In a multivariate model (ROC analysis), the area under the curve is 0.705 , explain how this is statistically significant. Images of the predictiv
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In a multivariate model (ROC analysis), the area under the curve is 0.705 , explain how this is statistically significant.
Images of the predictive power graph is attached. Please help explain the results.
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- 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…In Reid and Jamieson (2023) the authors had to use a low learning rate in their Minerva 2 model to successfully account for DRM effects. Describe what the learning rate in Minerva 2 does . Using the results from the above simulation describe why you believe it is necessary to use a low learning rate when accounting for DRM results using distributional models .Why Naïve Bayes is said to have high Bias, and low Variance
- The non-parametric density-based approach assumes that the density around a normal data observation within a cluster (relatively big) is similar to the density around its neighbours, and the density around an outlier (relatively small) is considerably different to the density around its neighbours. If we had the density of observation within a cluster smaller than the density around an outlier, explain why we would have such a situation. And provide a solution to this problem.give the steps by steps answerYou are developing a simulation model of a service system and are trying to create aninput model of the customer arrival Process, You have the following four observations of the process of interest [86, 24,9, 50] and you are considering either an exponential distributionOf a uniform distribution for the model. Using the data to estimate any necessary distributionParameters, write the steps to plot Q-Q plots for both cases.
- 2. Can you design a binary classification experiment with 100 total population (TP+TN+FP+ FN), with precision (TP/(TP+FP)) of 1/2, with sensitivity (TP/(TP+FN)) of 2/3, and specificity (TN/(FP+TN)) of 3/5? (Please consider the population to consist of 100 individuals.)Draw a QQ (quantile-quantile) plot for the built-in data set, islands, to assess the normality of the observations. Is the data set well-modeled by a normal distribution?