Random Forest and Decision Tree Interview Questions
Topic – NLP Interview Questions
Welcome to the 2200 questions series from The Data Monk, in this series we will cover all the topics in a Question-Answer mode that are required for anyone who wants to make a career in the following field:-
– Data Analysis
– Business Analysis
– Business Intelligence Engineering
– Machine Learning
– Data Science
– Product Analysis
– Data Engineering
– Risk Analysis
These 2200 questions are useful for anyone who is in their 2nd-3rd year of engineering to 8-10 years of experience in the IT industry( be it QA/Development/Support) and are willing to make a career in Analytics.
Why Analytics is a domain for you?
If you want to make a handsome switch with a good package then Analytics is for you because of the following reasons:-
– It is a high-paying job
– It is interesting as you will have a good impact on the growth of the organization
– It involves a lot of things like requirement gathering, building logic, making ETL, pipeline creation, reporting to the CXOs, and so on. So, it is a very impactful role
– It has a HUGE demand in the future as the data will keep on growing and so will your role
How much does an analytics role pay?
The CTC of the role will definitely depend on multiple factors but just to give you a glimpse of it:-
“Anyone from a tier 2-3 college with good knowledge of the material that we are providing will have a fair chance to bag something like 15+ LPA for a fresher. The more you grind the better you get and the CTC grows with experience.”
Now coming back to why you should try The Data Monk for your Analytics journey.
Why The Data Monk?
We are a group of 30+ Analytics Engineers working in various product-based companies like Zomato, Ola, OYO, Google, Rapido, Uber, Ugam, BYJUs, etc. and we observed that people do not have a well-structured way to enhance their knowledge. There are multiple courses here and there, but no one has consolidated what needs to be learned in order to move to the analytics domain.
Further, there are courses from Large institutes where they charge you something like 2-5 lacks and try to teach you everything from Data structure to SQL to Power BI to ML. You do not have to spend so much on these topics.
We followed a very old-school way, take a topic and solve 100-200 questions on these topics. Learn them, understand them, and revise them. This should be enough for you to crack that domain.
For example, if I am a very beginner in SQL, then I will just try to solve 200 questions starting from the definition to advance level questions. After solving and revising these questions I should have a good amount of knowledge to answer 6 out of 10 questions asked in an interview and going by that calculation I can be a strong candidate in 5-7 out of 10 companies.
See, by the end, you need to convert a job first and then keep on learning in the organization.
Most of the books are on questions like ‘250 questions to crack SQL interview’ and this will cost you around 250 rupees, take the book, understand, and learn it. This small amount can bag you a 15 LPA job 🙂
You can trust us as we have guided more than 1000 people to make a career in Analytics
Random Forest and Decision Tree Interview Questions
2200 Analytics Interview Questions
Chapter 1 – SQL – 250 SQL questions to Ace any Analytics Intervie
Chapter 2 – Python – 200 Most Asked Python Interview Questions
Chapter 3 – Pandas – 100 Most Asked Pandas Interview Questions with Solution
Chapter 4 – Numpy – 100 Most Asked Numpy Interview Questions with solution
Chapter 5 – Case Study and Guesstimate – 100 Case Study and Guesstimate with a complete solution
Chapter 6 -Linear Regression – 50 Most Asked Linear Regression Interview Questions with solution
Chapter 7 – Logistic Regression – 50 Most Asked Logistic Regression Interview Questions with solution
Chapter 8 – Natural Language Processing – 100 Most Asked NLP Questions with Solution
Chapter 9 – Decision Tree and Random Forest – 100 Most Asked RF and DT interview questions with solution
Decision Tree
949. What is a decision tree, and how does it work?
950. What are the advantages and disadvantages of using decision trees as a machine learning model?
951. What are the different types of decision trees, and how do they differ from each other?
952. What is entropy, and how is it used in decision tree algorithms?
953. What is the difference between the Gini impurity and entropy as measures of impurity in decision trees?
954. How are decision trees used in regression problems, and what is the difference between regression trees and classification trees?
955. What is overfitting, and how can it be addressed in decision tree models?
956. How are missing values handled in decision tree algorithms?
957. What is pruning, and how is it used to prevent overfitting in decision tree models?
958. What are some common algorithms used to build decision trees, and how do they differ from each other?
959. How can decision trees be used in combination with other machine learning models, such as random forests or gradient boosting machines?
960. How can the performance of a decision tree model be evaluated, and what metrics are commonly used?
961. What is the bias-variance tradeoff in machine learning, and how does it apply to decision trees?
962. How can decision trees be used for feature selection, and what advantages does this approach offer?
963. What are some limitations of decision trees, and when might they not be the best choice of model for a given problem?
964. List down some popular algorithms used for deriving Decision Trees along with their attribute selection measures.
965. Explain the CART Algorithm for Decision Trees.
966. List down the attribute selection measures used by the ID3 algorithm to construct a Decision Tree.
967. Briefly explain the properties of Gini Impurity.
968. Briefly explain the properties of Gini Impurity.
969. Measurement of the performance of decision tree algorithm.
970. What is F1 Score?
971. What is Recall?
972. What AUC?
973. What is a confusion matrix?
974. Can decision trees handle categorical variables? If so, how are they treated?
975. How does the depth of a decision tree affect its performance and complexity?
976. How can decision trees be used to perform feature selection, and what advantages does this approach offer over other feature selection methods?
977. What is the difference between a greedy algorithm and an exhaustive search algorithm for building decision trees?
978. Can decision trees be used for time-series forecasting or sequence prediction problems? If so, how are they adapted?
979. What is Pre-Pruning and Post-Pruning?
980. How to solve the problem of overfitting in Decision Tree?
981. What is Pre-Pruning and Post-Pruning?
982. What are the ways of evaluation of the Continuous Variable Decision
Tree?
983. What is Accuracy Score?
984. What is classification report?
985. F1_score is the harmonic mean of the precision and the recall.
986. What are the Advantages of Decision Tree?
987. What are the Disadvantages of Decision Tree?
988. Which is better Linear model or Tree-Based-Model?
989. What is hyperparameter tuning in Machine Learning?
990. What are the various hyperparameters used in Decision Tree for
tuning?
991. Steps for performing Decision Tree in Python.
Random Forest
992. What is a random forest?
993. What is the difference between a decision tree and a random forest?
994. How does a random forest work?
995. What are the advantages of using a random forest algorithm?
996. What are some of the parameters that can be tuned in a random forest algorithm?
997. What is bagging in the context of a random forest?
998. How does the random forest algorithm handle missing data?
999. What are some of the techniques used to evaluate the performance of a random forest model?
1000. What is the feature importance in a random forest model?
1001. What are the limitations of the random forest algorithm?
1002. Does Random Forest need Pruning? Why or why not?
1003.. Explain how the Random Forests give output for Classification, and Regression problems?
1004. How is a Random Forest related to Decision Trees?
1005. How would you find the optimal size of the Bootstrapped Dataset?
1006. What are Ensemble Methods?
1007. Explain the advantages of using Random Forest
1008. How does Random Forest handle missing values?
1009. How is it possible to perform Unsupervised Learning with Random Forest?
1010. How would you improve the performance of Random Forest?
1011. What are proximities in Random Forests?
1012. What does Random refer to in Random Forest?
1013. What is Entropy?
1014. Why Random Forest models are considered not interpretable?
1015. Why is the training efficiency of Random Forest better than Bagging?
1016. Implement Random Forest in Python
1017. What is n_estimators in Random forest?
1018. What is random_state in random forest?
1019. What are the hyperparameters in Random Forest?
1020. What is max_depth in Random Forest ?
1021. What is min_samples_split in Random Forest?
1022. What is min_smaples_leaf in Random Forest?
1023. What is max_features in Random Forest?
1024. What is the criterion in Random Forest?
1025. What is bootstrap in Random Forest?
1026. Implement Random Forest in R.
1027. Working of Random Forest Classifier?
1028. Working of Random Forest Regresor
1029. Grid Search in Hyperparameter tuning?
1030. Random Search in hyperparameter tuning?
1031. What is the impact of correlated features on a Random Forest model, and how can this issue be addressed?
1032. Can a Random Forest model suffer from overfitting? If so, how can overfitting be avoided or mitigated?
1033. How can the importance of individual trees in a Random Forest model be measured and used to improve the overall model?
1034. How does the out-of-bag (OOB) error estimate work in Random Forest, and what are its limitations?
1035. What is the difference between a feature selection technique and a feature importance measure in the context of Random Forest?
1036. How can the performance of a Random Forest model be improved by reducing the correlation among the trees?
1037. How can imbalanced class distributions affect the performance of a Random Forest model, and what techniques can be used to address this issue?
1038. How can the computational efficiency of a Random Forest model be improved for large datasets or high-dimensional feature spaces?
1039. How can the interpretability of a Random Forest model be improved, and what techniques can be used to extract insights from the model?
1040. What are some of the limitations and drawbacks of Random Forest, and when might it not be the best choice of model for a given problem?
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1. 2200 Interview Questions to become Full Stack Analytics Professional – 2200 Most Asked Interview Questions
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