the tow image below for tow solution of same question the first image explained and no calculations SSE AND RELATIVE ERROR the second image not full solution and wrong solution he used some times filter value -1. please my Q/ for 3rd time IS write the full CALCULATION AND EXPLAIN WHAT IS STANDARD ERROR MENTIONED AND WHY YOU USE N=15 AND IT IS 16 FROM SUM OF 1 VALUE FILTER to see and check how you got results. the table is for Y(DEPENDENT)(rut) and ypredicted for MLP ANN

rutting	filter	_PrdictdV_
8.00	1	7.23
3.40	1	3.37
7.00	-1	6.33
6.80	0	6.38
6.80	1	6.72
3.70	1	3.64
6.00	-1	5.56
3.60	0	3.73
5.80	-1	5.23
3.70	1	3.63
5.70	1	5.60
3.80	1	3.73
4.20	1	4.18
7.20	0	7.23
5.00	1	5.10
4.30	-1	4.17
4.20	1	4.11
5.50	0	5.30
4.80	0	4.82
4.90	-1	4.79
4.80	1	4.61
4.40	0	4.29
4.10	-1	4.31
6.00	1	6.50
4.50	1	4.39
4.20	1	4.31
6.10	1	6.38
5.40	0	5.96
5.00	-1	5.34
5.20	0	5.22
4.75	-1	5.38
5.90	1	6.33

THE OUTPUT OF ANN MLP

BELOW HOW WE GET RESULTS PLEASE

Training	SSE	.360
	Relative Error	.048
Testing	SSE	.166
	Relative Error	.057
Holdout	Relative Error	.237

This solution was written by a subject matter expert. It's designed to help students like you learn core concepts. Step-by-step Step 1/1 Sum of Squares Error SSE \( =\sum(\text { Observed_Value - Predicted_Value })^{2} \) SSE =(88.00)2+(7.231)2+(3.40 3.37)2+(77.00)2+(6.33(1))2+(6.806.38)2+(3.706.72)2+(33.64)2+(6 Final answer 5.56)2+(3.603.73)2+(5.805.5.22)2+(4.755.38)2+(5.906.33)2SSE=0.364 Relative Error Relative Error = SSE/N Relative Error =0.364/15 Relative Error =0.048 Was this answer helpful? The sum of squares error is a statistic that can be used to quantify the amount of error that is associated with a model (SSE). Calculating the sum of the squared differences that exist between the values that were actually observed and the values that were predicted is the method that is utilized in order to ascertain its value. Explanation To calculate the relative error, take the standard statistical error and divide it by the total sum of squares used in the calculation. This will give you the relative error. This provides a metric that can be utilized in order to evaluate how accurately the model is predicting the data. [Case in point:] Before we can calculate the SSE and relative error for each filter group, we need to first group the data according to the filter values, which are 1,0 , and 1 for training, testing, and validation, respectively. This will allow us to calculate the SSE and relative error for each filter group. After that, we will finally be able to move on to calculating the SSE as well as the relative error for each filter group. Calculating the standard statistical error (SSE) as well as the relative error for each group is required of us. In order to calculate the SSE, we need to add up the squared differences that exist between each group's actual values and their predicted values. These squared differences can be found by comparing each group's actual values to their predicted values. After that, the relative error is calculated by dividing the SSE by the total sum of squares for each group. This gives the SSE as a percentage of the total sum of squares. The result of this calculation is the relative error percentage