Today's Interview QnAs
Company Name - Hotstar
Role - Data Analyst
---------------------------------------
Q. What is the statistical power of sensitivity?
A. The statistical power of an A/B test refers to the test's sensitivity to certain magnitudes of effect sizes. More precisely, it is the probability of observing a statistically significant result at level alpha (α) if a true effect of a certain magnitude (MEI) is in fact present.
Q. What is the difference between covariance and correlation?
A. “Covariance” indicates the direction of the linear relationship between variables. “Correlation” on the other hand measures both the strength and direction of the linear relationship between two variables.
Q. What is negative indexing? Why is it needed? Can you give an example for the same in python
A. This means that the index value of -1 gives the last element, and -2 gives the second last element of an array. The negative indexing starts from where the array ends.
example: for list L = [0,2,35,3]; L[-1] will print 3 in Python.
Q. What is the condition for using a t-test or a z-test?
A. z-test is used for it when sample size is large, generally n >30. Whereas t-test is used for hypothesis testing when sample size is small, usually n < 30 where n is used to quantify the sample size.
Q. What is the main difference between overfitting and underfitting?
A. Overfitting is a modeling error which occurs when a function is too closely fit to a limited set of data points. Underfitting refers to a model that can neither model the training data nor generalize to new data.
Q. What is the KNN imputation method?
A. The idea in kNN methods is to identify 'k' samples in the dataset that are similar or close in the space. Then we use these 'k' samples to estimate the value of the missing data points. Each sample's missing values are imputed using the mean value of the 'k'-neighbors found in the dataset.
-------------------------------------
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Company Name - Hotstar
Role - Data Analyst
---------------------------------------
Q. What is the statistical power of sensitivity?
A. The statistical power of an A/B test refers to the test's sensitivity to certain magnitudes of effect sizes. More precisely, it is the probability of observing a statistically significant result at level alpha (α) if a true effect of a certain magnitude (MEI) is in fact present.
Q. What is the difference between covariance and correlation?
A. “Covariance” indicates the direction of the linear relationship between variables. “Correlation” on the other hand measures both the strength and direction of the linear relationship between two variables.
Q. What is negative indexing? Why is it needed? Can you give an example for the same in python
A. This means that the index value of -1 gives the last element, and -2 gives the second last element of an array. The negative indexing starts from where the array ends.
example: for list L = [0,2,35,3]; L[-1] will print 3 in Python.
Q. What is the condition for using a t-test or a z-test?
A. z-test is used for it when sample size is large, generally n >30. Whereas t-test is used for hypothesis testing when sample size is small, usually n < 30 where n is used to quantify the sample size.
Q. What is the main difference between overfitting and underfitting?
A. Overfitting is a modeling error which occurs when a function is too closely fit to a limited set of data points. Underfitting refers to a model that can neither model the training data nor generalize to new data.
Q. What is the KNN imputation method?
A. The idea in kNN methods is to identify 'k' samples in the dataset that are similar or close in the space. Then we use these 'k' samples to estimate the value of the missing data points. Each sample's missing values are imputed using the mean value of the 'k'-neighbors found in the dataset.
-------------------------------------
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Anonymous Poll
63%
5 ⭐
25%
4 ⭐
9%
3 ⭐
2%
2 ⭐
2%
1 ⭐
Today's Interview QnAs
Company Name - Tiger Analytics
Role - Data Scientist
----------------------------------------
Q. Deal with unbalanced binary classification?
A. Techniques to Handle unbalanced Data:
1. Use the right evaluation metrics
2. Use K-fold Cross-Validation in the right way
3. Ensemble different resampled datasets
4. Resample with different ratios
5. Design your own models
Q. Activation function?
A. Activation functions are mathematical equations that determine the output of a neural network model. It is a non-linear transformation that we do over the input before sending it to the next layer of neurons or finalizing it as output.
Q. Dimension reduction?
A. Dimensionality Reduction is used to reduce the feature space with consideration by a set of principal features.
Q. Why is mean square error a bad measure of model performance?
A. Mean Squared Error (MSE) gives a relatively high weight to large errors — therefore, MSE tends to put too much emphasis on large deviations.
Q. Remove multicollinearity?
A. To remove multicollinearities, we can do two things.
1. We can create new features
2. remove them from our data.
Q. long-tailed distribution ?
A. A long tail distribution of numbers is a kind of distribution having many occurrences far from the "head" or central part of the distribution. Most of occurrences in this kind of distributions occurs at early frequencies/values of x-axis.
Q. Outlier? Deal with it?
A. An outlier is an object that deviates significantly from the rest of the objects. They can be caused by measurement or execution error.
Removing outliers is legitimate only for specific reasons. Outliers can be very informative about the subject-area and data collection process. If the outlier does not change the results but does affect assumptions, you may drop the outlier. Or just trim the data set, but replace outliers with the nearest “good” data, as opposed to truncating them completely.
Q. Example where the median is a better measure than the mean ?
A. If your data contains outliers, then you would typically rather use the median because otherwise the value of the mean would be dominated by the outliers rather than the typical values. In conclusion, if you are considering the mean, check your data for outliers, if any then better choose median.
-------------------------------------
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Company Name - Tiger Analytics
Role - Data Scientist
----------------------------------------
Q. Deal with unbalanced binary classification?
A. Techniques to Handle unbalanced Data:
1. Use the right evaluation metrics
2. Use K-fold Cross-Validation in the right way
3. Ensemble different resampled datasets
4. Resample with different ratios
5. Design your own models
Q. Activation function?
A. Activation functions are mathematical equations that determine the output of a neural network model. It is a non-linear transformation that we do over the input before sending it to the next layer of neurons or finalizing it as output.
Q. Dimension reduction?
A. Dimensionality Reduction is used to reduce the feature space with consideration by a set of principal features.
Q. Why is mean square error a bad measure of model performance?
A. Mean Squared Error (MSE) gives a relatively high weight to large errors — therefore, MSE tends to put too much emphasis on large deviations.
Q. Remove multicollinearity?
A. To remove multicollinearities, we can do two things.
1. We can create new features
2. remove them from our data.
Q. long-tailed distribution ?
A. A long tail distribution of numbers is a kind of distribution having many occurrences far from the "head" or central part of the distribution. Most of occurrences in this kind of distributions occurs at early frequencies/values of x-axis.
Q. Outlier? Deal with it?
A. An outlier is an object that deviates significantly from the rest of the objects. They can be caused by measurement or execution error.
Removing outliers is legitimate only for specific reasons. Outliers can be very informative about the subject-area and data collection process. If the outlier does not change the results but does affect assumptions, you may drop the outlier. Or just trim the data set, but replace outliers with the nearest “good” data, as opposed to truncating them completely.
Q. Example where the median is a better measure than the mean ?
A. If your data contains outliers, then you would typically rather use the median because otherwise the value of the mean would be dominated by the outliers rather than the typical values. In conclusion, if you are considering the mean, check your data for outliers, if any then better choose median.
-------------------------------------
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👍1
What is the output of the code?
Anonymous Quiz
57%
CloudyMLCloudyMLCloudyMLCloudyMLCloudyMLCloudyML
12%
CloudyMLCloudyMLCloudyMLCloudyMLCloudyML
6%
C l o u d y M L
26%
Error: invalid syntax
𝐓𝐨𝐝𝐚𝐲'𝐬 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 𝐐𝐮𝐞𝐬𝐭 𝐍 𝐀𝐧𝐬
𝐂𝐨𝐦𝐩𝐚𝐧𝐲 𝐍𝐚𝐦𝐞 - 𝐂𝐚𝐩𝐠𝐞𝐦𝐢𝐧𝐢
𝐑𝐨𝐥𝐞 - 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐭𝐢𝐬𝐭
--------------------------------------------
𝐐1. 𝐩-𝐯𝐚𝐥𝐮𝐞?
𝐀ns. p-value is a measure of the probability that an observed difference could have occurred just by random chance. The lower the p-value, the greater the statistical significance of the observed difference. P-value can be used as an alternative to or in addition to pre-selected confidence levels for hypothesis testing.
𝐐2. 𝐈𝐧𝐭𝐞𝐫𝐩𝐨𝐥𝐚𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐄𝐱𝐭𝐫𝐚𝐩𝐨𝐥𝐚𝐭𝐢𝐨𝐧?
𝐀ns. Interpolation is the process of calculating the unknown value from known given values whereas extrapolation is the process of calculating unknown values beyond the given data points.
𝐐3. 𝐔𝐧𝐢𝐟𝐨𝐫𝐦𝐞𝐝 𝐃𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐢𝐨𝐧 & 𝐧𝐨𝐫𝐦𝐚𝐥 𝐝𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐢𝐨𝐧?
𝐀ns. The normal distribution is bell-shaped, which means value near the center of the distribution are more likely to occur as opposed to values on the tails of the distribution. The uniform distribution is rectangular-shaped, which means every value in the distribution is equally likely to occur.
𝐐4. 𝐑𝐞𝐜𝐨𝐦𝐦𝐞𝐧𝐝𝐞𝐫 𝐒𝐲𝐬𝐭𝐞𝐦𝐬?
𝐀ns. The recommender system mainly deals with the likes and dislikes of the users. Its major objective is to recommend an item to a user which has a high chance of liking or is in need of a particular user based on his previous purchases. It is like having a personalized team who can understand our likes and dislikes and help us in making the decisions regarding a particular item without being biased by any means by making use of a large amount of data in the repositories which are generated day by day.
𝐐5. 𝐉𝐎𝐈𝐍 𝐟𝐮𝐧𝐜𝐭𝐢𝐨𝐧 𝐢𝐧 𝐒𝐐𝐋
𝐀ns. The SQL Joins clause is used to combine records from two or more tables in a database.
𝐐6. 𝐒𝐪𝐮𝐚𝐫𝐞𝐝 𝐞𝐫𝐫𝐨𝐫 𝐚𝐧𝐝 𝐚𝐛𝐬𝐨𝐥𝐮𝐭𝐞 𝐞𝐫𝐫𝐨𝐫?
𝐀ns. mean squared error (MSE), and mean absolute error (MAE) are used to evaluate the regression problem's accuracy. The squared error is everywhere differentiable, while the absolute error is not (its derivative is undefined at 0). This makes the squared error more amenable to the techniques of mathematical optimization.
-----------------------
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𝐂𝐨𝐦𝐩𝐚𝐧𝐲 𝐍𝐚𝐦𝐞 - 𝐂𝐚𝐩𝐠𝐞𝐦𝐢𝐧𝐢
𝐑𝐨𝐥𝐞 - 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐭𝐢𝐬𝐭
--------------------------------------------
𝐐1. 𝐩-𝐯𝐚𝐥𝐮𝐞?
𝐀ns. p-value is a measure of the probability that an observed difference could have occurred just by random chance. The lower the p-value, the greater the statistical significance of the observed difference. P-value can be used as an alternative to or in addition to pre-selected confidence levels for hypothesis testing.
𝐐2. 𝐈𝐧𝐭𝐞𝐫𝐩𝐨𝐥𝐚𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐄𝐱𝐭𝐫𝐚𝐩𝐨𝐥𝐚𝐭𝐢𝐨𝐧?
𝐀ns. Interpolation is the process of calculating the unknown value from known given values whereas extrapolation is the process of calculating unknown values beyond the given data points.
𝐐3. 𝐔𝐧𝐢𝐟𝐨𝐫𝐦𝐞𝐝 𝐃𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐢𝐨𝐧 & 𝐧𝐨𝐫𝐦𝐚𝐥 𝐝𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐢𝐨𝐧?
𝐀ns. The normal distribution is bell-shaped, which means value near the center of the distribution are more likely to occur as opposed to values on the tails of the distribution. The uniform distribution is rectangular-shaped, which means every value in the distribution is equally likely to occur.
𝐐4. 𝐑𝐞𝐜𝐨𝐦𝐦𝐞𝐧𝐝𝐞𝐫 𝐒𝐲𝐬𝐭𝐞𝐦𝐬?
𝐀ns. The recommender system mainly deals with the likes and dislikes of the users. Its major objective is to recommend an item to a user which has a high chance of liking or is in need of a particular user based on his previous purchases. It is like having a personalized team who can understand our likes and dislikes and help us in making the decisions regarding a particular item without being biased by any means by making use of a large amount of data in the repositories which are generated day by day.
𝐐5. 𝐉𝐎𝐈𝐍 𝐟𝐮𝐧𝐜𝐭𝐢𝐨𝐧 𝐢𝐧 𝐒𝐐𝐋
𝐀ns. The SQL Joins clause is used to combine records from two or more tables in a database.
𝐐6. 𝐒𝐪𝐮𝐚𝐫𝐞𝐝 𝐞𝐫𝐫𝐨𝐫 𝐚𝐧𝐝 𝐚𝐛𝐬𝐨𝐥𝐮𝐭𝐞 𝐞𝐫𝐫𝐨𝐫?
𝐀ns. mean squared error (MSE), and mean absolute error (MAE) are used to evaluate the regression problem's accuracy. The squared error is everywhere differentiable, while the absolute error is not (its derivative is undefined at 0). This makes the squared error more amenable to the techniques of mathematical optimization.
-----------------------
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Today's Interview QnAs
Company Name - Cognizant
Role - Data Scientist
-------------------------------------
Q. Describe how Gradient Boosting works.
A. Gradient boosting is a type of machine learning boosting. It relies on the intuition that the best possible next model, when combined with previous models, minimizes the overall prediction error. If a small change in the prediction for a case causes no change in error, then next target outcome of the case is zero. Gradient boosting produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees.
Q. Describe the decision tree model.
A. Decision Trees are a type of Supervised Machine Learning where the data is continuously split according to a certain parameter. The leaves are the decisions or the final outcomes. A decision tree is a machine learning algorithm that partitions the data into subsets.
Q. What is a neural network?
A. Neural networks are a set of algorithms, modeled loosely after the human brain, that are designed to recognize patterns. They interpret sensory data through a kind of machine perception, labeling or clustering raw input. They, also known as Artificial Neural Networks, are the subset of Deep Learning.
Q. Explain the Bias-Variance Tradeoff
A. The bias–variance tradeoff is the property of a model that the variance of the parameter estimated across samples can be reduced by increasing the bias in the estimated parameters.
Q. What’s the difference between L1 and L2 regularization?
A. The main intuitive difference between the L1 and L2 regularization is that L1 regularization tries to estimate the median of the data while the L2 regularization tries to estimate the mean of the data to avoid overfitting. That value will also be the median of the data distribution mathematically.
----------------------------------
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Company Name - Cognizant
Role - Data Scientist
-------------------------------------
Q. Describe how Gradient Boosting works.
A. Gradient boosting is a type of machine learning boosting. It relies on the intuition that the best possible next model, when combined with previous models, minimizes the overall prediction error. If a small change in the prediction for a case causes no change in error, then next target outcome of the case is zero. Gradient boosting produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees.
Q. Describe the decision tree model.
A. Decision Trees are a type of Supervised Machine Learning where the data is continuously split according to a certain parameter. The leaves are the decisions or the final outcomes. A decision tree is a machine learning algorithm that partitions the data into subsets.
Q. What is a neural network?
A. Neural networks are a set of algorithms, modeled loosely after the human brain, that are designed to recognize patterns. They interpret sensory data through a kind of machine perception, labeling or clustering raw input. They, also known as Artificial Neural Networks, are the subset of Deep Learning.
Q. Explain the Bias-Variance Tradeoff
A. The bias–variance tradeoff is the property of a model that the variance of the parameter estimated across samples can be reduced by increasing the bias in the estimated parameters.
Q. What’s the difference between L1 and L2 regularization?
A. The main intuitive difference between the L1 and L2 regularization is that L1 regularization tries to estimate the median of the data while the L2 regularization tries to estimate the mean of the data to avoid overfitting. That value will also be the median of the data distribution mathematically.
----------------------------------
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Today's Interview QnAs
Company Name - Genpact
Role - Data Scientist
----------------------------
Q. Inter quartile ranges?
A. The most effective way to find all of your outliers is by using the interquartile range (IQR). The IQR contains the middle bulk of your data, so outliers can be easily found once you know the IQR. Quartiles divide the entire set into four equal parts. So, there are three quartiles, first, second and third represented by Q 1 , Q 2 and Q 3 , respectively. Q 2 is nothing but the median.
Q. Imputation methods?
A. They are:
List wise or case deletion
Pairwise deletion
Mean substitution
Regression imputation
Maximum likelihood.
Q. Deal overfittting?
A. Techniques to reduce overfitting:
Increase training data.
Reduce model complexity.
Early stopping during the training phase.
Ridge Regularization and Lasso Regularization.
Q. Gridsearch vs Random search?
A. Random search differs from grid search in that we no longer provide an explicit set of possible values for each hyperparameter; rather, we provide a statistical distribution for each hyperparameter from which values are sampled. Essentially, we define a sampling distribution for each hyperparameter to carry out a randomized search.
Q. Hyperparameters in SVM?
A. kernel: It maps the observations into some feature space. Ideally the observations are more easily (linearly) separable after this transformation. There are multiple standard kernels for this transformations, e.g. the linear kernel, the polynomial kernel and the radial kernel.
C: It is a hypermeter in SVM to control error. The C parameter tells the SVM optimization how much you want to avoid misclassifying each training example. For large values of C, the optimization will choose a smaller-margin hyperplane if that hyperplane does a better job of getting all the training points classified correctly.
gamma: Gamma is used when we use the Gaussian RBF kernel. if you use linear or polynomial kernel then you do not need gamma only you need C hypermeter. Somewhere it is also used as sigma. Gamma decides that how much curvature we want in a decision boundary.
Q. Ridge vs lasso?
A. Ridge and Lasso regression are some of the simple techniques to reduce model complexity and prevent over-fitting which may result from simple linear regression . Ridge Regression : In ridge regression, the cost function is altered by adding a penalty equivalent to square of the magnitude of the coefficients. lasso (least absolute shrinkage and selection operator; also Lasso or LASSO) is a regression analysis method that performs both variable selection and regularization in order to enhance the prediction accuracy and interpretability of the resulting statistical model.
--------------------------
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Course link👉🏻https://bit.ly/Learn-Data-Science-with-CloudyML
Happy Learning With CloudyML 💙
Company Name - Genpact
Role - Data Scientist
----------------------------
Q. Inter quartile ranges?
A. The most effective way to find all of your outliers is by using the interquartile range (IQR). The IQR contains the middle bulk of your data, so outliers can be easily found once you know the IQR. Quartiles divide the entire set into four equal parts. So, there are three quartiles, first, second and third represented by Q 1 , Q 2 and Q 3 , respectively. Q 2 is nothing but the median.
Q. Imputation methods?
A. They are:
List wise or case deletion
Pairwise deletion
Mean substitution
Regression imputation
Maximum likelihood.
Q. Deal overfittting?
A. Techniques to reduce overfitting:
Increase training data.
Reduce model complexity.
Early stopping during the training phase.
Ridge Regularization and Lasso Regularization.
Q. Gridsearch vs Random search?
A. Random search differs from grid search in that we no longer provide an explicit set of possible values for each hyperparameter; rather, we provide a statistical distribution for each hyperparameter from which values are sampled. Essentially, we define a sampling distribution for each hyperparameter to carry out a randomized search.
Q. Hyperparameters in SVM?
A. kernel: It maps the observations into some feature space. Ideally the observations are more easily (linearly) separable after this transformation. There are multiple standard kernels for this transformations, e.g. the linear kernel, the polynomial kernel and the radial kernel.
C: It is a hypermeter in SVM to control error. The C parameter tells the SVM optimization how much you want to avoid misclassifying each training example. For large values of C, the optimization will choose a smaller-margin hyperplane if that hyperplane does a better job of getting all the training points classified correctly.
gamma: Gamma is used when we use the Gaussian RBF kernel. if you use linear or polynomial kernel then you do not need gamma only you need C hypermeter. Somewhere it is also used as sigma. Gamma decides that how much curvature we want in a decision boundary.
Q. Ridge vs lasso?
A. Ridge and Lasso regression are some of the simple techniques to reduce model complexity and prevent over-fitting which may result from simple linear regression . Ridge Regression : In ridge regression, the cost function is altered by adding a penalty equivalent to square of the magnitude of the coefficients. lasso (least absolute shrinkage and selection operator; also Lasso or LASSO) is a regression analysis method that performs both variable selection and regularization in order to enhance the prediction accuracy and interpretability of the resulting statistical model.
--------------------------
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✅Capstone End-to-end Projects
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It is a Self-Paced course with lifetime access.
Course link👉🏻https://bit.ly/Learn-Data-Science-with-CloudyML
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Today's Interview Quest
Company Name - Tiger Analytics
Role - Data Scientist
---------------------------------------
Q. Explain the difference between an array and a linked list.
A. An array is a collection of elements of a similar data type. A linked list is a collection of objects known as a node where node consists of two parts, i.e., data and address. Array elements store in a contiguous memory location. Linked list elements can be stored anywhere in the memory or randomly stored.
Q. How do you ensure you are not overfitting a model?
A. Keep your model simple. Use regularization technique. Use cross-validation.
Q. How do you fix high variance in a model?
A. You can reduce High variance, by reducing the number of features in the model. There are several methods available to check which features don't add much value to the model and which are of importance. Increasing the size of the training set can also help the model generalize.
Q. What are hyperparameters? How do they differ from model parameters?
A. Model Parameters: These are the parameters in the model that must be determined using the training data set. These are the fitted parameters. Hyperparameters: These are adjustable parameters that must be tuned in order to obtain a model with optimal performance.
Q. You are told that your regression model is suffering from multicollinearity. How do verify this is true and build a better model?
A. A simple method to detect multicollinearity in a model is by using something called the variance inflation factor or the VIF for each predicting variable. We can follow these steps in order to build a better model:
Remove some of the highly correlated independent variables.
Linearly combine the independent variables, such as adding them together.
Perform an analysis designed for highly correlated variables, such as principal components analysis or partial least squares regression.
Q. You build a random forest model with 10,000 trees. Training error as at 0.00, but validation error is 34.23. Explain what went wrong ?
A. It means that the model has mimicked the training pattern perfectly that it will cause overfitting problem in test samples. To avoid this overfitting, use techniques like less complex model or cross validation etc.
Q. What is the recall, specificity and precision of the confusion matrix?
A. The recall is the ratio of the relevant results returned by the search engine to the total number of the relevant results that could have been returned. Specificity (SP) is calculated as the number of correct negative predictions divided by the total number of negatives. The precision is the proportion of relevant results in the list of all returned search results.
------------------------------------
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Company Name - Tiger Analytics
Role - Data Scientist
---------------------------------------
Q. Explain the difference between an array and a linked list.
A. An array is a collection of elements of a similar data type. A linked list is a collection of objects known as a node where node consists of two parts, i.e., data and address. Array elements store in a contiguous memory location. Linked list elements can be stored anywhere in the memory or randomly stored.
Q. How do you ensure you are not overfitting a model?
A. Keep your model simple. Use regularization technique. Use cross-validation.
Q. How do you fix high variance in a model?
A. You can reduce High variance, by reducing the number of features in the model. There are several methods available to check which features don't add much value to the model and which are of importance. Increasing the size of the training set can also help the model generalize.
Q. What are hyperparameters? How do they differ from model parameters?
A. Model Parameters: These are the parameters in the model that must be determined using the training data set. These are the fitted parameters. Hyperparameters: These are adjustable parameters that must be tuned in order to obtain a model with optimal performance.
Q. You are told that your regression model is suffering from multicollinearity. How do verify this is true and build a better model?
A. A simple method to detect multicollinearity in a model is by using something called the variance inflation factor or the VIF for each predicting variable. We can follow these steps in order to build a better model:
Remove some of the highly correlated independent variables.
Linearly combine the independent variables, such as adding them together.
Perform an analysis designed for highly correlated variables, such as principal components analysis or partial least squares regression.
Q. You build a random forest model with 10,000 trees. Training error as at 0.00, but validation error is 34.23. Explain what went wrong ?
A. It means that the model has mimicked the training pattern perfectly that it will cause overfitting problem in test samples. To avoid this overfitting, use techniques like less complex model or cross validation etc.
Q. What is the recall, specificity and precision of the confusion matrix?
A. The recall is the ratio of the relevant results returned by the search engine to the total number of the relevant results that could have been returned. Specificity (SP) is calculated as the number of correct negative predictions divided by the total number of negatives. The precision is the proportion of relevant results in the list of all returned search results.
------------------------------------
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Today's Interview Quest
Company Name - Fractal
Role - Data Scientist
---------------------------------------
Q. Difference between array and list
A. The main difference between these two data types is the operation you can perform on them. Lists are containers for elements having differing data types but arrays are used as containers for elements of the same data type.
Q. Which is faster dictionary or list for look up
A. Dictionary is faster because you used a better algorithm. The reason is because a dictionary is a lookup, while a list is an iteration. Dictionary uses a hash lookup, while your list requires walking through the list until it finds the result from beginning to the result each time.
Q. How much time SVM takes to complete if 1 iteration takes 10sec for 1st class And there are 4 classes.
A. It would take 4*10 = 40 seconds to train one-vs-all method one to one.
Q. Kernals in svm, there difference
A. Kernel Function in SVM is a method used to take data as input and transform into the required form of processing data.
Gaussian Kernel Radial Basis Function (RBF) : It is used to perform transformation, when there is no prior knowledge about data and it uses radial basis method to improve the transformation.
Sigmoid Kernel: this function is equivalent to a two-layer, perceptron model of neural network, which is used as activation function for artificial neurons.
Polynomial Kernel: It represents the similarity of vectors in training set of data in a feature space over polynomials of the original variables used in kernel.
Linear Kernel: used when data is linearly separable.
------------------------------
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Company Name - Fractal
Role - Data Scientist
---------------------------------------
Q. Difference between array and list
A. The main difference between these two data types is the operation you can perform on them. Lists are containers for elements having differing data types but arrays are used as containers for elements of the same data type.
Q. Which is faster dictionary or list for look up
A. Dictionary is faster because you used a better algorithm. The reason is because a dictionary is a lookup, while a list is an iteration. Dictionary uses a hash lookup, while your list requires walking through the list until it finds the result from beginning to the result each time.
Q. How much time SVM takes to complete if 1 iteration takes 10sec for 1st class And there are 4 classes.
A. It would take 4*10 = 40 seconds to train one-vs-all method one to one.
Q. Kernals in svm, there difference
A. Kernel Function in SVM is a method used to take data as input and transform into the required form of processing data.
Gaussian Kernel Radial Basis Function (RBF) : It is used to perform transformation, when there is no prior knowledge about data and it uses radial basis method to improve the transformation.
Sigmoid Kernel: this function is equivalent to a two-layer, perceptron model of neural network, which is used as activation function for artificial neurons.
Polynomial Kernel: It represents the similarity of vectors in training set of data in a feature space over polynomials of the original variables used in kernel.
Linear Kernel: used when data is linearly separable.
------------------------------
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Anonymous Quiz
69%
pop()
8%
append()
2%
sort()
21%
remove()
Today's Interview QnAs
Company Name - Larsen and Toubro
Role - Data Scientist
----------------------------------------
Q. Ways to avoid overfitting
A. Some steps that we can take to avoid it:
1. Data augmentation
2. L1/L2 Regularization
3. Remove layers / number of units per layer
4. Cross-validation
Q. Image classification algorithms
A. Image Classification algorithms are the algorithms which are used to classify labels for images using their characteristics. Example: Convolutional Neural Networks.
Q. args will return?
A. The special syntax *args in function is used to pass a variable number of arguments to a function. It is used to pass a non-key worded, variable-length argument list. The syntax is to use the symbol * to take in a variable number of arguments; by convention, it is often used with the word args.
Q. Difference between having and where clause in SQL.
A. WHERE Clause is used to filter the records from the table based on the specified condition. HAVING Clause is used to filter record from the groups based on the specified condition.
Q. How do you handle categorical data?
A. One-Hot Encoding is the most common, correct way to deal with non-ordinal categorical data. It consists of creating an additional feature for each group of the categorical feature and mark each observation belonging (Value=1) or not (Value=0) to that group.
Q. What Is Interpolation And Extrapolation?
A. Interpolation is the process of calculating the unknown value from known given values whereas extrapolation is the process of calculating unknown values beyond the given data points.
Q. SQL joins and Groups
A. The SQL Joins clause is used to combine records from two or more tables in a database. The GROUP BY statement groups rows that have the same values into summary rows, like "find the number of customers in each country".
Q. How do you handle null values and which Imputation method is more favorable?
A. Ways to handle missing values in the dataset:
Deleting Rows with missing values.
Impute missing values for continuous variable.
Impute missing values for categorical variable.
Other Imputation Methods.
Using Algorithms that support missing values.
Prediction of missing values.
Multiple imputation is more advantageous than the single imputation because it uses several complete data sets and provides both the within-imputation and between-imputation variability.
---------------------------------------
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It is a Self-Paced course with lifetime access.
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⛄Course on Christmas Sale⛄
Happy Learning With CloudyML 💙
Company Name - Larsen and Toubro
Role - Data Scientist
----------------------------------------
Q. Ways to avoid overfitting
A. Some steps that we can take to avoid it:
1. Data augmentation
2. L1/L2 Regularization
3. Remove layers / number of units per layer
4. Cross-validation
Q. Image classification algorithms
A. Image Classification algorithms are the algorithms which are used to classify labels for images using their characteristics. Example: Convolutional Neural Networks.
Q. args will return?
A. The special syntax *args in function is used to pass a variable number of arguments to a function. It is used to pass a non-key worded, variable-length argument list. The syntax is to use the symbol * to take in a variable number of arguments; by convention, it is often used with the word args.
Q. Difference between having and where clause in SQL.
A. WHERE Clause is used to filter the records from the table based on the specified condition. HAVING Clause is used to filter record from the groups based on the specified condition.
Q. How do you handle categorical data?
A. One-Hot Encoding is the most common, correct way to deal with non-ordinal categorical data. It consists of creating an additional feature for each group of the categorical feature and mark each observation belonging (Value=1) or not (Value=0) to that group.
Q. What Is Interpolation And Extrapolation?
A. Interpolation is the process of calculating the unknown value from known given values whereas extrapolation is the process of calculating unknown values beyond the given data points.
Q. SQL joins and Groups
A. The SQL Joins clause is used to combine records from two or more tables in a database. The GROUP BY statement groups rows that have the same values into summary rows, like "find the number of customers in each country".
Q. How do you handle null values and which Imputation method is more favorable?
A. Ways to handle missing values in the dataset:
Deleting Rows with missing values.
Impute missing values for continuous variable.
Impute missing values for categorical variable.
Other Imputation Methods.
Using Algorithms that support missing values.
Prediction of missing values.
Multiple imputation is more advantageous than the single imputation because it uses several complete data sets and provides both the within-imputation and between-imputation variability.
---------------------------------------
P.S.: If you wanna crack interviews of Data Scientist, Data Analyst, ML Engineer, Business Analyst and Research analyst, join our Assignments based "AI For ALL" Course.
✅Hands on Guided assignments
✅Capstone End-to-end Projects
✅1-1 Skype Live Doubt Clearance
✅Frequently Asked Interview QnAs PDF collection
✅Course completion Certificate
It is a Self-Paced course with lifetime access.
👉🏻Check the Course link in our Telegram Description
⛄Course on Christmas Sale⛄
Happy Learning With CloudyML 💙
Today's Interview QnAs
Company Name - Tiger Analytics
Role - Data Scientist
--------------------------------------
Q.Autoencoder methods
A. Autoencoder is a type of neural network where the output layer has the same dimensionality as the input layer. In simpler words, the number of output units in the output layer is equal to the number of input units in the input layer. Various techniques exist to prevent autoencoders from learning the identity function and to improve their ability to capture important ' information and learn richer representations. 1.Sparse autoencoder (SAE) 2. Denoising autoencoder (DAE) 3. Contractive autoencoder (CAE) 4. Principal component analysis.
Q. L1 and L2 regularization?
A. L1 regularization gives output in binary weights from 0 to 1 for the model's features and is adopted for decreasing the number of features in a huge dimensional dataset. L2 regularization disperse the error terms in all the weights that leads to more accurate customized final models.
Q. How to measure the Euclidean distance betweeen the two arrays in numpy?
A. Euclidean distance is defined in mathematics as the magnitude or length of the line segment between two points. There are multiple methods for measuring the euclidean methods.
Method 1. In this method, we first initialize two numpy arrays. Then, we use linalg.norm() of numpy basically to compute the euclidean distance directly.
Method 2. In this method, we first initialize two numpy arrays. Then, we take the difference of the two arrays, compute the dot product of the result, and transpose of the result. Then we take the square root of the answer. This is another way to implement Euclidean distance.
Method 3. In this method, we first initialize two numpy arrays. Then, we compute the difference of these arrays and take their square. We take the sum of the squared elements, and after that, we take the square root in the end. This is another way to implement Euclidean distance.
Q.What are the support vectors in SVM?
A. Support vectors are data points that are closer to the hyperplane and influence the position and orientation of the hyperplane. Using these support vectors, we maximize the margin of the classifier. Deleting the support vectors will change the position of the hyperplane. These are the points that help us build our SVM.
Q. How do you handle categorical data?
A. One-Hot Encoding is the most common, correct way to deal with non-ordinal categorical data. It consists of creating an additional feature for each group of the categorical feature and mark each observation belonging (Value=1) or not (Value=0) to that group.
Q. What is coerrelation?
A.Correlation is a statistical measure that expresses the extent to which two variables are linearly related (meaning they change together at a constant rate). It's a common tool for describing simple relationships without making a statement about cause and effects
Q. What is covariance?
A. Covariance is nothing but a measure of correlation. Covariance is a measure of how much two random variables vary together. It’s similar to variance, but where variance tells you how a single variable varies, co variance tells you how two variables vary together
------------------
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Company Name - Tiger Analytics
Role - Data Scientist
--------------------------------------
Q.Autoencoder methods
A. Autoencoder is a type of neural network where the output layer has the same dimensionality as the input layer. In simpler words, the number of output units in the output layer is equal to the number of input units in the input layer. Various techniques exist to prevent autoencoders from learning the identity function and to improve their ability to capture important ' information and learn richer representations. 1.Sparse autoencoder (SAE) 2. Denoising autoencoder (DAE) 3. Contractive autoencoder (CAE) 4. Principal component analysis.
Q. L1 and L2 regularization?
A. L1 regularization gives output in binary weights from 0 to 1 for the model's features and is adopted for decreasing the number of features in a huge dimensional dataset. L2 regularization disperse the error terms in all the weights that leads to more accurate customized final models.
Q. How to measure the Euclidean distance betweeen the two arrays in numpy?
A. Euclidean distance is defined in mathematics as the magnitude or length of the line segment between two points. There are multiple methods for measuring the euclidean methods.
Method 1. In this method, we first initialize two numpy arrays. Then, we use linalg.norm() of numpy basically to compute the euclidean distance directly.
Method 2. In this method, we first initialize two numpy arrays. Then, we take the difference of the two arrays, compute the dot product of the result, and transpose of the result. Then we take the square root of the answer. This is another way to implement Euclidean distance.
Method 3. In this method, we first initialize two numpy arrays. Then, we compute the difference of these arrays and take their square. We take the sum of the squared elements, and after that, we take the square root in the end. This is another way to implement Euclidean distance.
Q.What are the support vectors in SVM?
A. Support vectors are data points that are closer to the hyperplane and influence the position and orientation of the hyperplane. Using these support vectors, we maximize the margin of the classifier. Deleting the support vectors will change the position of the hyperplane. These are the points that help us build our SVM.
Q. How do you handle categorical data?
A. One-Hot Encoding is the most common, correct way to deal with non-ordinal categorical data. It consists of creating an additional feature for each group of the categorical feature and mark each observation belonging (Value=1) or not (Value=0) to that group.
Q. What is coerrelation?
A.Correlation is a statistical measure that expresses the extent to which two variables are linearly related (meaning they change together at a constant rate). It's a common tool for describing simple relationships without making a statement about cause and effects
Q. What is covariance?
A. Covariance is nothing but a measure of correlation. Covariance is a measure of how much two random variables vary together. It’s similar to variance, but where variance tells you how a single variable varies, co variance tells you how two variables vary together
------------------
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