Today's Interview QnAs
Company Name - Google
Role: Data Scientist
Q. Why do you use feature selection?
A. Feature selection offers a simple yet effective way to overcome the challenge of high dimensional data analysis by eliminating redundant and irrelevant data. Removing the irrelevant data improves learning accuracy, reduces the computation time, and facilitates an enhanced understanding for the learning model or data. .
Q. Whatโs the difference between Gaussian Mixture Model and K-Means?
A. The first visible difference between K-Means and Gaussian Mixtures is the shape the decision boundaries. GMs are somewhat more flexible and with a covariance matrix โ we can make the boundaries elliptical, as opposed to circular boundaries with K-means.
Another thing is that GMs is a probabilistic algorithm. If we compare both algorithms, the Gaussian mixtures seem to be more robust. However, GMs usually tend to be slower than K-Means because it takes more iterations of the EM algorithm to reach the convergence.
Q. How do you pick k for K-Means?
A. Calculate the Within-Cluster-Sum of Squared Errors (WSS) for different values of k, and choose the k for which WSS becomes first starts to diminish. In the plot of WSS-versus-k, this is visible as an elbow. Also, the silhouette method is used. The silhouette value measures how similar a point is to its own cluster (cohesion) compared to other clusters (separation).
Q. How do you know when Gaussian Mixture Model is applicable?
An approach is to find the clusters using soft clustering methods and then see if they are gaussian. If they are then you can apply a GMM model which represents the whole dataset.
.....................................
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Company Name - Google
Role: Data Scientist
Q. Why do you use feature selection?
A. Feature selection offers a simple yet effective way to overcome the challenge of high dimensional data analysis by eliminating redundant and irrelevant data. Removing the irrelevant data improves learning accuracy, reduces the computation time, and facilitates an enhanced understanding for the learning model or data. .
Q. Whatโs the difference between Gaussian Mixture Model and K-Means?
A. The first visible difference between K-Means and Gaussian Mixtures is the shape the decision boundaries. GMs are somewhat more flexible and with a covariance matrix โ we can make the boundaries elliptical, as opposed to circular boundaries with K-means.
Another thing is that GMs is a probabilistic algorithm. If we compare both algorithms, the Gaussian mixtures seem to be more robust. However, GMs usually tend to be slower than K-Means because it takes more iterations of the EM algorithm to reach the convergence.
Q. How do you pick k for K-Means?
A. Calculate the Within-Cluster-Sum of Squared Errors (WSS) for different values of k, and choose the k for which WSS becomes first starts to diminish. In the plot of WSS-versus-k, this is visible as an elbow. Also, the silhouette method is used. The silhouette value measures how similar a point is to its own cluster (cohesion) compared to other clusters (separation).
Q. How do you know when Gaussian Mixture Model is applicable?
An approach is to find the clusters using soft clustering methods and then see if they are gaussian. If they are then you can apply a GMM model which represents the whole dataset.
.....................................
๐ดP.S: Join our Beginner to Advanced Data Science Combo Course and get our 4 courses at extra discounted price.
โ Data Science + ML Course = โน1249
โ Deep Learning = โน3499
โ SQL For Data Science = โน999
โ Maths and Statistic For Data Science = โน999
๐๐ปTotal Price:
Course Features
โ Hands on Guided assignments
โ Capstone End-to-end Projects
โ 1-1 Skype Live Doubt Clearance
โ Frequently Asked Interview QnAs PDF collection
โ Course completion Certificate
โ Resume and LinkedIn Review
โ Job Referrals after course completion
โ Job Opening Mails, Quiz, Newsletters and more
โ 1 Month Internship Opportunity
โ Self-Paced course with Lifetime Access
Start Your Data Science Journey Today!
Enroll Now ๐๐ป https://www.cloudyml.com/course-combo-package/
โค1
๐ดInternship Opening Alert
CloudyML is hiring for Data Science Research Intern
It will be paid internship.
Link ๐๐ป https://forms.gle/4wBELq3ATYbH6Tdp9
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It will be paid internship.
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Today's Interview QnAs
Company Name - Genpact
Role: Data Scientist
Q. How would you deal with missing random values from a data set?
A. Deletions. Pairwise Deletion. Listwise Deletion/ Dropping rows. Dropping complete columns. Basic Imputation Techniques. Imputation with a constant value. Imputation using the statistics (mean, median, mode) K-Nearest Neighbor Imputation.
Q. What is a Gaussian distribution and it's use?
A. In a Gaussian distribution(normal distribution), data is symmetrically distributed with no skew. When plotted on a graph, the data follows a bell shape, with most values clustering around a central region and tapering off as they go further away from the center.
Q. What is โcluster samplingโ?
A. Cluster sampling is a probability sampling technique where researchers divide the population into multiple groups (clusters) for research. Researchers then select random groups with a simple random or systematic random sampling technique for data collection and data analysis. Cluster sampling is defined as a sampling method where the researcher creates multiple clusters of people from a population where they are indicative of homogeneous characteristics and have an equal chance of being a part of the sample.
Q. What are the alternatives to PyTorch?
A. scikit-learn, Keysight Eggplant Platform, machine-learning in Python, V7, Personalizer, Kubeflow, Google Cloud TPU, python-recsys.
Company Name - Genpact
Role: Data Scientist
Q. How would you deal with missing random values from a data set?
A. Deletions. Pairwise Deletion. Listwise Deletion/ Dropping rows. Dropping complete columns. Basic Imputation Techniques. Imputation with a constant value. Imputation using the statistics (mean, median, mode) K-Nearest Neighbor Imputation.
Q. What is a Gaussian distribution and it's use?
A. In a Gaussian distribution(normal distribution), data is symmetrically distributed with no skew. When plotted on a graph, the data follows a bell shape, with most values clustering around a central region and tapering off as they go further away from the center.
Q. What is โcluster samplingโ?
A. Cluster sampling is a probability sampling technique where researchers divide the population into multiple groups (clusters) for research. Researchers then select random groups with a simple random or systematic random sampling technique for data collection and data analysis. Cluster sampling is defined as a sampling method where the researcher creates multiple clusters of people from a population where they are indicative of homogeneous characteristics and have an equal chance of being a part of the sample.
Q. What are the alternatives to PyTorch?
A. scikit-learn, Keysight Eggplant Platform, machine-learning in Python, V7, Personalizer, Kubeflow, Google Cloud TPU, python-recsys.
Today's Interview QnAs
Company Name - EY
Role: Data Scientist
Q. What is meant by โcurse of dimensionalityโ?
A. Curse of Dimensionality refers to a set of problems that arise when working with high-dimensional data. The dimension of a dataset corresponds to the number of attributes/features that exist in a dataset. A dataset with a large number of attributes, generally of the order of a hundred or more, is referred to as high dimensional data. Some of the difficulties that come with high dimensional data manifest during analyzing or visualizing the data to identify patterns, and some manifest while training machine learning models. The difficulties related to training machine learning models due to high dimensional data is referred to as โCurse of Dimensionalityโ.
Q. What is DBSCAN Clustering?
A. DBSCAN groups โdensely groupedโ data points into a single cluster. It can identify clusters in large spatial datasets by looking at the local density of the data points. The most exciting feature of DBSCAN clustering is that it is robust to outliers. It also does not require the number of clusters to be told beforehand, unlike K-Means, where we have to specify the number of centroids.
Q. What are the different forms of joins in a table?
A. SQL has many kinds of different joins including INNER JOIN, SELF JOIN, CROSS JOIN, and OUTER JOIN. In fact, each join type defines the way two tables are related in a query. OUTER JOINS can further be divided into LEFT OUTER JOINS, RIGHT OUTER JOINS, and FULL OUTER JOINS.
Q. What is Gradient Descent?
A. Gradient descent is an optimization algorithm which is commonly-used to train machine learning models and neural networks. Training data helps these models learn over time, and the cost function within gradient descent specifically acts as a barometer, gauging its accuracy with each iteration of parameter updates. Until the function is close to or equal to zero, the model will continue to adjust its parameters to yield the smallest possible error.
Company Name - EY
Role: Data Scientist
Q. What is meant by โcurse of dimensionalityโ?
A. Curse of Dimensionality refers to a set of problems that arise when working with high-dimensional data. The dimension of a dataset corresponds to the number of attributes/features that exist in a dataset. A dataset with a large number of attributes, generally of the order of a hundred or more, is referred to as high dimensional data. Some of the difficulties that come with high dimensional data manifest during analyzing or visualizing the data to identify patterns, and some manifest while training machine learning models. The difficulties related to training machine learning models due to high dimensional data is referred to as โCurse of Dimensionalityโ.
Q. What is DBSCAN Clustering?
A. DBSCAN groups โdensely groupedโ data points into a single cluster. It can identify clusters in large spatial datasets by looking at the local density of the data points. The most exciting feature of DBSCAN clustering is that it is robust to outliers. It also does not require the number of clusters to be told beforehand, unlike K-Means, where we have to specify the number of centroids.
Q. What are the different forms of joins in a table?
A. SQL has many kinds of different joins including INNER JOIN, SELF JOIN, CROSS JOIN, and OUTER JOIN. In fact, each join type defines the way two tables are related in a query. OUTER JOINS can further be divided into LEFT OUTER JOINS, RIGHT OUTER JOINS, and FULL OUTER JOINS.
Q. What is Gradient Descent?
A. Gradient descent is an optimization algorithm which is commonly-used to train machine learning models and neural networks. Training data helps these models learn over time, and the cost function within gradient descent specifically acts as a barometer, gauging its accuracy with each iteration of parameter updates. Until the function is close to or equal to zero, the model will continue to adjust its parameters to yield the smallest possible error.
Today's Interview QnAs
Company Name - Delhivery
Role: Data Scientist
Q. What is Lasso Regression and why do we use it for?
A. The lasso regression allows you to shrink or regularize these coefficients to avoid overfitting and make them work better on different datasets. This type of regression is used when the dataset shows high multicollinearity or when you want to automate variable elimination and feature selection.
Q. How do you deal with overfitting in Logistic regression?
A. One of the ways to combat over-fitting is to increase the training data size. Another way to combat over-fitting is to perform early stopping. A way to combat over-fitting is through regularization. Regularization techniques can be viewed as imposing certain prior distribution on the model parameters.
Q. What is Multicollinearity and how to deal with it?
A. Multicollinearity occurs when independent variables in a regression model are correlated. This correlation is a problem because independent variables should be independent. If the degree of correlation between variables is high enough, it can cause problems when you fit the model and interpret the results. 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. LASSO and Ridge regression are advanced forms of regression analysis that can handle multicollinearity.
Company Name - Delhivery
Role: Data Scientist
Q. What is Lasso Regression and why do we use it for?
A. The lasso regression allows you to shrink or regularize these coefficients to avoid overfitting and make them work better on different datasets. This type of regression is used when the dataset shows high multicollinearity or when you want to automate variable elimination and feature selection.
Q. How do you deal with overfitting in Logistic regression?
A. One of the ways to combat over-fitting is to increase the training data size. Another way to combat over-fitting is to perform early stopping. A way to combat over-fitting is through regularization. Regularization techniques can be viewed as imposing certain prior distribution on the model parameters.
Q. What is Multicollinearity and how to deal with it?
A. Multicollinearity occurs when independent variables in a regression model are correlated. This correlation is a problem because independent variables should be independent. If the degree of correlation between variables is high enough, it can cause problems when you fit the model and interpret the results. 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. LASSO and Ridge regression are advanced forms of regression analysis that can handle multicollinearity.
โค1
Today's Interview QnAs
Company Name - Cognizant
Role: Data Scientist
Q. How will you remove duplicates from dataframe based on particular column?
A. An important part of Data analysis is analyzing Duplicate Values and removing them. Pandas drop_duplicates() method helps in removing duplicates from the data frame. Subset parameter takes a column or list of column label. After passing columns, it will consider them only for duplicates.
Q. Explain ROC curve.
A. A Receiver Operator Characteristic (ROC) curve is a graphical plot used to show the diagnostic ability of binary classifiers.A ROC curve is constructed by plotting the true positive rate (TPR) against the false positive rate (FPR). The true positive rate is the proportion of observations that were correctly predicted to be positive out of all positive observations (TP/(TP + FN)). Similarly, the false positive rate is the proportion of observations that are incorrectly predicted to be positive out of all negative observations (FP/(TN + FP)).
Q. What is learning rate in gradient descent?
A. Gradient descent is the popular optimization algorithm used in machine learning to estimate the model parameters. Learning rate, generally represented by the symbol โฮฑโ, is a hyper-parameter used to control the rate at which an algorithm updates the parameter estimates or learns the values of the parameters.
Q. What do you understand by Recall and Precision?
A. Precision is defined as the fraction of relevant instances among all retrieved instances. Recall, sometimes referred to as โsensitivity, is the fraction of retrieved instances among all relevant instances. A perfect classifier has precision and recall both equal to 1.
Company Name - Cognizant
Role: Data Scientist
Q. How will you remove duplicates from dataframe based on particular column?
A. An important part of Data analysis is analyzing Duplicate Values and removing them. Pandas drop_duplicates() method helps in removing duplicates from the data frame. Subset parameter takes a column or list of column label. After passing columns, it will consider them only for duplicates.
Q. Explain ROC curve.
A. A Receiver Operator Characteristic (ROC) curve is a graphical plot used to show the diagnostic ability of binary classifiers.A ROC curve is constructed by plotting the true positive rate (TPR) against the false positive rate (FPR). The true positive rate is the proportion of observations that were correctly predicted to be positive out of all positive observations (TP/(TP + FN)). Similarly, the false positive rate is the proportion of observations that are incorrectly predicted to be positive out of all negative observations (FP/(TN + FP)).
Q. What is learning rate in gradient descent?
A. Gradient descent is the popular optimization algorithm used in machine learning to estimate the model parameters. Learning rate, generally represented by the symbol โฮฑโ, is a hyper-parameter used to control the rate at which an algorithm updates the parameter estimates or learns the values of the parameters.
Q. What do you understand by Recall and Precision?
A. Precision is defined as the fraction of relevant instances among all retrieved instances. Recall, sometimes referred to as โsensitivity, is the fraction of retrieved instances among all relevant instances. A perfect classifier has precision and recall both equal to 1.
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It will be PAID internship
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It will be PAID internship
If interested, follow the link below ๐๐ป
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Today's Interview QnAs
Company Name - IBM
Role - Data Scientist
Q1. What is P-value?
Ans. The p-value is the probability that the null hypothesis is true. (1 โ the p-value) is the probability that the alternative hypothesis is true. A low p-value shows that the results are replicable. A low p-value shows that the effect is large or that the result is of major theoretical, clinical or practical importance.
Q2. Forward and Backward selection? It's working?
Ans. Forward Selection chooses a subset of the predictor variables for the final model. Unlike forward stepwise selection, it begins with the full least squares model containing all p predictors, and then iteratively removes the least useful predictor, one-at-a-time. forward selection starts with a null model (with no predictors) and proceeds to add variables one at a time, and so unlike backward selection, it DOES NOT have to consider the full model (which includes all the predictors).
Q3. Fix multicollinearity?
Ans. To remove multicollinearities, we can do two things.
1. We can create new features
2. remove those features from our data.
Q4. Difference between Gradient boosting and random forest?
Ans. The two main differences are: How trees are built: random forests builds each tree independently while gradient boosting builds one tree at a time. Combining results: random forests combine results at the end of the process (by averaging or "majority rules") while gradient boosting combines results along the way.
.........................
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It Covers Python, ML, DL, SQL, Statistics and Maths, AWS Sagemaker, Model Deployment, 25+ Capstone Projects for ML and DL, Interview QnAs PDF Collections and comes with 1-1 Live Doubt Clearance Support.
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Company Name - IBM
Role - Data Scientist
Q1. What is P-value?
Ans. The p-value is the probability that the null hypothesis is true. (1 โ the p-value) is the probability that the alternative hypothesis is true. A low p-value shows that the results are replicable. A low p-value shows that the effect is large or that the result is of major theoretical, clinical or practical importance.
Q2. Forward and Backward selection? It's working?
Ans. Forward Selection chooses a subset of the predictor variables for the final model. Unlike forward stepwise selection, it begins with the full least squares model containing all p predictors, and then iteratively removes the least useful predictor, one-at-a-time. forward selection starts with a null model (with no predictors) and proceeds to add variables one at a time, and so unlike backward selection, it DOES NOT have to consider the full model (which includes all the predictors).
Q3. Fix multicollinearity?
Ans. To remove multicollinearities, we can do two things.
1. We can create new features
2. remove those features from our data.
Q4. Difference between Gradient boosting and random forest?
Ans. The two main differences are: How trees are built: random forests builds each tree independently while gradient boosting builds one tree at a time. Combining results: random forests combine results at the end of the process (by averaging or "majority rules") while gradient boosting combines results along the way.
.........................
๐ดIf you want to learn Data Science from scratch and crack interviews, join our Assignment based Data Science Combo course, trusted by 4500+ Learners.
It Covers Python, ML, DL, SQL, Statistics and Maths, AWS Sagemaker, Model Deployment, 25+ Capstone Projects for ML and DL, Interview QnAs PDF Collections and comes with 1-1 Live Doubt Clearance Support.
Total Worth of this course is Rs. 10389 but it is available at Rs. 3999/- today.
This offer gonna end on 19th March by 11:59PM, so Hurry Up!
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Python Interview Question asked in Amazon for Business Research Analyst
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Python Interview Question asked in Amazon for Business Research Analyst
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Python Interview Question asked in Tiger Analytics
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You will be learning Python, Machine Learning, Deep Learning, SQL, Statistics, AWS Sagemaker from basics to advanced level with Capstone End-to-End Projects that you can add in your resume and Interview QnAs PDF Collection for job preparation.
Not only that, you are getting 1-1 Live Doubt Clearance support on skype chat everyday. Mentors will resolve all of your queries and doubts within 10 mins.
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So what are you waiting for, join our course today at best affordable price and become a part of 4500+ Happy learners community and become a Job-Ready Data Scientist.
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30% of our learners coming from non-coding background like mechanical, civil, commerce, MBA, teaching etc have switched to Data science and getting other benefits from our Assignment based Data Science course.
It is not just a normal course, it is a complete Hands on practical learning experience of Data Science field from scratch.
You will be learning Python, Machine Learning, Deep Learning, SQL, Statistics, AWS Sagemaker from basics to advanced level with Capstone End-to-End Projects that you can add in your resume and Interview QnAs PDF Collection for job preparation.
Not only that, you are getting 1-1 Live Doubt Clearance support on skype chat everyday. Mentors will resolve all of your queries and doubts within 10 mins.
It is a self-paced course with lifetime course access so you can learn according to your time availability.
So what are you waiting for, join our course today at best affordable price and become a part of 4500+ Happy learners community and become a Job-Ready Data Scientist.
Visit Course Now๐๐ป https://www.cloudyml.com/course-combo-package/
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Today's Interview QnAs
Company Name - Mindtree
Role: Data Scientist
Q. What is central tendency ?
A. The central tendency is a descriptive description of a dataset represented by a single value that represents the data distribution's center. It does not provide information on individual values in the dataset, but it does provide a thorough overview of the entire dataset. The following measurements can be used to describe the central tendency of a dataset in general: Mean: Represents the sum of all values in a dataset divided by total number of values in the dataset. The median is the midpoint value in an ascending-ordered dataset. The most often occurring value in a dataset is defined by the mode.
Q. Which central tendency method is used If there exists any outliers ?
A. Because the value of the mean might be skewed by outliers, the median is frequently recommended in these instances. It will, however, depend on how powerful the outliers are. Using the mean as the measure of central tendency is usually preferred if they do not significantly alter the mean.
Q. Explain Central limit theorem ?
A. The central limit theorem (CLT) asserts that as the sample size grows higher, the distribution of a sample variable approaches a normal distribution (i.e., a "bell curve"), assuming that all samples are identical in size and independent of the population's actual distribution shape.
Q. What is Chi-Square test ?
A. A hypothesis testing method is the Chi-square test. Checking if observed frequencies in one or more categories match expected frequencies is one of two frequent Chi-square tests. The Chi-square test is used to determine whether your data is as expected. The test's core premise is to compare the observed values in your data to the expected values that you would see if the null hypothesis is true. The Chi-square goodness of fit test and the Chi-square test of independence are two regularly used Chi-square tests. Both tests use variables to categorize your data into groups.
..................
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Company Name - Mindtree
Role: Data Scientist
Q. What is central tendency ?
A. The central tendency is a descriptive description of a dataset represented by a single value that represents the data distribution's center. It does not provide information on individual values in the dataset, but it does provide a thorough overview of the entire dataset. The following measurements can be used to describe the central tendency of a dataset in general: Mean: Represents the sum of all values in a dataset divided by total number of values in the dataset. The median is the midpoint value in an ascending-ordered dataset. The most often occurring value in a dataset is defined by the mode.
Q. Which central tendency method is used If there exists any outliers ?
A. Because the value of the mean might be skewed by outliers, the median is frequently recommended in these instances. It will, however, depend on how powerful the outliers are. Using the mean as the measure of central tendency is usually preferred if they do not significantly alter the mean.
Q. Explain Central limit theorem ?
A. The central limit theorem (CLT) asserts that as the sample size grows higher, the distribution of a sample variable approaches a normal distribution (i.e., a "bell curve"), assuming that all samples are identical in size and independent of the population's actual distribution shape.
Q. What is Chi-Square test ?
A. A hypothesis testing method is the Chi-square test. Checking if observed frequencies in one or more categories match expected frequencies is one of two frequent Chi-square tests. The Chi-square test is used to determine whether your data is as expected. The test's core premise is to compare the observed values in your data to the expected values that you would see if the null hypothesis is true. The Chi-square goodness of fit test and the Chi-square test of independence are two regularly used Chi-square tests. Both tests use variables to categorize your data into groups.
..................
๐ดIf you want to learn Data Science from scratch and crack interviews, join our Assignment based Data Science Combo course, trusted by 4500+ Learners.
It Covers Python, ML, DL, SQL, Statistics and Maths, AWS Sagemaker, Model Deployment, 25+ Capstone Projects for ML and DL, Interview QnAs PDF Collections and comes with 1-1 Live Doubt Clearance Support.
Visit Course Now ๐๐ป https://www.cloudyml.com/course-combo-package/
Today's Interview QnAs
Company Name - IBM
Role - Data Scientist
Q1. What is P-value?
Ans. The p-value is the probability that the null hypothesis is true. (1 โ the p-value) is the probability that the alternative hypothesis is true. A low p-value shows that the results are replicable. A low p-value shows that the effect is large or that the result is of major theoretical, clinical or practical importance.
Q2. Forward and Backward selection? It's working?
Ans. Forward Selection chooses a subset of the predictor variables for the final model. Unlike forward stepwise selection, it begins with the full least squares model containing all p predictors, and then iteratively removes the least useful predictor, one-at-a-time. forward selection starts with a null model (with no predictors) and proceeds to add variables one at a time, and so unlike backward selection, it DOES NOT have to consider the full model (which includes all the predictors).
Q3. Fix multicollinearity?
Ans. To remove multicollinearities, we can do two things.
1. We can create new features
2. remove those features from our data.
Q4. Difference between Gradient boosting and random forest?
Ans. The two main differences are: How trees are built: random forests builds each tree independently while gradient boosting builds one tree at a time. Combining results: random forests combine results at the end of the process (by averaging or "majority rules") while gradient boosting combines results along the way.
..................
๐ดIf you want to learn Data Science from scratch and crack interviews, join our Assignment based Data Science Combo course, trusted by 4500+ Learners.
It Covers Python, ML, DL, SQL, Statistics and Maths, AWS Sagemaker, Model Deployment, 25+ Capstone Projects for ML and DL, Interview QnAs PDF Collections and comes with 1-1 Live Doubt Clearance Support.
Visit Course Now ๐๐ป https://www.cloudyml.com/course-combo-package/
Company Name - IBM
Role - Data Scientist
Q1. What is P-value?
Ans. The p-value is the probability that the null hypothesis is true. (1 โ the p-value) is the probability that the alternative hypothesis is true. A low p-value shows that the results are replicable. A low p-value shows that the effect is large or that the result is of major theoretical, clinical or practical importance.
Q2. Forward and Backward selection? It's working?
Ans. Forward Selection chooses a subset of the predictor variables for the final model. Unlike forward stepwise selection, it begins with the full least squares model containing all p predictors, and then iteratively removes the least useful predictor, one-at-a-time. forward selection starts with a null model (with no predictors) and proceeds to add variables one at a time, and so unlike backward selection, it DOES NOT have to consider the full model (which includes all the predictors).
Q3. Fix multicollinearity?
Ans. To remove multicollinearities, we can do two things.
1. We can create new features
2. remove those features from our data.
Q4. Difference between Gradient boosting and random forest?
Ans. The two main differences are: How trees are built: random forests builds each tree independently while gradient boosting builds one tree at a time. Combining results: random forests combine results at the end of the process (by averaging or "majority rules") while gradient boosting combines results along the way.
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