Refer to the given diagram, which shows consumption schedules for economies A and B. We can say that the
Why doesn’t the Fed want to drive nominal interest rates bel…
Why doesn’t the Fed want to drive nominal interest rates below zero in response to a financial crisis and recession?
In an economy, the government wants to increase aggregate de…
In an economy, the government wants to increase aggregate demand by $50 billion at each price level to increase real GDP and reduce unemployment. If the MPS is 0.4, then it could increase government spending by
The multiplier is
The multiplier is
With a marginal propensity to save of 0.4, the marginal prop…
With a marginal propensity to save of 0.4, the marginal propensity to consume will be
Which of the following fiscal policy changes would be the mo…
Which of the following fiscal policy changes would be the most contractionary?
True or False: Patients can be court-orders (given Jarvis Me…
True or False: Patients can be court-orders (given Jarvis Medication Orders) for Electroconvulsive Therapy (ECT).
Decision Tree (DF) for classification selects a feature and…
Decision Tree (DF) for classification selects a feature and threshold to split feature space in each node that minimize impurity measures. Please select the two impurity measures of DT for classification:_____________
Q27. Bonus Question (5 points) The above figure is for the…
Q27. Bonus Question (5 points) The above figure is for the gradient boosting algorithm for regression. Step 1. A new decision tree (DT) is trained with feature X and label r (i.e., residual) to predict the residual. Step 2. The predicted residual in Step 1 is multiplied by the learning rate and is added to the prior predicted The learning rate is between 0 and 1 for slow learning to avoid overfitting. Step 3. The residual is updated by subtracting the new DT in Step 1 multiplied by the learning rate. Step 4. The final predicted Y in the gradient boosting is the additive function of DTs multiplied by the learning rate in each stage. Overall, gradient boosting is a (1) _____________ (a. parallel learning, b. sequential learning; 2 points). In addition, a new decision tree in each stage is created based on the information from the prior trees to improve performance. Based on the algorithm, which one is not a hyperparameter for gradient boosting? (2)_________ (3 points) the number of trees the maximum depth of each tree learning rate dropout rate the number of splits in each tree
Which one is not true about the random forest (RF) model?
Which one is not true about the random forest (RF) model?