1. How to handle model overfitting and model underfitting situations ? 2. In which situations should we standardize the data and where it is not required ? 3. How does Decision Trees work ? 4. How to select features for a problem statement ?

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Answering common questions related to data science concepts and techniques.

To handle model overfitting, one can use techniques like cross-validation, regularization, and early stopping. For model underfitting, consider using more complex models or adding more features.
Standardizing data is important for algorithms like K-Nearest Neighbors and Support Vector Machines. It is not required for tree-based models like Decision Trees and Random Forests.
Decision Trees work by recursively splitting the data based on feature values to create a tree structure that predicts the target variable. They are easy to interpret but prone to overfitting.
Feature selection can be done using techniques like correlation analysis, feature importance from models like Random Forest, or dimensionality reduction methods like PCA.

1. How to handle model overfitting and model underfitting situations ? 2. In which situations should we standardize the data and where it is not required ? 3. How does Decision Trees work ? 4. How to select features for a problem statement ?

Angel One Data Scientist 2 interview questions & answers