p1_assess_learners_report

: |: 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…

Data may be subdivided into smaller groups using decision trees, which do this by dividing the data into two or more categories at each node. There may be unintended consequences to this method if the decision tree is not trimmed. Explain the process of pruning, when it happens, and why it's beneficial to do it.

Discretized values in a decision tree may be combined into a single branch if order is not preserved. True False

The accuracy on an unpruned decision tree is 40% on the test data, while that on the training data is close to 60%. Write the accuracy expression of this using 0.632 bootstrap?

Discretized values in a decision tree may be combined into a single branch if order is not preserved. Having trouble understanding why this is true ?

Search in a tree Given the following search tree with root node S and goal state G. The states of the nodes and their heuristic values (h-value) are shown. The step costs are shown along the edges. Tie breaking is based on the alphabetical order of the node's state name, i.e., in the order of A, B, C..... D:4 2 1 6:0 A:2 1 M.S 1:6 C3 3 1 E:7 5:4 H8 89 fav F:4 K-2 11 Is the heuristic admissible? Yes No

Discretized values in a decision tree may be combined into a single branch, if: Group of answer choices, can choose multiple: order is not preserved data is continuous data has wide gaps order is preserved.

The goal of regularization techniques such as L2-norm or limiting the depth of the tree is to lower the bias of the learning algorithms. Select: True or False?

A decision tree with better splits means that: a. all the branches have binary splits. b. the tree has higher accuracy in prediction. c. the branches are of equal depth. d. the tree has more branches.

Explain what random forests are and how they differ from decision trees in a single sentence or a few lines of text. To make sure we randomize twice while utilizing the random forest technique, how do we do it?

4. The training error of a decision tree with ONE leaf node is 10/40 and 40 is the total number of training samples. What is the pessimistic error of this decision tree: ? After the splitting of the leaf node, there are four leaf nodes, and the training error becomes 9/40. Then what is the pessimistic error of the new decision tree: ? Should you prune the sub-tree after the splitting (yes/no): ?

» Class problem : Sketch the decision tree model for comparison sorting on a list of four elements (a,, a2, a3, a4). Find the tree maximum height, the tree minimum height, and the number of leaves.

Decision trees split data into two or more categories at each branch. This method may backfire if the decision tree is not trimmed. Prune when and why.

Draw the decision tree for the binary search algorithm for a list of 13 elements. For the internal nodes of your decision tree, the node should be labeled with the element checked, the left child should represent what happens if the target is less than the element checked, and the right child should represent what happens if the target is greater than the element checked.

Can random forests be used to fix decision trees that tend to have high bias and low variance?

Briefly explain the differences and similarities between random forest and decision trees. How do we randomize twice when implementing the random forest algorithm?

Describe in as few as one or as many as a few words as possible the differences between a decision tree and a random forest. In utilising the random forest technique, what are some ways that we may ensure that randomization will occur twice?

Correct answer will be upvoted else Multiple Downvoted. Computer science. Alice and Bob are playing a game. They have a tree comprising of n vertices. At first, Bob has k chips, the I-th chip is situated in the vertex computer based intelligence (every one of these vertices are extraordinary). Prior to the game beginnings, Alice will put a chip into one of the vertices of the tree.    The game comprises of turns. Each turn, the accompanying occasions occur (consecutively, precisely in the accompanying request):    Alice either moves her chip to a neighboring vertex or doesn't move it;    for each Bob's chip, he either moves it to a neighboring vertex or doesn't move it. Note that this decision is done freely for each chip.    The game closures when Alice's chip has a similar vertex with one (or numerous) of Bob's chips. Note that Bob's chips might have a similar vertex, despite the fact that they are in various vertices toward the start of the game.    Alice needs to augment the number…

Explain what a random forest is and how it differs from a decision tree in one or two sentences. How can we be sure to randomize twice when using the random forest algorithm?

Correct answer will be upvoted else Multiple Downvoted. Don't submit random answer. Computer science. You are given a tree — associated undirected chart without cycles. One vertex of the tree is exceptional, and you need to track down which one. You can pose inquiries in the accompanying structure: given an edge of the tree, which endpoint is nearer to the uncommon vertex, which means which endpoint's most brief way to the extraordinary vertex contains less edges. You need to track down the uncommon vertex by posing the base number of inquiries in the most pessimistic scenario for a given tree.    If it's not too much trouble, note that the exceptional vertex probably won't be fixed by the interactor ahead of time: it may change the vertex to some other one, with the prerequisite of being steady with the recently offered responses.    Input    You are given an integer n (2≤n≤100) — the number of vertices in a tree.    The folloiwing n−1 lines contain two integers every, u and v…

under machine learning decision tree, please help, thank you!

Binary classifications, classification error, partitioning attribute values

Q1 Classification The classification tree was trained with response data Y and using two explanatory variables X1, X2. Yes X20.310283 X10.606056 No X2 0.697338 Yes Yes ## A) Sketch the classification implied by the tree in the region [0,1] 2. Use "Yes" /1 and "No"/0. B) A new set of points has been provided with fresh response values, as follows. x1 x2 Y ## [1,] 0.25 0.33 1 ## [2,] 0.50 0.33 0 ## [3,] 0.75 0.33 0 ## [4,] 0.25 0.67 0 ## [5,] 0.50 0.67 0 ## [6,] 0.75 0.67 1 Using the data and the trained tree, give predicted (0/1) labels for the new points. C) Report the confusion matrix and performance measures TPR=| on the performance of this classifier. and FPR= Briefly comment D) Can the given trained tree be trimmed to a simpler version, removing redundancies? E) Write a different tree that is equivalent to the given one.