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I applied via Referral and was interviewed in Jun 2024. There was 1 interview round.
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Random forest is an ensemble learning method used for classification and regression tasks.
Random forest is a collection of decision trees that are trained on random subsets of the data.
Each tree in the random forest independently predicts the target variable, and the final prediction is made by averaging the predictions of all trees.
Random forest is robust to overfitting and noisy data, and it can handle large datasets...
I applied via Naukri.com and was interviewed in Jun 2024. There were 3 interview rounds.
2 leetcode easy questions were asked
Data science case study
Basic Aptitude questions
I applied via Recruitment Consulltant and was interviewed in Sep 2022. There were 4 interview rounds.
Random forest uses decision trees to split data into subsets based on feature importance.
Random forest builds multiple decision trees and selects the best split based on feature importance.
Each decision tree splits data into subsets based on a randomly selected subset of features.
The best split is determined by minimizing impurity or maximizing information gain.
Random forest can handle missing values and outliers.
Rando...
I applied via Company Website and was interviewed before May 2021. There was 1 interview round.
I applied via Naukri.com and was interviewed before Oct 2020. There were 3 interview rounds.
Data Scientist interview questions on model building, random forest, ROC curve, gradient boosting, and real estate valuation
For model building, I followed the CRISP-DM process and used various algorithms like logistic regression, decision trees, and random forest
Random forest hyperparameters include number of trees, maximum depth, minimum samples split, and minimum samples leaf
ROC curve is a graphical representation of...
Fundamentals of classical machine learning
Classical machine learning involves algorithms that learn from data and make predictions or decisions.
Common algorithms include linear regression, decision trees, support vector machines, and k-nearest neighbors.
Key concepts include training data, testing data, model evaluation, and hyperparameter tuning.
Classical ML is often used for tasks like classification, regression, clus
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