Bison Plastics Interview Questions and Answers

Hyperparameters of XGBoost, Random Forest, and SVM can be tuned using techniques like grid search, random search, and Bayesian optimization.

• For XGBoost, important hyperparameters to tune include learning rate, maximum depth, and number of estimators.

• For Random Forest, important hyperparameters to tune include number of trees, maximum depth, and minimum samples split.

• For SVM, important hyperparameters to tune include kernel type, regularization parameter, and gamma value.

• Grid ...read more

Hyperparameters are settings that control the behavior of machine learning algorithms.

• Hyperparameters are set before training the model.

• They control the learning process and affect the model's performance.

• Examples include learning rate, regularization strength, and number of hidden layers.

• Optimizing hyperparameters is important for achieving better model accuracy.

Steps to fit a time series model

• Identify the time series pattern

• Choose a suitable model

• Split data into training and testing sets

• Fit the model to the training data

• Evaluate model performance on testing data

• Refine the model if necessary

• Forecast future values using the model

Ridge and LASSO are regularization techniques used in linear regression to prevent overfitting.

• Ridge adds a penalty term to the sum of squared errors, which shrinks the coefficients towards zero but doesn't set them exactly to zero.

• LASSO adds a penalty term to the absolute value of the coefficients, which can set some of them exactly to zero.

• The geometric interpretation of Ridge is that it adds a constraint to the size of the coefficients, which shrinks them towards the origi...read more

RNN and CNN are neural network architectures used for different types of data.

• RNN is used for sequential data like time series, text, speech, etc.

• CNN is used for grid-like data like images, videos, etc.

• RNN has feedback connections while CNN has convolutional layers.

• RNN can handle variable length input while CNN requires fixed size input.

• Both can be used for classification, regression, and generation tasks.