Simulate a binary classification dataset with a single feature using a mixture of normal distributions with NumPy (Hint: Generate two data frames with the random number and a class label, and combine them together). The normal distribution parameters (np. random. normal) should be (5,2) and (-5,2) for the pair of samples. Induce a binary Decision Tree of maximum depth 2, and obtain the threshold value for the feature in the first split. How does this value compare to the empirical distribution of the feature

In a heat transfer course, we can derive the equation for the temperature distribution in a flat rectangular plate. in this example, we will look at a plate at steadystate with three sides being held at t1, and one side held at t2. the temperature for any location on the plate, t(x,y), can be calculated by where create a function (prob3_5) that will take inputs of vectors x and y in feet, scalar n, scalars l and w in feet and scalars t1 and t2 in degrees fahrenheit. it will output a matrix t which is the temperature of each x and y locations. t will have the number of columns equal to the number of elements in x and rows equal to the number of elements in y. though this can be done without loops (perhaps more efficiently), your program must use a nested loop.