HCLTech Interview Questions and Answers

Random forest is an ensemble method using multiple decision trees, XGBoost is a gradient boosting algorithm that builds trees sequentially.

• Random forest is an ensemble learning method that builds multiple decision trees and combines their predictions.

• Decision tree is a single tree model that makes decisions based on features to predict outcomes.

• XGBoost is a gradient boosting algorithm that builds trees sequentially, optimizing the model's performance.

• Random forest reduces ove...read more

Delete removes specific rows from a table, while Truncate removes all rows from a table.

• Delete is a DML command, while Truncate is a DDL command.

• Delete can be rolled back, while Truncate cannot be rolled back.

• Delete maintains the table structure and indexes, while Truncate resets the table structure and indexes.

• Delete triggers delete triggers and delete constraints, while Truncate does not trigger any triggers or constraints.

• Delete is slower than Truncate for large tables.

I can code a program in Python and Java.

• I am proficient in both Python and Java programming languages.

• I have experience in developing web applications using Python and Java frameworks.

• I can write efficient and optimized code in both languages.

• I can develop programs for data analysis, machine learning, and automation using Python.

• I can develop enterprise-level applications using Java.

• Examples: Python - a program to scrape data from a website, Java - a program to implement a so...read more

Axtria promotes a collaborative and inclusive culture with a focus on innovation and growth.

• Axtria values diversity and inclusion in the workplace

• The company encourages innovation and creativity

• There are policies in place to support employee growth and development

• Axtria promotes a collaborative work environment

Ridge and lasso regression are both regularization techniques used in linear regression to prevent overfitting by adding penalty terms to the cost function.

• Ridge regression adds a penalty term equivalent to the square of the magnitude of coefficients, while lasso regression adds a penalty term equivalent to the absolute value of the magnitude of coefficients.

• Ridge regression tends to shrink the coefficients towards zero but does not eliminate them completely, while lasso regr...read more