Evalueserve Consultant Interview Questions, Process, and Tips

Bagging and boosting are ensemble learning techniques used to improve the performance of machine learning models by combining multiple weak learners.

• Bagging (Bootstrap Aggregating) involves training multiple models independently on different subsets of the training data and then combining their predictions through averaging or voting.

• Boosting involves training multiple models sequentially, where each subsequent model c...read more

Overfitting is when a model learns the training data too well, leading to poor performance on new, unseen data.

• Overfitting occurs when a model is too complex and captures noise in the training data.

• It can be mitigated by using techniques like cross-validation, regularization, and early stopping.

• Examples of overfitting include a decision tree with too many branches or a neural network with too many hidden layers.

Discrete variables can only take specific values, while continuous variables can take any value within a range.

• Discrete variables are countable and have distinct values, such as number of students in a class.

• Continuous variables can take any value within a range, such as height or weight.

• Discrete variables are often represented by integers, while continuous variables are represented by real numbers.

I have extensive knowledge of various tools and their practical applications in consulting projects.

• Proficient in using tools like Microsoft Excel, PowerPoint, and Project for data analysis and project management

• Familiar with CRM systems like Salesforce for client relationship management

• Experience with industry-specific tools like Tableau for data visualization

• Ability to adapt and learn new tools quickly to meet projec