10 Questions
What is the main challenge in finding the optimal coefficients for a logistic regression model?
The infinite number of possible combinations
What is the purpose of gradient descent in logistic regression?
To find the optimal coefficients
What is the formula for the partial derivative of the loss function with respect to a parameter θj?
∂/∂θj J(θ) = 1/m * Σ [σ(θT * x) - y(i)]
What is the purpose of the batch gradient descent algorithm?
To update the model parameters using the entire training dataset
What is the main difference between stochastic gradient descent and mini-batch gradient descent?
The number of instances used to update the model parameters
What is the purpose of the logistic regression model in the iris example?
To detect the iris-virginica type based on the petal width feature
What is the output of the logistic regression model in the iris example?
A probability distribution
What is the purpose of the plot in the iris example?
To visualize the decision boundary
How is the class predicted in the logistic regression model?
By selecting the class with the highest probability
What is the advantage of using logistic regression over linear regression?
It can handle binary classification problems
Test your understanding of unsupervised learning, specifically clustering, including the K-Means algorithm, centroids, and determining the optimal number of clusters. This quiz covers key concepts from Lecture Five of Machine Learning at Western Sydney University.
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