Please find the solution for this.

2. Consider the following training data: X=0.700.650.890.310.600.181.120.520.292.1352.,y=0110151 where X are the inputs, and y is the binary classification vector, where each entry indicates the classification (0 or 1) of the corresponding observation. A logistic regression model is estimated on these data, with the following vector of weights w=(0.5,1.1,0.3) The equation for binary cross entropy is, where the first entry denotes the intercept. E(w)=lsum_{n=1}{N}[y_ilog{(xw)}+(1y_i)log{1(xTw)}} Solve the following 2 questions in R programming - Given the estimated regression weights, compute the average binary cross-entropy loss of this sample. - Using the Maximum a Posteriori rule to classify the observations (i.e. a threshold of 0.5) compute the classification accuracy of the model on this sample