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Today's Interview QnA
Company: Blinkit
Role: Data Scientist
Q1. You are given a train data set having 2000 columns and 2 million rows. The data set is based on a classification problem. Your manager has asked you to reduce the dimension of this data so that model computation time can be reduced. Your machine has memory constraints? How you will deal with this?
Ans:
1.Problem is because of Lower RAM - I will close all not required applications running on my machine using task manager/activity tracker - aiming to use most of memory.
2. I will try random sampling on data set. By this I mean, we can create a smaller data set, let’s say having 2000 variables and 300000 rows and do computations.
3. I will try to reduce dimensionality, I will separate numerical and categorical variables and remove correlated variables.
4. For numerical variables, I will use correlation. For categorical variables, I will use chi-square test.
5. I will also try to use PCA and pick components which can explain maximum variance in given data set
6. I guess building a Linear Model using Stochastic Gradient Descent will be helpful
7. If possible I will try to apply business understanding to estimate which all predictors can impact response variable (domain knowledge will be required). Cons can be - as this is an intuitive approach failing to identify useful predictors might result in significant loss of information
Q2. Why are Validation and Test Datasets Needed?
Ans: Data is split into three different categories while creating a model:
• Training dataset: Training dataset is used for building a model and adjusting its variables. The correctness of the model built on the training dataset cannot be relied on as the model might give incorrect outputs after being fed new inputs.
• Validation dataset: Validation dataset is used to look into a model’s response. After this, the hyperparameters on the basis of the response of the model(on the validation dataset data) are tuned.When a model’s response is evaluated by using the validation dataset, the model is indirectly trained with the validation set. This may lead to the overfitting of the model to specific data. So, this model will not be strong enough to give the desired response to real-world data.
• Test dataset: Test dataset is the subset of the actual dataset, which is not yet used to train the model. The model is unaware of this dataset. So, by using the test dataset, the response of the created model can be computed on hidden data. The model’s performance is tested on the basis of the test dataset.Note: The model is always exposed to the test dataset after tuning the hyperparameters on top of the validation dataset.
As we know, the evaluation of the model on the basis of the validation dataset would not be enough. Thus, the test dataset is used for computing the efficiency of the model.
Q3. What are Gaussian Mixture Model? Where they are used?
Ans: Probabilistic model for representing normally distributed subpopulations within an overall population. Mixture models in general don't require knowing which subpopulation a data point belongs to, allowing the model to learn the subpopulations automatically. Since subpopulation assignment is not known, this constitutes a form of unsupervised learning. These models have been used for feature extraction from speech data, and have also been used extensively in object tracking of multiple objects, where number of mixture components and their means predict object locations at each frame in a video sequence. change inplace of 1st.
Company: Blinkit
Role: Data Scientist
Q1. You are given a train data set having 2000 columns and 2 million rows. The data set is based on a classification problem. Your manager has asked you to reduce the dimension of this data so that model computation time can be reduced. Your machine has memory constraints? How you will deal with this?
Ans:
1.Problem is because of Lower RAM - I will close all not required applications running on my machine using task manager/activity tracker - aiming to use most of memory.
2. I will try random sampling on data set. By this I mean, we can create a smaller data set, let’s say having 2000 variables and 300000 rows and do computations.
3. I will try to reduce dimensionality, I will separate numerical and categorical variables and remove correlated variables.
4. For numerical variables, I will use correlation. For categorical variables, I will use chi-square test.
5. I will also try to use PCA and pick components which can explain maximum variance in given data set
6. I guess building a Linear Model using Stochastic Gradient Descent will be helpful
7. If possible I will try to apply business understanding to estimate which all predictors can impact response variable (domain knowledge will be required). Cons can be - as this is an intuitive approach failing to identify useful predictors might result in significant loss of information
Q2. Why are Validation and Test Datasets Needed?
Ans: Data is split into three different categories while creating a model:
• Training dataset: Training dataset is used for building a model and adjusting its variables. The correctness of the model built on the training dataset cannot be relied on as the model might give incorrect outputs after being fed new inputs.
• Validation dataset: Validation dataset is used to look into a model’s response. After this, the hyperparameters on the basis of the response of the model(on the validation dataset data) are tuned.When a model’s response is evaluated by using the validation dataset, the model is indirectly trained with the validation set. This may lead to the overfitting of the model to specific data. So, this model will not be strong enough to give the desired response to real-world data.
• Test dataset: Test dataset is the subset of the actual dataset, which is not yet used to train the model. The model is unaware of this dataset. So, by using the test dataset, the response of the created model can be computed on hidden data. The model’s performance is tested on the basis of the test dataset.Note: The model is always exposed to the test dataset after tuning the hyperparameters on top of the validation dataset.
As we know, the evaluation of the model on the basis of the validation dataset would not be enough. Thus, the test dataset is used for computing the efficiency of the model.
Q3. What are Gaussian Mixture Model? Where they are used?
Ans: Probabilistic model for representing normally distributed subpopulations within an overall population. Mixture models in general don't require knowing which subpopulation a data point belongs to, allowing the model to learn the subpopulations automatically. Since subpopulation assignment is not known, this constitutes a form of unsupervised learning. These models have been used for feature extraction from speech data, and have also been used extensively in object tracking of multiple objects, where number of mixture components and their means predict object locations at each frame in a video sequence. change inplace of 1st.
Q4. Say your model is suffering from lowbias and high variance !! Which algorithm you think will help you to tackle it? Why?
Ans: Low bias occurs when the model’s predicted values are near to actual values. In other words, model becomes flexible enough to mimic the training data distribution. Here not to forget a flexible model has no generalization capabilities. It means, when this model is tested on an unseen data, it gives disappointing results.
In such situations, we can use Bagging Algorithm (like random forest) to tackle high variance problem. Bagging algorithms divides a data set into subsets made with repeated randomized sampling. Then these samples are used to generate a set of models using a single learning algorithm. Later model predictions are combined using voting (classification) or averaging (regression).
Also to overcome high variance one can:
1. Use Regularization technique, where higher model coefficients get penalized, hence lowering model complexity.
2. Use top n features from variable importance chart. Maybe, with all the variable in the data set, algorithm is having difficulty in finding meaningful signal.
Ans: Low bias occurs when the model’s predicted values are near to actual values. In other words, model becomes flexible enough to mimic the training data distribution. Here not to forget a flexible model has no generalization capabilities. It means, when this model is tested on an unseen data, it gives disappointing results.
In such situations, we can use Bagging Algorithm (like random forest) to tackle high variance problem. Bagging algorithms divides a data set into subsets made with repeated randomized sampling. Then these samples are used to generate a set of models using a single learning algorithm. Later model predictions are combined using voting (classification) or averaging (regression).
Also to overcome high variance one can:
1. Use Regularization technique, where higher model coefficients get penalized, hence lowering model complexity.
2. Use top n features from variable importance chart. Maybe, with all the variable in the data set, algorithm is having difficulty in finding meaningful signal.
Machine learning interview question asked in Arraykart company. Explained solution video.
Link👉🏻 https://youtu.be/bll2mivBSqQ
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Today's Interview QnA
Company: Splunk
Role: Data Scientist
1. How Are Weights Initialized in a Neural network?
Ans: There are two methods here: we can either initialize the weights to zero or assign them randomly.
Initializing all weights to 0: This makes your model similar to a linear model. All the neurons and every layer perform the same operation, giving the same output and making the deep net useless.
Initializing all weights randomly: Here, the weights are assigned randomly by initializing them very close to 0. It gives better accuracy to the model since every neuron performs different computations. This is the most commonly used method.
2. What are the variants of Gradient descent?
Ans: Stochastic Gradient Descent: We use only a single training example for calculation of gradient and
update parameters.
Batch Gradient Descent: We calculate the gradient for the whole dataset and perform the update at
each iteration.
Mini-batch Gradient Descent: It’s one of the most popular optimization algorithms. It’s a variant of
Stochastic Gradient Descent and here instead of single training example, mini-batch of samples is
used.
3. What are the feature selection methods used to select the right variables?
Ans: There are two main methods for feature selection:
Filter Methods
This involves:
• Linear discrimination analysis
• ANOVA
• Chi-Square
The best analogy for selecting features is "bad data in, bad answer out." When we're limiting or selecting the features, it's all about selecting the useful feature.
Wrapper Methods
This involves:
• Forward Selection: We test one feature at a time and keep adding them until we get a good fit
• Backward Selection: We test all the features and start removing them to see what works better
• Recursive Feature Elimination: Recursively looks through all the different features and how they pair together. Wrapper methods are very labor-intensive, and high-end computers are needed if a lot of data analysis is performed with the wrapper method.
4. What is joint sampling and separate sampling?
Ans:
· Joint sampling is done when there are equal number of events and non-events. Not appropriate for imbalanced data
· Separate sampling is done for imbalanced data. For rare event, all observations are kept when target = 1 and only few observations are kept when target = 0.
.................
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Company: Splunk
Role: Data Scientist
1. How Are Weights Initialized in a Neural network?
Ans: There are two methods here: we can either initialize the weights to zero or assign them randomly.
Initializing all weights to 0: This makes your model similar to a linear model. All the neurons and every layer perform the same operation, giving the same output and making the deep net useless.
Initializing all weights randomly: Here, the weights are assigned randomly by initializing them very close to 0. It gives better accuracy to the model since every neuron performs different computations. This is the most commonly used method.
2. What are the variants of Gradient descent?
Ans: Stochastic Gradient Descent: We use only a single training example for calculation of gradient and
update parameters.
Batch Gradient Descent: We calculate the gradient for the whole dataset and perform the update at
each iteration.
Mini-batch Gradient Descent: It’s one of the most popular optimization algorithms. It’s a variant of
Stochastic Gradient Descent and here instead of single training example, mini-batch of samples is
used.
3. What are the feature selection methods used to select the right variables?
Ans: There are two main methods for feature selection:
Filter Methods
This involves:
• Linear discrimination analysis
• ANOVA
• Chi-Square
The best analogy for selecting features is "bad data in, bad answer out." When we're limiting or selecting the features, it's all about selecting the useful feature.
Wrapper Methods
This involves:
• Forward Selection: We test one feature at a time and keep adding them until we get a good fit
• Backward Selection: We test all the features and start removing them to see what works better
• Recursive Feature Elimination: Recursively looks through all the different features and how they pair together. Wrapper methods are very labor-intensive, and high-end computers are needed if a lot of data analysis is performed with the wrapper method.
4. What is joint sampling and separate sampling?
Ans:
· Joint sampling is done when there are equal number of events and non-events. Not appropriate for imbalanced data
· Separate sampling is done for imbalanced data. For rare event, all observations are kept when target = 1 and only few observations are kept when target = 0.
.................
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Today's Interview QnAs
Company : KPMG
Role: Data Scientist
Q1. You are given a data set. The data set has missing values that spread along 1 standard deviation from the median. What percentage of data would remain unaffected? Why?
This question has enough hints for you to start thinking! Since the data is spread across the median, let’s assume it’s a normal distribution. We know, in a normal distribution, ~68% of the data lies in 1 standard deviation from mean (or mode, median), which leaves ~32% of the data unaffected. Therefore, ~32% of the data would remain unaffected by missing values.
Q2. What do you mean by convex hull?
Convex hull is represents to outer boundaries of two-level group of data point. Once is convex hull has to been created data-set value, we get maximum data-set value level of margin hyperplane (MMH), which attempts to create data set value greatest departure between two groups data set value, as a vertical bisector between two convex hulls data set value.
A convex hull of a set S is the intersection of all convex set of which S is a subset. We denote it by [S] the convex hull of S.
Example:
S= {x: |x|=1} implies [S]= {x: |x|< or =1}
S= {x: |x| > or= 1} implies [S]=En
Q3. You’ve built a random forest model with 10000 trees. You got delighted after getting training error as 0.00. But, the validation error is 34.23. What is going on? Haven’t you trained your model perfectly?
Ans: The model has overfitted. Training error 0.00 means the classifier has mimicked the training data patterns to an extent, that they are not available in the unseen data. Hence, when this classifier was run on an unseen sample, it couldn’t find those patterns and returned predictions with higher error. In a random forest, it happens when we use a larger number of trees than necessary. Hence, to avoid this situation, we should tune the number of trees using cross-validation.
Q4. State one real life applications of convex hulls?
Ans: One applications convex hulls is to computation/construction of convex relaxations. Can say this is a way to find 'closest' convex problem to a non-convex problem one is attempting to solve.
.................
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Company : KPMG
Role: Data Scientist
Q1. You are given a data set. The data set has missing values that spread along 1 standard deviation from the median. What percentage of data would remain unaffected? Why?
This question has enough hints for you to start thinking! Since the data is spread across the median, let’s assume it’s a normal distribution. We know, in a normal distribution, ~68% of the data lies in 1 standard deviation from mean (or mode, median), which leaves ~32% of the data unaffected. Therefore, ~32% of the data would remain unaffected by missing values.
Q2. What do you mean by convex hull?
Convex hull is represents to outer boundaries of two-level group of data point. Once is convex hull has to been created data-set value, we get maximum data-set value level of margin hyperplane (MMH), which attempts to create data set value greatest departure between two groups data set value, as a vertical bisector between two convex hulls data set value.
A convex hull of a set S is the intersection of all convex set of which S is a subset. We denote it by [S] the convex hull of S.
Example:
S= {x: |x|=1} implies [S]= {x: |x|< or =1}
S= {x: |x| > or= 1} implies [S]=En
Q3. You’ve built a random forest model with 10000 trees. You got delighted after getting training error as 0.00. But, the validation error is 34.23. What is going on? Haven’t you trained your model perfectly?
Ans: The model has overfitted. Training error 0.00 means the classifier has mimicked the training data patterns to an extent, that they are not available in the unseen data. Hence, when this classifier was run on an unseen sample, it couldn’t find those patterns and returned predictions with higher error. In a random forest, it happens when we use a larger number of trees than necessary. Hence, to avoid this situation, we should tune the number of trees using cross-validation.
Q4. State one real life applications of convex hulls?
Ans: One applications convex hulls is to computation/construction of convex relaxations. Can say this is a way to find 'closest' convex problem to a non-convex problem one is attempting to solve.
.................
🔴If you want to learn Data Science from scratch and crack interviews, join our Assignment based Beginner to Advanced Data Science Combo course, trusted by 5000+ Learners.
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🔵Interview QnA
Python Interview Qestion asked in Royal bank of Canada
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Python Interview Qestion asked in Royal bank of Canada
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SQL interview question asked by IBM Company.
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SQL interview question asked by IBM Company.
Check explained solution on our YouTube channel, link below.
👉🏻https://youtu.be/y259wQyFBbg
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🔵 Interview QnA Explained Video
Python interview question asked in Meta Company, by Instagram team.
Check explained solution on our YouTube channel, link below.
👉🏻https://youtu.be/jdQEjf3cdbQ
Python interview question asked in Meta Company, by Instagram team.
Check explained solution on our YouTube channel, link below.
👉🏻https://youtu.be/jdQEjf3cdbQ
Today's Interview QnAs
Company- Wipro
Role: Data Scientist
1. List the most popular distribution curves along with scenarios where you will use them in an algorithm.
Ans: The most popular distribution curves are as follows- Bernoulli Distribution, Uniform Distribution, Binomial Distribution, Normal Distribution, Poisson Distribution, and Exponential Distribution.
Each of these distribution curves is used in various scenarios.
Bernoulli Distribution can be used to check if a team will win a championship or not, a newborn child is either male or female, you either pass an exam or not, etc.
Uniform distribution is a probability distribution that has a constant probability. Rolling a single dice is one example because it has a fixed number of outcomes.
Binomial distribution is a probability with only two possible outcomes, the prefix ‘bi’ means two or twice. An example of this would be a coin toss. The outcome will either be heads or tails.
Normal distribution describes how the values of a variable are distributed. It is typically a symmetric distribution where most of the observations cluster around the central peak. The values further away from the mean taper off equally in both directions. An example would be the height of students in a classroom.
Poisson distribution helps predict the probability of certain events happening when you know how often that event has occurred. It can be used by businessmen to make forecasts about the number of customers on certain days and allows them to adjust supply according to the demand.
Exponential distribution is concerned with the amount of time until a specific event occurs. For example, how long a car battery would last, in months.
2.Why is logistic regression a type of classification technique and not a regression? Name the function it is derived from?
Ans: Since the target column is categorical, it uses linear regression to create an odd function that is wrapped with a log function to use regression as a classifier. Hence, it is a type of classification technique and not a regression. It is derived from cost function.
3. What is OOB error and how does it occur?
Ans: For each bootstrap sample, there is one-third of data that was not used in the creation of the tree, i.e., it was out of the sample. This data is referred to as out of bag data. In order to get an unbiased measure of the accuracy of the model over test data, out of bag error is used. The out of bag data is passed for each tree is passed through that tree and the outputs are aggregated to give out of bag error. This percentage error is quite effective in estimating the error in the testing set and does not require further cross-validation.
4. State the limitations of Fixed Basis Function.
Ans: Linear separability in feature space doesn’t imply linear separability in input space. So, Inputs are non-linearly transformed using vectors of basic functions with increased dimensionality. Limitations of Fixed basis functions are:
Non-Linear transformations cannot remove overlap between two classes but they can increase overlap.
Often it is not clear which basis functions are the best fit for a given task. So, learning the basic functions can be useful over using fixed basis functions.
If we want to use only fixed ones, we can use a lot of them and let the model figure out the best fit but that would lead to overfitting the model thereby making it unstable.
Company- Wipro
Role: Data Scientist
1. List the most popular distribution curves along with scenarios where you will use them in an algorithm.
Ans: The most popular distribution curves are as follows- Bernoulli Distribution, Uniform Distribution, Binomial Distribution, Normal Distribution, Poisson Distribution, and Exponential Distribution.
Each of these distribution curves is used in various scenarios.
Bernoulli Distribution can be used to check if a team will win a championship or not, a newborn child is either male or female, you either pass an exam or not, etc.
Uniform distribution is a probability distribution that has a constant probability. Rolling a single dice is one example because it has a fixed number of outcomes.
Binomial distribution is a probability with only two possible outcomes, the prefix ‘bi’ means two or twice. An example of this would be a coin toss. The outcome will either be heads or tails.
Normal distribution describes how the values of a variable are distributed. It is typically a symmetric distribution where most of the observations cluster around the central peak. The values further away from the mean taper off equally in both directions. An example would be the height of students in a classroom.
Poisson distribution helps predict the probability of certain events happening when you know how often that event has occurred. It can be used by businessmen to make forecasts about the number of customers on certain days and allows them to adjust supply according to the demand.
Exponential distribution is concerned with the amount of time until a specific event occurs. For example, how long a car battery would last, in months.
2.Why is logistic regression a type of classification technique and not a regression? Name the function it is derived from?
Ans: Since the target column is categorical, it uses linear regression to create an odd function that is wrapped with a log function to use regression as a classifier. Hence, it is a type of classification technique and not a regression. It is derived from cost function.
3. What is OOB error and how does it occur?
Ans: For each bootstrap sample, there is one-third of data that was not used in the creation of the tree, i.e., it was out of the sample. This data is referred to as out of bag data. In order to get an unbiased measure of the accuracy of the model over test data, out of bag error is used. The out of bag data is passed for each tree is passed through that tree and the outputs are aggregated to give out of bag error. This percentage error is quite effective in estimating the error in the testing set and does not require further cross-validation.
4. State the limitations of Fixed Basis Function.
Ans: Linear separability in feature space doesn’t imply linear separability in input space. So, Inputs are non-linearly transformed using vectors of basic functions with increased dimensionality. Limitations of Fixed basis functions are:
Non-Linear transformations cannot remove overlap between two classes but they can increase overlap.
Often it is not clear which basis functions are the best fit for a given task. So, learning the basic functions can be useful over using fixed basis functions.
If we want to use only fixed ones, we can use a lot of them and let the model figure out the best fit but that would lead to overfitting the model thereby making it unstable.
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With all that, you are getting 1-1 Live Doubt Clearance support, 100% Internship Opportunity, Job Referral Mails, Sample resumes, Resume Review, Job Opening Mails etc.
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🔵 Interview QnA Explained Video
Python interview question asked in GOOGLE, AMAZON, FACEBOOK
Check explained solution on our YouTube channel, link below.
👉🏻 https://youtu.be/1TO7MdxC4oM
Python interview question asked in GOOGLE, AMAZON, FACEBOOK
Check explained solution on our YouTube channel, link below.
👉🏻 https://youtu.be/1TO7MdxC4oM
🔴JOB OPENING ALERT🔴
Role: Data Scientist
Company: Talentify
Exp: 0 - 5 years
Link: https://ai.cloudyml.com/3PaBREH
Role: Machine Learning Engineer
Company: 2Coms
Exp: 0 - 5 years
Link: https://ai.cloudyml.com/3srz40i
Role: Data Analyst
Company: Lemniscate Technologies
Exp: 0 - 1 years
Link: https://ai.cloudyml.com/399KC16
Role: Data Analyst
Company: A & M Supplies India Private Limited
Exp: 0 - 2 years
Link: https://ai.cloudyml.com/396RYm2
Role: Data Scientist
Company: Talentify
Exp: 0 - 5 years
Link: https://ai.cloudyml.com/3PaBREH
Role: Machine Learning Engineer
Company: 2Coms
Exp: 0 - 5 years
Link: https://ai.cloudyml.com/3srz40i
Role: Data Analyst
Company: Lemniscate Technologies
Exp: 0 - 1 years
Link: https://ai.cloudyml.com/399KC16
Role: Data Analyst
Company: A & M Supplies India Private Limited
Exp: 0 - 2 years
Link: https://ai.cloudyml.com/396RYm2