need to print only the column on the left, how do I do this?
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I need to print only the column on the left, how do I do this?
So the output will be
4.0
5.0
6.0
7.0
8.0
9.0
10.0
11.0 etc
Step by step
Solved in 2 steps
- Evaluate the performance of a Fisher Discriminant Function using the following output. Compute the rate for total missclassification and state your conclusion. Classification Results Predicted Group Membership Malignant 190 Benign 22 Diagnosis Malignant Benign Malignant Benign Malignant Benign Malignant Benign Total 212 357 Original Count 12 345 89.6 10.4 100.0 3.4 96.6 100.0 Cross- Count 189 23 212 validated 342 357 100.0 15 % 89.2 10.8 4.2 95.8 100.0In python, for a sample data with 4 columns and 60 rows how do you find the parameters for the regression with the feature map (see attached) where we consider the loss function to be the square of residuals. Once this is done, how do you compute the empirical risk? I've attached some of the data below, it would be sufficient to see how you get results for the question using the above dataset. 1 14 25 620 -1 69 29 625 0 83 27 850 0 28 25 1315 1 41 25 2120 -1 153 31 1315 0 55 25 2600 0 55 31 490 1 69 25 3110 1 83 25 35351 2 3 95 108 110 126 118 102 124 121 145 118 140 155 185 158 8 190 178 9 205 159 10 222 184 The table shows scores of 10 students in Java programming and Data Science The estimated Spearman's rank-order coefficient of correlation p is: A. r=0.842 B. r = -0.842 C. r= 0.158 D. r=-0.158 5 Student 6 Java Programming Data Science
- Electronic Spreadsheet Applications Compare What-If Analysis using Trial and Error and Goal Seek to the given scenario: Let's say a student is enrolled in an online class at a learning institution for a semester. His overall average grade stands at 43% in the course (Term Grade is 45%, Midterm Grade is 65%, Class Participation is 62% and Final Exam is 0%). Unfortunately, he missed his Final Exam and was given 0%. However, he has the opportunity to redo his Final Exam and needs at least an overall average of 60% to pass the course. How can you use Trial and Error and Goal Seek to find out what is the lowest grade he needs on the Final Exam to pass the class? Which method worked best for you and why?Answer the following questions given the following recurrence relations: 1. T; (n) = 2 x T, (4) + n 2. T, (п) — 4 x Т, + n? 3. T3 (п) —D 8 х T; (" +n 4. TА(п) %3D 8 х Т, (3) + 1 And T, (n) = T2(n) = T3 (n) = T4(n) = 0(1) A. Order the respective time complexities from FASTEST to SLOWEST B. Assuming the same input n, arrange the recurrence relations based on the expected recursion depth from DEEPEST to SHALLOWEST. C. Assuming that each node in their respective recursion trees consume the same amount of constant space, which recurrence relation/s has/have the worst worst-case space complexity (0(M(n)))? Would this/these also require the most amount of space or program stack memory?Sum of Squared Errors: Remember from your statistics courses that if two random variables X and Y are related by a relation YaX+b and you had a set of observations {(1, 1), (2, 2)..... (z.)). then for every estimated values of a and b, the Sum of Squared Errors (SSE) was defined as in the following formula. SSE(-ar-b)². 1-1 The less SSE, the better a and b are estimated. Consider the following fixed list. In this list each sub-list of length two is standing for one pair (z.). Write a function that recieves two numbers a and b and returns the associated SSE. L- [[1, 2], [1.1, 2], [2, 7.1), (2.5, 7.21, (3, 11]]
- : Employee Graph = (V,E) V(EmployeeGraph) = { Susan, Darlene, Mike, Fred, John, Sander, Lance, Jean, Brent, Fran} E(EmployeeGraph) = {(Susan, Darlene), (Fred, Brent), (Sander, Susan),(Lance, Fran), (Sander, Fran), (Fran, John), (Lance, Jean), (Jean, Susan), (Mike, Darlene)} Draw the picture of the Employee Graph.cements Suppose we use Adaboosting with linear classifiers to classify between red and green ents points, and in iteration 1, the classifier chosen is as shown in the Figure (the decision boundary is indicated by dotted line and the bold text indicates the label assigned to either ons side of the boundary). Which of (a), (b) or (c) is most likely to be the next classifher chasen in the ensemble. (Note: no numerical computations needed to answer this) RED teration 1 RED GREEN RED GREEN ons RED GREEN (b) (a)Given: e-NFA M2 = ({A,B,C}, {0,1}, delta, A, {C}) where delta: (A,0) -> {C}, (A,1) -> {B}, (A,e) -> null, (B,0) -> {A}, (B,1) -> null, (B,e) -> {C}, (C,0) -> null, (C,1) -> {A}, (C,e) -> null Get the equivalent DFA of the given e-NFA. How many 3-symbol strings are accepted in the given e-NFA M2 or in the equivalent DFA?
- Consider fitting a linear regression he(x) = 0Tx = 01x1+ 02x2+ O3x3 to a training dataset D = {(x1,y1),..., (xn, Yn)}, where 0 = [01, 02, 03]T and x = [#1, x2, 83]T Q1.1 Let D = {([1,0, 0]",0), ([0, 1, 0]" , 0.5), ([0, 0, 1]", – 1)}. Our current estimate of the parameter 0 = [0, 0, 0]T. which training instance makes the smallest contribution to the gradient ? (contribution means the magnitude of gradient change) O 1 O 2 O 3 Q1.2. What is the updated parameter after one step of gradient descent (assuming step size = 1)? O [0, 0, 0]T O (0, 1, 0]" О (), 0.5, — 1]7 O [0, 0.5, 1]"Given: e-NFA M2 = ({A,B,C}, {0,1}, delta, A, {C}) where delta: (A,0) -> {C}, (A,1) -> {B}, (A,e) -> null, (B,0) -> {A}, (B,1) -> null, (B,e) -> {C}, (C,0) -> null, (C,1) -> {A}, (C,e) -> null. Get the equivalent DFA of the given e-NFA. How many accepting states in the equivalent DFA?Imagine I want to estimate the following regression in Stata TestScorei = . Bo B₁ Height; + ɛi What code would correctly estimate this regression ols height test_score ols test_score height reg test_score height reg height test_score