The Loss Function Of Compile And Fit The Network

We conduct an independent sample t-test using Excel, and obtain the following output (see t-test-height)

·  Compare the measurements in the study with the standard normal distribution, what does this tell you about the data?

Statistical results of the data analysis have been received by using the Gauss curve, as preferred distribution function, and the

 Explain how the data collected will provide the data necessary to support or negate the hypothesis or proposition

-The shape of both data sets fall within the rule of thumb estimate and that actual standard deviation. There is a much high range for both and that is because there are several data samples that fall outside of the standard deviation.

‘Validation Partition- This partition (sometimes called the test partition) is used to assess the performance of each model so that you can

The standard Deviation comparing both sets of data only shows a slight difference on the numbers (a difference of only 1.38187054), but in the histograms both show a huge difference on the frequencies of each data set, but shows a similarity on the cumulative percentage. Data set #2 seem to be more stable even though the grades of the student were lower than data set #1. Data set#2 shows a relative stability in regards the frequency on the histograms, when comparing the frequency histograms data set #2 has more frequency on number two’s than data set #1 on frequency on the number three’s. In this case Data set #2 shows a normal distribution behavior.

Criterion 3: 푆푝푒푐푖푓푖푐푖푡y is considered as negative integer value. 푆푝푒푐푖푓푖푐i푡y indicates the model’s ability for recognizing natural examples. If this criterion and sensitivity increase and the difference is less than 1%, the best classifier is provided. The criterion’s equation is:

As the author continuously integrates

In other words, both the training and test data belong to the same population as the

evaluated at θ = θ ̂ and w = w0 = I, i = 1, …, q, j = 1, …, q

The variance of the residual term should be constant. The assumption of Homoscedasticity was assessed by the plot of standardized residuals against standardized predicted values according to the recommendations of Field (2005). As we have seen in figure3 it can be assured that the point are random and evenly throughout the scattered diagram and no evidence of funnel like shape of points on one side than the other is observed, so no hetero scedasticity in the data is confirmed.

on a test set with as little labeling effort as possible. A trained classifier is

Consider (x(i,j)-x(i+1,j))^2 and (x(i,j)-x(i,j+1))^2 are the convolution between gx = [1, -1] and x, gy = [1, -1] T and x, respectively. Accordingly, the P(x) can be expressed as:

Rearranging the terms in formula (4), following Färe et al. [41], we obtain the subsequent formula: