Show that in every stable matching, m₁, m₂ are matched to w₁, w₁, i.e., (m₁,w₁), (m₂, w₂) or (m₁,w₂), (m₂, w₁) must be part of any stable matching.
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Given
m1, m2 be two of the men, and w1, w2 be two of the women
The preference lists of m1, m2, w1, w2 are:
m₁'s preference: w1> w2 >...
m2's preference: w2> w1 >...
w1's preference: m2> m1 >...
w2's preference: m1> m2 >...
Step by step
Solved in 2 steps
- You are given a dataset of continuous numerical features with a categorical target class. There is not any missing data or outliers. Which algorithm would you use to create a model? K-means clustering Logistic regression Decision Tree Linear regressionWhat is the mean of Linear Correlation ? if r = 0 has strong linear correlation it does mean has strong linear relationship?A. Suppose that every random variable in the joint distribution of P(A,B,C,D,E) = P(E|C,D)P(D|C)P(C|A,B)P(B|A)P(A). has a domain containing 10 elements. How many rows are needed to list the full joint distribution in an explicit table? B. Suppose that every random variable in the joint distribution of P(A,B,C,D,E) = P(E|C,D)P(D|C)P(C|A,B)P(B|A)P(A). has a domain containing 10 elements. How many rows in total are needed to list the conditional probability tables for your belief network representation?
- 4 the task is to estimate two models1. Cobduglus (after taking log to convert it into log-linear)2. Estimate the linear model without logWe want to train a binary Logistic Regression classifier for the given trainingdataset:Suppose two relationsr and s have histograms on attributes r.A and s.A, respectively, but with different ranges. Suggest how to use the histograms to estimate the size of r ⋈ s. Hint: Split the ranges of each histogram further.
- Estimate John’s weight if you gave: A) The degree of membership for each fuzzy fact: • John is tall (degree 0.5). • John is of medium height (degree 0.7). • John is short (degree 0.1) • John is well-built (degree 0.3). • John is weak (degree 0.2). Where fuzzy membership functions are defined in terms of numerical values of an underlying crisp attribute. (For example: Short, Medium and Tall in terms of height. Weak and Well-built in terms of muscle mass)Consider the dataset in terms of attribute-value pairs where F is the value, and A.B, C are attributes. What is the entropy of the dataset Compute the information gain with respect to the attributes A, B and C. Which attribute would be used at the root of the decision tree constructed by the ID3 algorithm?Find maximum Matching in the graph given below, where Dotted lines are given initial matchings
- Let c1, c2, and c3 be the confidence values of the rules {p}->{q}, {p}->{q, r}, and {p, r}->{q}, respectively. If we assume that c1, c2, and c3 have different values, what are the possible relationships that may exist among c1, c2, and c3? Which rule has the lowest confidence?Let X and Y be sets of attributes. Show that if X ⊂ Y , then X+ ⊂ Y + , where the closures are taken with respect to the same set of FD’s.Using Armstrong's axioms plus union and decomposition, prove the following: Given attribute sets A, B, C, and D, if A→D and C→B, B→A and D→C then B,D→A,C.