Podcast
Questions and Answers
What is the main challenge in finding the optimal coefficients for a logistic regression model?
What is the main challenge in finding the optimal coefficients for a logistic regression model?
- The choice of solver
- The infinite number of possible combinations (correct)
- The complexity of the model
- The lack of data
What is the purpose of gradient descent in logistic regression?
What is the purpose of gradient descent in logistic regression?
- To visualize the data
- To find the optimal coefficients (correct)
- To predict the class probabilities
- To evaluate the model performance
What is the formula for the partial derivative of the loss function with respect to a parameter θj?
What is the formula for the partial derivative of the loss function with respect to a parameter θj?
- ∂/∂θj J(θ) = 1/m * Σ [xj - y(i)]
- ∂/∂θj J(θ) = 1/m * Σ [σ(θT * x) - y(i)] (correct)
- ∂/∂θj J(θ) = 1/m * Σ [xj * σ(θT * x)]
- ∂/∂θj J(θ) = 1/m * Σ [y(i) - σ(θT * x)]
What is the purpose of the batch gradient descent algorithm?
What is the purpose of the batch gradient descent algorithm?
What is the main difference between stochastic gradient descent and mini-batch gradient descent?
What is the main difference between stochastic gradient descent and mini-batch gradient descent?
What is the purpose of the logistic regression model in the iris example?
What is the purpose of the logistic regression model in the iris example?
What is the output of the logistic regression model in the iris example?
What is the output of the logistic regression model in the iris example?
What is the purpose of the plot in the iris example?
What is the purpose of the plot in the iris example?
How is the class predicted in the logistic regression model?
How is the class predicted in the logistic regression model?
What is the advantage of using logistic regression over linear regression?
What is the advantage of using logistic regression over linear regression?
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