Assume we have generated the following random trees, and we want to train the random forest algorithm toclassify the unseen data. A) What is the class of the unseen data point (0, 1, 0, 1, 0) and (0, 0, 1, 1, 0)? (showyour work.) If you need to break ties, mention how you did that in your work. B) Is the random forest asuitable algorithm for the given data? If not, what do you suggest?x3 = 0X1x3 =]0=x1 = 1X4 = 0X10111110 = xX5 =x4 = 1X5 = 10101x3 = 0x2 x3 X4 X500110101100 1100111110000100Class-111-1-1x2 =x₂ = 10

Random forest is a machine learning algorithm used for classification and regression tasks. It works…

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4. Consider the graph G, shown below, which is simply a copy of K5. 02 V3 5 V1 24 V5 How many distinct spanning trees does G have? (Hint: Break up your search by the isomorphism type of the tree, as discovered on the previous page. So for example, start by counting the paths of length 5 in G. Then proceed to the next type of tree with 5 vertices. The total number of trees is 125, but please use this answer only to check that your solution is complete!)
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: |: Section 3 - Random Forests Decision Trees on their own are effectiev classifiers. The true power, though, comes from a forest of trees -- multiple decision trees working together as an ensemble learner. We create multiple trees, each using a subset of the available attributes, let them each make a guess at the correct classification, and then take the majority vote as the predicted class. Sounds tricky, right? Once again, sklearn to the rescue. We are going to see if a random forest can improve on our decision tree accuracy for the bank data. To begin, we are going to create a Random Forest using the wheat training and test data from the previous section. First, reload our data. 1 # reload the wheat dataset from UCI 2 3 df = pd. read_csv ("seeds_dataset.txt", sep='\\t', engine='python') 4 6 7 df.columns = ['a', 'p', 'compactness', 'length', 'width', 'coeff', 'length_g', 'type'] print (f'Our data has {df.shape [0]} rows and {df.shape [1]} columns') 8 #Mark 70% of the data for…
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