ML2 Yazeed Basic Math

What is the process of fine-tuning and improving a model to enhance its performance?

• Representation

• Evaluation

• Optimization

• Algorithms

Which technique is used to prevent overfitting by adding a penalty to the loss function?

• Regularization

• Feature Engineering

• Ensemble Methods

• Gradient Descent

What is the role of lambda in Regularization?

• It controls the strength of the regularization penalty

• It enhances the performance of the model

• It minimizes the model's loss function

• It evaluates the performance of regression models

What is the Curse of Dimensionality?

• The process of transforming raw data into features

• The challenges that arise when working with high-dimensional data

• The importance of domain knowledge

• The notion that a more complex learner isn't always the best solution

Which metric is commonly used to evaluate the performance of regression models?

• Precision

• Recall

• Accuracy

• Mean Squared Error (MSE)

What is the importance of domain knowledge in modeling?

• It prevents overfitting

• It helps in selecting relevant features

• It enhances the performance of the model

• It controls the strength of the regularization penalty

What is the process of transforming raw data into features that better represent the underlying problem to the predictive models?

• Optimization

• Evaluation

• Representation

• Feature Engineering

Which algorithm is more resistant to multicollinearity but doesn't reduce the coefficients to zero?

• KNN

• Naive Bayes

• Regression

• Ridge

What occurs when a model performs well on the training data but poorly on unseen data?

• Optimization

• Overfitting

• Feature Engineering

• Ensemble Methods

What is the technique used to minimize the model's loss function by iteratively moving towards the minimum value?

• Gradient Descent

• Ensemble Methods

• Mean Squared Error (MSE)

• Regularization