Please explain in detail the 3X3 forecasting model, 2X2 forecasting model, and Receiver Operator Characteristic (ROC) curve.
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Please explain in detail the 3X3 forecasting model, 2X2 forecasting model, and Receiver Operator Characteristic (ROC) curve.
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- Forecasting models extrapolate future values of a time series based on its historical values, allowing you to attempt to predict the evolution of a measure. Which mathematical model does Tableau's forecasting tool use? Exponential Smoothing Simple Moving Average Weighted Moving AverageConsider all 40 observations on the delivery time data. Delete 10% (4) of the observations at random. Fit a model to the remaining 36 observations, predict the four deleted values, and calculate R^2 for prediction. Repeat these calculations 100 times. Calculate the-average R^2 for prediction. What information does this convey about the predictive capability of the model? How does the average of the 100R^2 for prediction values compare to R^2 for prediction based on PRESS for all 40 observations?Alert dont submit AI generated answer.
- An analyst must decide between two different forecasting techniques for weekly sales of roller blades: a linear trend equation and the naive approach. The linear trend equation is F = 124 + 2.1t, and it was developed using data from periods 1 through 10. Based on data for periods 11 through 20 as shown in the table, which of these two methods has the greater accuracy if MAD and MSE are used? (Round your intermediate calculations and final answers to 2 decimal places.) t Units Sold 11 144 146 152 14 142 15 152 16 149 17 152 18 154 19 157 20 164 Click here for the Excel Data File MAD (Naive) MAD (Linear) MSE (Naive) MSE (Linear) N M S 1O H d d H d d - dTake the first 1000 entries of the training portion of the Scikit-learn 20 Newsgroups dataset (in the original order) as the training set, and set aside the next 100 entries as the test set. Predict whether a post belongs to a political discussion group using a bag-of-words model (category includes 'talk.politics'). Provide the accuracy on the test set, the input shape of the network, and the predictions of the network for the last two entries in the test set as "accuracy on the test set, network input shape, network predictions for the last two entries in the test set as 'politics' or 'non-politics'". Here's what I have so far: from sklearn.datasets import fetch_20newsgroupsfrom sklearn.feature_extraction.text import CountVectorizerimport numpy as npfrom keras.models import Sequentialfrom keras.layers import Dense, Dropoutfrom keras.optimizers import Adam categories = ['talk.politics.guns', 'talk.politics.mideast', 'talk.politics.misc']newsgroups =…Consider the following table that contains the IDs of 12 participants and their crash- avoidance reaction times (in milliseconds) on four tests. Two tests were done during the day in daylight and two tests were done at night with reduced lighting. Crash-avoidance Reaction Time (ms) Night Test 3 Participant Day ID Test 2 Test 1 Test 4 9001 887 838 765 648 9002 680 491 953 767 9003 662 553 985 696 9004 789 526 581 770 9005 508 451 688 714 9006 566 633 856 642 9007 656 747 846 718 9008 776 491 944 604 9009 770 672 814 617 9010 333 432 591 602 9011 730 593 796 700 9012 496 404 846 892 This question has multiple parts. Create output to MATLAB's command window exactly as shown, except replacing xxxx with actual values, as instructed. You may use the disp command, but not the fprintf command If you have difficulty with the alignment or formatting of numeric output, add the command format shortg near the top of your solution. In your solution, put the following MATLAB statements at the…
- explain the value of a data model in the context of a standard strength prediction.consider the following model: y = b_0+ b_1*x what is the parameter b_0? O a. the slope coefficient. O b. O C. O d. The independent variable. the dependent variable. the intercept.The Frequentist approach looks at Question 17 options: the long-term relative frequency of events occurring both single events occurring and the long-term relative frequency of events occurring a single event occurring neither single events occurring nor the long-term relative frequency of events occurring For data that is approximately normally distributed, any observation more than 1 standard deviation away from the mean is an outlier. Question 26 options: True False
- Approximately what percentage of people would have scores lower than an individual with a z-score of 1.65 in a normally distributed population? Approximately 95% 50% Approximately 5% Not possible to calculate (mean and standard deviation are not given)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…The predictive performance of a model is the measure of how close the model’s prediction values are to the actual values. A close-to-ideal model would have the minimum error in the predicted and actual values. The validation set is used to assess the predictive ability of the model which has been trained using the training set. True False