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I applied via Referral and was interviewed in Jan 2021. There were 3 interview rounds.
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I applied via Monster and was interviewed before Aug 2020. There was 1 interview round.
Hashmap is unordered while LinkedHashmap maintains insertion order.
Hashmap uses hashing to store key-value pairs while LinkedHashmap uses a doubly linked list to maintain order.
Hashmap allows null values and one null key while LinkedHashmap does not allow null keys or values.
Hashmap has O(1) time complexity for basic operations while LinkedHashmap has O(1) for insertion and deletion but O(n) for iteration.
Example: Hash...
I applied via Referral and was interviewed in Feb 2023. There were 2 interview rounds.
I applied via Naukri.com and was interviewed in Nov 2021. There was 1 interview round.
I applied via Referral and was interviewed in Feb 2022. There were 2 interview rounds.
I was asked 2 programming questions.
I applied via Naukri.com and was interviewed in Aug 2023. There were 3 interview rounds.
Mcq tests on java ,springboot and data base concepts
I applied via Referral and was interviewed in Jan 2022. There were 2 interview rounds.
I applied via Walk-in
45 mins, maths
Some mcq questions and coding question. Both where easy to medium level. Prepare combination/permutation/Time complexity etc
A question related to Binary search and some other follow ups.
I applied via Campus Placement and was interviewed before Sep 2023. There was 1 interview round.
Random forest is an ensemble learning method that builds multiple decision trees and merges them to improve accuracy and prevent overfitting.
Random forest is a type of ensemble learning method.
It builds multiple decision trees during training.
Each tree is built using a subset of the training data and a random subset of features.
The final prediction is made by averaging the predictions of all the individual trees.
Random...
Boosting is a machine learning ensemble technique where multiple weak learners are combined to create a strong learner.
Boosting is an iterative process where each weak learner is trained based on the errors of the previous learners.
Examples of boosting algorithms include AdaBoost, Gradient Boosting, and XGBoost.
Boosting is used to improve the accuracy of models and reduce bias and variance.
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