What is the best way to decide how many epochs of training to perform? It is always obvious looking at the decision boundary when the model begins to overfit. None of the others. As soon as the value of the Testing dataset performance begins to decrease. As soon as the value of the Tuning dataset performance begins to decrease. As soon as the value of the Training dataset performance (accuracy, F1.) begins to decrease. As soon as the value of the Testing dataset loss begins to increase. As soon as the value of the Tuning dataset loss begins to increase. As soon as the value of the Training dataset loss begins to increase.

Suppose you are working as a data scientist for an online retailer, and you are tasked with optimizing the pricing strategy for a new product. The product is currently priced at $50, but you want to determine whether a higher or lower price would result in greater profit. You estimate that the demand for the product can be modeled using a normal distribution with a mean of 100 units per day and a standard deviation of 20 units per day. The cost of producing each unit is $30, and the retailer's profit is the difference between the revenue and the cost. The retailer incurs a fixed cost of $1000 per day for operating expenses, regardless of the number of units sold. Use simulation to answer to the question.

You decide to run a simpler model to predict churn, using only the variables tenure (in months) and TotalCharges (in US$). The output is given below. The AIC of this model is 4727.6 (in contrast to the AIC of 4240 for the full model). On the basis of this which model would be expected to give superior predictive performance? Actual ## Coefficients: ## Estimate Std. Error z value Pr(>|z|) ## (Intercept) 2.471e-01 5.360e-02 4.611 4.01e-06 *** ## tenure < 2e-16 *** -1.124e-01 5.816e-03 -19.334 ## TotalCharges 8.236e-04 5.618e-05 14.660 < 2e-16 *** ## No --- ## Signif. codes: 0 ## Yes Yes ## Null deviance: 5701.5 on 4921 ## Residual deviance: 4721.6 on 4919 ## AIC: 4727.6 515 345 ## (Dispersion parameter for binomial family taken to be 1) ## Predicted ***** No 795 3267 0.001 Confusion Matrix (Training) **** Actual 0.01 Yes No degrees of freedom degrees of freedom Yes The simpler model (with just tenure and TotalCharges) The full model (with all variables) 0.05 0.1 220 145 Predicted No 339…