2. Suppose that fish come in two classes, salmon (class 1) and sea bass (class 2). We take a picture of a fish and measure its length, x, and wish to make a decision on the identity of the fish based on the value of x. For each of the conditions below Determine the decision regions in x for the Bayes classifier corresponding to the two classes under the following conditions. Find the numerical value of the probability of making a classification error using these decision regions. Note that the area under a Gaussian distribution can be found using qfunc) in MATLAB or scipy.stats.norm.cdf() in python (a) The class conditional densities are Gaussian with the following means, variances and class prior probabilities: m, 17,-|0, m, 25, ,-10 , p(salmon)-0.5,p(sea bass)-0.5 (b) The class conditional densities are Gaussian with the same parameters in (a), but the class prior probabilities are changed to p(salmon) 0.2, p(sea bass) 0.8 (c) The class conditional densities are Gaussian with the following means, variances and class prior probabilities: m 30, 20,m215, 5, p(salmon) 0.5, p(sea bass) 0.5 2. Suppose that fish come in two classes, salmon (class 1) and sea bass (class 2). We take a picture of a fish and measure its length, x, and wish to make a decision on the identity of the fish based on the value of x. For each of the conditions below Determine the decision regions in x for the Bayes classifier corresponding to the two classes under the following conditions. Find the numerical value of the probability of making a classification error using these decision regions. Note that the area under a Gaussian distribution can be found using qfunc) in MATLAB or scipy.stats.norm.cdf() in python (a) The class conditional densities are Gaussian with the following means, variances and class prior probabilities: m, 17,-|0, m, 25, ,-10 , p(salmon)-0.5,p(sea bass)-0.5 (b) The class conditional densities are Gaussian with the same parameters in (a), but the class prior probabilities are changed to p(salmon) 0.2, p(sea bass) 0.8 (c) The class conditional densities are Gaussian with the following means, variances and class prior probabilities: m 30, 20,m215, 5, p(salmon) 0.5, p(sea bass) 0.5