arbitrary sampling and divide the dataset into N. And then, we build a model by employing a single training algorithm. We combine the final predictions by polling, right after. Bagging also help in increasing the efficiency of a model by decreasing the variance to eschew overfitting.
  
  
• Boosting: In boosting, the algorithm reviews and corrects the inadmissible predictions at the initial iteration. Then, the algorithm’s sequence of iterations for correction continues until we get our required prediction. Boosting helps in reducing bias and variance for strengthening the weak learners.

 

Machine Learning Interview Questions and Answers for Experienced

Q33) Given two strings, A and B, of the same length n, find whether it is possible to cut both strings at a common point such that the first part of A and the second part of B is a palindrome.

Ans: You’ll often get standard algorithms and data structure questions as part of your interview process as a machine learning engineer, which might feel akin to a software engineering interview. In this comes from Google’s interview process. There are multiple ways to check for palindromes—one way of doing so if you’re using a programming language such as Python is to reverse the string and check if it still is equal to the original string, for example. Look out for the category of questions you can expect, akin to software engineering questions that drill down to your knowledge of algorithms and data structures. Make sure you’re comfortable with the language of your choice to express that logic.

Q34) Describe Rescaling of Data and explain how is it done.

Ans: In the real world, the attributes present in data are of different patterns. So, rescaling the characteristics to a standard scale is beneficial for algorithms to process data efficiently.

We can rescale data using Scikit-learn. The code for rescaling the data using MinMaxScaler is as follows:

#Rescaling data
import pandas
import scipy
import numpy
from sklearn.preprocessing import MinMaxScaler
names = ['Abhi', 'Piyush', 'Pranay', 'Sourav', 'Sid', 'Mike', 'pedi', 'Jack', 'Tim']
Dataframe = pandas.read_csv(url, names=names)
Array = dataframe.values
# Splitting the array into input and output
X = array[:,0:8]
Y = array[:,8]
Scaler = MinMaxScaler(feature_range=(0, 1))
rescaledX = scaler.fit_transform(X)
# Summarizing the modified data
numpy.set_printoptions(precision=3)
print(rescaledX[0:5,:])

Apart from the theoretical concepts, some interviewers also focus on implementing Machine Learning topics. The following Interview Questions are related to the implementation of theoretical concepts.

Q35) How can you handle duplicate values in a dataset for a variable in Python?

Ans: Consider the following Python code:

bill_data=pd. read_csv( ("datasetsTelecom Data AnalysisBill.csv")
bill_data. shape
#Identify duplicates records in the data
Dupes = bill_data.duplicated()
sum(dupes)
#Removing Duplicates
bill_data_uniq = bill_data.drop_duplicates()

Q36) Here’s a game where you are asked to roll two fair six-sided dice. If the sum of the values on the dice equals seven, then you win $21. However, you must pay $5 to play each time you roll both dice. Do you play this game? And in the follow-up: If he plays six times what is the probability of making money from this game?

Ans:

• The first condition states that if the sum of the values on the two dices is equal to 7, then you win $21. But for all the other cases, you must pay $5.
• First, let’s calculate the number of possible cases. Since we have two 6-sided dices, the total number of cases => 6*6 = 36.
• Out of 36 cases, we must calculate the number of cases that produces a sum of 7 (in such a way that the sum of the values on the 2 dices is equal to 7)
• Possible combinations that produce a sum of 7 is, (1,6), (2,5), (3,4), (4,3), (5,2) and (6,1). All these 6 combinations generate a sum of 7.
• This means that out of 36 chances, only 6 will produce a sum of 7. On taking the ratio, we get: 6/36 = 1/6
• So this suggests that we have a chance of winning $21, once in 6 games.
• So to answer the question if a person plays 6 times, he will win one game of $21, whereas for the other 5 games he will have to pay $5 each, which is $25 for all five games. Therefore, he will face a loss because he wins $21 but ends up paying $25.

Q37) Define Precision and Recall.

Ans: Precision and recall are two ways of keeping a track on the power of machine learning implementation. They are often used simultaneously.

Precision answers, “Out of all the items the classifier predicted to be relevant, how many are actually relevant?”

Whereas recall answers the question, “Out of all the genuinely relevant items, how many are found by the classifier?

In simple language, precision means exact and accurate. So the same will go for our machine learning model if you have a set of items your model needs to predict to be relevant. How many items are truly relevant?

Mathematically, precision and recall can be defined as the following:

precision = # happy correct answers/# total items returned by ranker

recall = # happy correct answers/# total relevant answers

Q38) What are Loss Function and Cost Functions? Explain the key Difference Between them.

Ans: When calculating loss, we consider only a single data point and then use the term loss function.

When calculating the sum of errors for multiple data, we use the cost function. There is no significant difference.

In other words, the loss function captures the difference between a single record's actual and predicted values, whereas cost functions aggregate the difference for the entire training dataset.

The Most commonly used loss functions are Mean-squared error and Hinge loss.

Mean-Squared Error (MSE): In simple words, we can say how our model predicted values against the actual values.

MSE = √ (predicted value - actual value)2

Hinge loss: It is used to train the machine learning classifier, which is

L(y) = max (0,1- yy)

Where y = -1 or 1, indicating two classes, and y represents the output form of the classifier. The most common cost function represents the total cost as the sum of the fixed costs and the variable costs in the equation y = mx + b

Q39) A jar has 1000 coins, of which 999 are fair and 1 is double headed. Pick a coin at random, and toss it 10 times. Given that you see 10 heads, what is the probability that the next toss of that coin is also a head?

Ans: There are two ways of choosing a coin. One is to pick a fair coin and the other is to pick the one with two heads.

Probability of selecting fair coin = 999/1000 = 0.999

Probability of selecting unfair coin = 1/1000 = 0.001

Selecting 10 heads in a row = Selecting fair coin * Getting 10 heads + Selecting an unfair coin

P (A) = 0.999 * (1/2)^10 = 0.999 * (1/1024) = 0.000976

P (B) = 0.001 * 1 = 0.001

P (A / A + B) = 0.000976 / (0.000976 + 0.001) = 0.4939

P (B / A + B) = 0.001 / 0.001976 = 0.5061

Probability of selecting another head = P(A/A+B) * 0.5 + P(B/A+B) * 1 = 0.4939 * 0.5 + 0.5061 = 0.7531

Q40) You are asked to build a multiple regression model but your model R² isn’t as good as you wanted. For improvement, you remove the intercept term now your model R² becomes 0.8 from 0.3. Is it possible? How?

Ans: Yes, it is possible.

The intercept term refers to model prediction without any independent variable or in other words, mean prediction

R² = 1 – ∑(Y – Y´)²/∑(Y – Ymean)² where Y´ is the predicted value.

In the presence of the intercept term, R² value will evaluate your model with respect to the mean model.

In the absence of the intercept term (Ymean), the model can make no such evaluation,

With large denominator,

Value of ∑(Y – Y´)²/∑(Y

Q41) What is Binarizing of Data? How to Binarize?

Ans: Converting data into binary values based on threshold values is known as binarizing of data. The values less than the threshold are set to 0, and those more remarkable than the threshold are set to 1. This process is helpful when feature engineering has to be performed and can also be used for adding unique features. Data can be binarized using Scikit-learn.

The code for binarizing data using Binarizer is as follows:

from sklearn.preprocessing import Binarizer
 import pandas
 import numpy
 names = [ Abhi', 'Piyush', 'Pranay', 'Sourav', 'Sid', 'Mike', 'pedi', 'Jack', 'Tim']
 dataframe = pandas.read_csv(ur1, names=names)
array = dataframe. values
# Splitting the array into input and output
X = array[:,0:8]
Y = array[:,8]
binarizer : = Binarizer(threshold=0.0).fit(X)
binaryx = binarizer.transform(X)
 # Summarizing the modified data
numpy.set_printoptions (precision=3)
print(binaryX[0:5,:])

Q42) There is a game where you are asked to roll two fair six-sided dice. If the sum of the values on the dice equals seven, then you win $21. However, you must pay $5 to play each time you roll both dice. Do you play this game? And in the follow-up: If he plays 6 times what is the probability of making money from this game?

Ans: The first condition states that if the sum of the values on the 2 dices is equal to 7, then you win $21. But for all the other cases you must pay $5.

First, let’s calculate the number of possible cases. Since we have two 6-sided dices, the total number of cases => 6*6 = 36.

Out of 36 cases, we must calculate the number of cases that produces a sum of 7 (in such a way that the sum of the values on the 2 dices is equal to 7)

Possible combinations that produce a sum of 7 is, (1,6), (2,5), (3,4), (4,3), (5,2) and (6,1). All these 6 combinations generate a sum of 7.

This means that out of 36 chances, only 6 will produce a sum of 7. On taking the ratio, we get: 6/36 = 1/6

So this suggests that we have a chance of winning $21, once in 6 games.

So to answer the question if a person plays 6 times, he will win one game of $21, whereas for the other 5 games he will have to pay $5 each, which is $25 for all five games. Therefore, he will face a loss because he wins $21 but ends up paying $25.

Q43) How to Implement the KNN Classification Algorithm?

Ans: Iris dataset is used for implementing the KNN classification algorithm.

Q44) Executing a binary classification tree algorithm is a simple. But how does tree split take place? How does the tree determine which variable to break at the root node and which at its child nodes?

Ans: Gini index and Node Entropy assist the binary classification tree in decision-making. Basically, the tree algorithm determines the feasible feature that is used to distribute data into the most genuine child nodes.

According to the Gini index, if we a randomly pick a pair of objects from a group, then they should be of identical class and the probability for this event should be 1.

The following are the steps to compute the Gini index:

Compute Gini for sub-nodes with the formula: The sum of the square of probability for success and failure (p^2 + q^2)

Compute Gini for split by weighted Gini rate of every node of the split

Now, Entropy is the degree of indecency that is given by the following:

Where a and b are the probabilities of success and failure of the node

When Entropy = 0, the node is homogenous

When Entropy is high, both groups are present at 50–50 percent in the node.

Finally, to determine the suitability of the node as a root node, the entropy should be very low.

Q45) If you found that your model is suffering from high variance. Which algorithm will be able to handle this situation according to you and why?

Ans: Handling High Variance

Bagging algorithm is the best pick for handling issues of high variance.

The bagging algorithm would split data into subgroups with a replicated sampling of random data.

Once the algorithm splits the data, we can use random data to create rules using a particular training algorithm. After that, we can use polling for combining the predictions of the model.

Q46) How do XML and CSVs compare in terms of size?

In practice, XML is much more verbose than CSVs and takes up much more space. CSVs use some separators to categorize and organize data in proper columns. XML uses tags to outline a tree-like structure for key-value pairs. You’ll often get XML back to semi-structured data from APIs or HTTP responses. In practice, you’ll want to ingest XML data and try to process it into a usable CSV. This question tests your familiarity with data wrangling, sometimes messy data formats.  

Q47) What is A/B Testing?

Ans: A/B Testing is a Statistical hypothesis testing for a randomized experiment with two variables, A and B. It is used to compare two models that use different predictor variables to check which variable fits best for a given sample of data.

Consider a scenario with two models (using different predictor variables) that can be used to recommend products for an e-commerce platform.

Can use A/B Testing to compare these two models to check which one best recommends products to a customer.

 

Machine Learning FAQs

1. How do I prepare for a machine learning interview?

Ans:

• Brush up on the basics of machine learning and statistics
• Practice implementing machine learning algorithms on real datasets
• Study common machine learning interview questions and practice answering them
• Research the company you're interviewing with and familiarize yourself with their current projects and technology stack

2. What are the 4 basics of machine learning?

 Ans: The four basics of machine learning are:

• Data collection and preparation
• Model selection and training
• Evaluation and validation
• Prediction or inference on new data

3. What are some common machine learning interview questions? 

Ans: Some common machine-learning interview questions include:

• What is the difference between supervised and unsupervised learning?
• What is overfitting and how can you prevent it?
• What is regularization and why is it important?
• What evaluation metrics would you use for a binary classification problem?
• Explain the bias-variance tradeoff.

4. What are the 7 steps of machine learning? 

Ans: The seven steps of machine learning are:

• Define the problem and collect the data
• Pre-process the data (cleaning, normalization, feature engineering)
• Split the data into training and testing sets
• Choose a model and train it on the training set
• Evaluate the model on the testing set
• Tune hyperparameters to improve the model's performance
• Deploy the model and use it to make predictions on new data

5. What are the 3 types of machine learning? 

Ans: The three types of machine learning are:

• Supervised learning, where the model is trained on labeled examples and learns to predict labels for new, unseen data
• Unsupervised learning, where the model learns to find patterns in unlabeled data without any specific target variable to predict
• Reinforcement learning, where the model learns to make decisions based on rewards or punishments received from the environment.

Tips for preparing for Machine Learning Interview

• Brush up on your fundamentals of statistics, calculus, and linear algebra.
• Read research papers and stay up to date with the latest advancements in machine learning.
• Get hands-on experience by working on personal projects or contributing to open-source projects.
• Familiarize yourself with popular machine learning libraries and frameworks.
• Practice coding and debugging skills by participating in coding challenges and competitions.
• Develop strong problem-solving skills and the ability to explain your thought process.
• Be prepared to discuss your previous machine learning projects and your role in them.
• Research the company and the interviewer beforehand to better tailor your responses and questions.
• Prepare questions to ask the interviewer about the company’s machine learning projects and initiatives.
• Showcase your ability to communicate complex technical concepts in a clear and concise manner.
• Demonstrate your ability to work collaboratively by discussing your experience working in team environments.
• Keep a positive attitude and remain confident in your abilities.

 

Conclusion:

Machine learning is an exciting field with a lot of potential for growth and innovation. Whether you're just starting out or you're a pro with loads of experience, you can totally up your game and impress potential employers with some key preparation. With these interview question and tips you are able to verse yourself with the basics and extremens of machine learning. And of course, be prepared for those tricky Machine learning interview questions.

Moreover, don't let the interview process discourage you. The job outlook for machine learning professionals is looking good, with a projected job growth of 21% from 2018-2028 according to the Bureau of Labor Statistics. So if you're interested in pursuing a career in machine learning, go for it! You've got this.