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HDFC Bank
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I applied via Naukri.com and was interviewed in Mar 2024. There were 3 interview rounds.
Machine learning algorithms are tools used to analyze data, identify patterns, and make predictions without being explicitly programmed.
Machine learning algorithms can be categorized into supervised, unsupervised, and reinforcement learning.
Examples of machine learning algorithms include linear regression, decision trees, support vector machines, and neural networks.
These algorithms require training data to learn patte...
Developing a credit risk model involves several steps to assess the likelihood of a borrower defaulting on a loan.
1. Define the problem and objectives of the credit risk model.
2. Gather relevant data such as credit history, income, debt-to-income ratio, etc.
3. Preprocess the data by handling missing values, encoding categorical variables, and scaling features.
4. Select a suitable machine learning algorithm such as logi...
AIC and BIC are statistical measures used for model selection in the context of regression analysis.
AIC (Akaike Information Criterion) is used to compare the goodness of fit of different models. It penalizes the model for the number of parameters used.
BIC (Bayesian Information Criterion) is similar to AIC but penalizes more heavily for the number of parameters, making it more suitable for model selection when the focus...
XGBoost is a popular gradient boosting library while LightGBM is a faster and more memory-efficient alternative.
XGBoost is known for its accuracy and performance on structured/tabular data.
LightGBM is faster and more memory-efficient, making it suitable for large datasets.
LightGBM uses a histogram-based algorithm for splitting whereas XGBoost uses a level-wise tree growth strategy.
I applied via Campus Placement and was interviewed before Dec 2023. There were 2 interview rounds.
The first technical round will cover how computer vision works, including the advantages and disadvantages of regression and random forest. It will also include discussions on when to use precision and recall, methods to reduce false positives, and criteria for selecting different models. Additionally, disadvantages of PCA will be addressed, along with project-related questions. The second round will focus on standard aptitude tests, while the third round will involve a casual conversation with the Executive Vice President.
Normal aptitude questions
I applied via Naukri.com and was interviewed before May 2023. There were 2 interview rounds.
Test was conducted on datacamp assessments. Overall, there were three tests.
1. Stats test
2. ML test
3. Python/coding test
What people are saying about HDFC Bank
I applied via Job Portal and was interviewed in Dec 2021. There were 2 interview rounds.
HDFC Bank interview questions for designations
I was interviewed in Mar 2023.
I applied via LinkedIn and was interviewed in Oct 2021. There were 5 interview rounds.
I applied via LinkedIn and was interviewed in Oct 2021. There were 5 interview rounds.
Machine learning evaluation metrics are used to measure the performance of a model.
Accuracy
Precision
Recall
F1 Score
ROC Curve
AUC
Confusion Matrix
Mean Squared Error
Root Mean Squared Error
R-squared
I applied via Campus Placement and was interviewed in Dec 2022. There were 4 interview rounds.
Hard level aptitude questions... Time taking sums
Step function is a function that returns a constant value for a certain range of inputs.
In machine learning, step functions are used as activation functions in neural networks.
They are typically used in binary classification problems where the output is either 0 or 1.
Examples include Heaviside step function and sigmoid step function.
Investigate the model performance metrics and adjust the threshold for classification.
Analyze the confusion matrix to understand the distribution of false positives.
Adjust the threshold for classification to reduce false positives.
Consider using different evaluation metrics like precision, recall, and F1 score.
Explore feature importance to identify variables contributing to false positives.
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