- The overall entropy is (round to 2 decimals): - The information gain for attribute A is (round to 2 decimals): - The information gain for attribute B is (round to 2 decimals): -The information gain for attribute C is (round to 2 decimals):
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- The overall entropy is (round to 2 decimals):
- The information gain for attribute A is (round to 2 decimals):
- The information gain for attribute B is (round to 2 decimals):
-The information gain for attribute C is (round to 2 decimals):
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- Database Question:In this problem, you will come up with decision trees to predict if a planet is habitable based only on features observed by the telescope.In the table below, you are given the data from all 800 planets surveyed so far. The features observed by telescope are Size (“Big” or “Small”), and Orbit (“Near” or “Far”). Each row indicates the values of the features and habitability, and how many times that set of values was observed. So, for example, there were 20 “Big” planets “Near” their star that were habitable.Determine if Planet=Big and Orbit=Far is habitable? Derive and draw the decision tree learned by ID3 on this data.Complete the docstring below using the helper function and information attached: def get_last_syllable(word_phonemes: PHONEMES) -> PHONEMES: """Return the last syllable from word_phonemes. The last syllable in word_phonemes is formed from the last vowel phoneme and any subsequent consonant phoneme(s) in word_phonemes, in the same order as they appear in word_phonemes. >>> get_last_syllable(('AE1', 'B', 'S', 'IH0', 'N', 'TH')) ('IH0', 'N', 'TH') >>> get_last_syllable(('IH0', 'N')) ('IH0', 'N') >>> get_last_syllable(('B', 'S')) () """Problem set4.
- Hef sharks_minnows (minnows, sharks): shark_count = 0 minnow_count = len (minnows) for i in range(minnow_count): curr_shark_height = minnows [i] if curr_shark_height is not None: minnows [i] = None for j in range (i + 1, minnow_count): if minnows [j] == curr_shark_height: minnows [j] = None curr_shark_height -- 1 shark_count += 1 return shark_count <= sharks The provided code is imperfect, in that it sometimes returns True when it should return False, and sometimes returns False when it should return True. (a) Provide an example of a function call where the provided code will correctly return True (i.e. a True Positive) (b) Provide an example of a function call where the provided code will correctly return False (i.e. a True Negative) (c) Provide an example of a function call where the provided code will incorrectly return True (i.e. a False Positive) (d) Provide an example of a function call where the provided code will incorrectly return False (i.e. a False Negative)Three datasets have been used to make the boxplots and histograms. Match each boxplot to the appropriate histogram. A A B n 1 B [Choose] [Choose] [Choose ] 614 ThDomino cycledef domino_cycle(tiles):A single domino tile is represented as a two-tuple of its pip values, such as (2,5) or (6,6). This function should determine whether the given list of tiles forms a cycle so that each tile in the list ends with the exact same pip value that its successor tile starts with, the successor of the last tile being the first tile of the list since this is supposed to be a cycle instead of a chain. Return True if the given list of domino tiles form such a cycle, and False otherwise. tiles Expected result [(3, 5), (5, 2), (2, 3)] True [(4, 4)] True [] True [(2, 6)] False [(5, 2), (2, 3), (4, 5)] False [(4, 3), (3, 1)] False
- Q5) Run the code below to check on the model behavior for different polynomials (2,3, 5,10,20). Comment on the generated figure. In [26]: # Hyperparam initialization eta = 0.25 epochs = 500000 # This will take a while but you can set it to 10000. Poly_degree_values = [2, 3, 5, 10, 20] # Initializing plot plt.title( "Regression Lines for Different polynomials") plt.scatter(data.GranulesDiameter, data.Beachslope, label='Traning Data') # iterating over different alpha values (This is going to take a while) for i in Poly_degree_values: polynomial x = GeneratePolynomialFeatures (X, i) thetaInit - np.zeros( (polynomial_x.shape[1],1)) theta, losses = gradientDescent (polynomial_x, Y, thetaInit, eta, epochs) poly = PolynomialFeatures (i) plot SimpleNonlinearRegression line (theta, X, poly) plt.legend(Poly_degree_values) plt.show() Regression Lines for Different polynomials 2 25 10 20 20 15 10 0.2 0.3 0.4 0.5 0.6 0.7 0.8 In [271: # Write your response herefunction [ ] = square_spectrum( L,N )%Activity 1 for CEN415 Summer 2022 x=linspace(0,2*L,200);f1=(-1).^floor(x/L);plot(x,f1)f2=0;for n=1:2:N, f2=f2+4*sin(n*pi.*x/L)/n/pi;endhold onplot(x,f2)hold offend Q1= Which one is true? a. When the N increases, the approximated graph tends to be closer to the original square wave b. When the L increases, the approximated graph tends to be closer to the original square wave c. When the L decreases, the approximated graph tends to be closer to the original square wave d. When the N decreases, the approximated graph tends to be closer to the original square wave e. NoneYour Tasks ● Read the description thoroughly and carefully to understand exactly what IsolateTargetSoloAsTail is meant to do and the IMPORTANT requirements you must meet when implementing the function. ● Fill in the prototype for IsolateTargetSoloAsTail in the supplied header file (llcpInt.h). ● Fill in the definition for IsolateTargetSoloAsTail in the supplied implementation file (llcpImp.cpp). ●If node target cannot be found on the given list, a new node containing target is created and added to the end (made the new tail node) of the list. ●If target appears only once on the given list, the target-matching node is moved to the end (made the new tail node) of the list. If target appears multiple times on the given list, the first target-matching node is moved to the end (made the new tail node) of the list, and all other target-matching nodes are to be deleted from the list. The order in which non-target-matching nodes originally appear in the given…
- function [ ] = square_spectrum( L,N )%Activity 1 for CEN415 Summer 2022 x=linspace(0,2*L,200);f1=(-1).^floor(x/L);plot(x,f1)f2=0;for n=1:2:N, f2=f2+4*sin(n*pi.*x/L)/n/pi;endhold onplot(x,f2)hold offend Q1= In which case the bandwidth decreases? a. When N decreases b. When L increases c. When L decreases d. When N increases e. Nonefunction [ ] = square_spectrum( L,N )%Activity 1 for CEN415 Summer 2022 x=linspace(0,2*L,200);f1=(-1).^floor(x/L);plot(x,f1)f2=0;for n=1:2:N, f2=f2+4*sin(n*pi.*x/L)/n/pi;endhold onplot(x,f2)hold offend 2-Run the function for L=3 and N=3 and upload the image of the graph plotted use matlap1. An enumeration type is a set of ordered values. True False