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I appeared for an interview in Sep 2024.
A coding test was administered to address several machine learning problem statements.
ML metrics help evaluate model performance, each with trade-offs affecting accuracy, interpretability, and application.
Accuracy vs. Precision: High accuracy may come with low precision in imbalanced datasets. Example: Classifying rare diseases.
Recall vs. F1 Score: High recall may lower F1 score, impacting balance in precision and recall. Example: Fraud detection.
ROC-AUC vs. PR-AUC: ROC-AUC is sensitive to class imbalan...
XGBoost is an efficient implementation of gradient boosting that optimizes performance and accuracy through ensemble learning.
1. **Gradient Boosting Framework**: XGBoost builds models in a sequential manner, where each new model corrects errors made by the previous ones.
2. **Decision Trees**: It primarily uses decision trees as base learners, where each tree is built to minimize the loss function.
3. **Regularization**:...
This program reverses an array containing both integers and strings.
Use a loop to iterate through the array from the last index to the first.
Create a new array to store the reversed elements.
Example: For input ['apple', 1, 'banana', 2], output should be [2, 'banana', 1, 'apple'].
Linear programming optimizes a linear objective function subject to linear constraints, finding the best outcome in a feasible region.
Linear programming involves maximizing or minimizing a linear objective function.
Constraints are linear inequalities that define the feasible region.
The feasible region is typically a convex polygon in multi-dimensional space.
The Simplex method is a popular algorithm used to solve linear...
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