Ripik.ai Data Scientist Interview Questions, Process, and Tips

Bagging and Boosting are ensemble learning techniques used to improve the performance of machine learning models.

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

• Random Forest is an example of a bagging algorithm where decision trees are trained on random subsets of the data.

• Boosting focuses on trainin...read more

Hyperparameters are parameters that are set before the learning process begins. Hyperparameter tuning is the process of selecting the best hyperparameters for a machine learning model.

• Hyperparameters are not learned during the training process, but are set before training begins.

• Examples of hyperparameters include learning rate, number of hidden layers in a neural network, and regularization strength.

• Hyperparameter tun...read more

Covariance measures the relationship between two variables, while colinearity refers to the situation where two or more predictors in a multiple regression model are highly correlated.

• Covariance is a measure of how two variables change together. A positive covariance indicates that the variables tend to increase or decrease together, while a negative covariance indicates that one variable tends to increase as the other...read more

Chi-square test is a statistical test used to determine if there is a significant association between two categorical variables.

• Used to compare observed data with expected data based on a hypothesis

• Calculates the difference between observed and expected frequencies

• Commonly used in research studies to analyze relationships between variables