Senior Data Scientist Lead Interview Questions and Answers

Utilize feature engineering techniques like one-hot encoding or target encoding to handle datasets with many categories.

• Use feature engineering techniques like one-hot encoding to convert categorical variables into numerical values

• Consider using target encoding to encode categorical variables based on the target variable

• Apply dimensionality reduction techniques like PCA or LDA to reduce the number of features

• Use tree-based models like Random Forest or XGBoost which can handle...read more

Dropout helps prevent overfitting in neural networks by randomly setting a fraction of input units to zero during training.

• Dropout helps in preventing overfitting by reducing the interdependence between neurons

• It acts as a regularization technique by randomly setting a fraction of input units to zero during training

• Dropout forces the network to learn redundant representations, making it more robust and generalizable

• It can be applied to different layers of the neural network t...read more

XGBoost can handle missing values (NaN) by assigning them to a default direction during tree construction.

• XGBoost treats NaN values as missing values and learns the best direction to go at each node to handle them

• During tree construction, XGBoost assigns NaN values to the default direction based on the training data statistics

• XGBoost can handle missing values in both input features and target variables

Serialization in Python is the process of converting a Python object into a byte stream to store or transmit data.

• Serialization is used to convert complex data structures like lists, dictionaries, and objects into a format that can be easily stored or transmitted.

• The 'pickle' module in Python is commonly used for serialization, allowing objects to be serialized and deserialized.

• Serialization is important for saving and loading data, as well as for communication between differ...read more