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I applied via Job Portal and was interviewed in Apr 2023. There were 2 interview rounds.
Lists and tuples are essential data structures in Python for storing and manipulating collections of items.
Lists are mutable and allow for dynamic resizing, making them suitable for scenarios where the size of the collection may change over time.
Tuples are immutable and provide a fixed-size collection, making them useful for scenarios where the collection should not be modified.
Lists and tuples can be used to store and...
Boosting techniques are ensemble methods that combine weak learners to create a strong learner.
Boosting algorithms iteratively train weak models and give more weight to misclassified instances.
Examples of boosting techniques include AdaBoost, Gradient Boosting, and XGBoost.
Boosting can be used for classification and regression tasks.
Boosting algorithms aim to reduce bias and variance in the final model.
Boosting can imp...
Pruning is a technique used in machine learning to reduce the size of a decision tree by removing unnecessary branches.
Pruning helps prevent overfitting by simplifying the model.
It improves the model's generalization ability by reducing complexity.
Pruning can be done through pre-pruning or post-pruning.
Pre-pruning involves setting a threshold to stop tree growth early.
Post-pruning involves removing branches that do not...
I applied via Referral and was interviewed in Sep 2023. There were 2 interview rounds.
I applied via Referral and was interviewed in Mar 2021. There were 3 interview rounds.
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