Supervised Learning Flashcards
What is machine learning in simple terms?
What are the two main types of machine learning algorithms?
What is Supervised Learning, and what are some common tasks it is used for?
What is linear regression, and what is the goal of this algorithm?
What is the linear regression function definition?
What are other names for the variables w and b in linear regression?
What is the cost function formula for a simple linear function.
How is the cost function used to measure the difference between the predicted value and actual label for each training example?
How is the cost function used to aggregate the error across all examples in the training set?
What is the regularization term used in the cost function for linear regression, and why is it helpful for gradient computation?
What is the goal of gradient descent in machine learning, and how is the gradient used in this process?
How does the learning rate affect the efficiency of the gradient descent algorithm?
What is the consequence of using a learning rate that is too small in gradient descent?
What is the consequence of using a learning rate that is too large in gradient descent?
How is the gradient used to update the values of the parameters during optimization process in gradient descent.
How is the choice of learning rate related to the convergence rate of the gradient descent algorithm?
What is the equation for the model’s prediction with multiple variables give by the linear model?
What is vectorization, and why is it useful?
What is the vector notation equation for the linear model?
What happens to the parameters w and b in multiple linear regression?
What is the equation for the cost function with multiple variables?
What is feature engineering and why is it important for machine learning
What are the different tasks involved in feature engineering?
Explain the process of feature scaling, and what it is important for machine learning.
What is polynomial regression, and how does it relate to feature engineering?
Provide an example of how you have used feature engineering in your project.
What is classification, and how does it differ from other types of machine learning techniques?
Explain the logistic regression model and how it is used for binary classification models?
What is the sigmoid function, and how is it used in logistic regression?
What is a decision boundary, and how is it used in logistic regression?
What is a decision boundary, and how is it determined in binary classification problems?
Why is the squared error cost function not suitable for logistic regression?
What is the cost function in logistic regression and how is it calculated?
Explain the difference between loss and cost function in machine learning.
How does gradient descent work in logistic regression?
What are some advantages of logistic regression over other classification algorithms.
How do you evaluate the performance of a logistic regression model?
What is overfitting, and how does it occur in machine learning?
Explain the differences between overfitting and underfitting.
How is overfitting addressed?
What is regularization, and how does it address overfitting?
What is the role of the regularization parameter in controlling the trade off between model fit and the model complexity?
What is the difference between the cost functions of regularized and unregularized linear regression?
What is the difference between the cost functions of regularized and unregularized logistic regression?
What is the gradient calculation for regularized linear and logistic regression, and how does it differ from the gradient calculation for unregularized regression?
