If we have TEN different features a1, a2, a3,a10; from which we want to select three features based on Factor Analysis.After performing factor analysis using the FactorAnalyzer package, suppose we got the following loading matrix.[[ 0.46[-0.12[ 0.03[-0.03 -0.03[ 0.75[ 0.39[ 0.620.120.050.75]0.65]0.13]0.69]0.0010.65]0.02]0.01]0.08]0.02]]0.920.010.110.08[ 0.76 0. 46[ 0.070.88[ 0.02 0.76Find out which features are grouped intoto create Factor2 if the loading cutoff of 0.6 issed.O a. a1, a2, a4, and a6O b. a5, a7, and a8O c.a3, a9, and a10O d. a1, a2, a3, and a7

Factor analysis is one of the unsupervised machine learning algorithms which is used for…

If we have TEN different features a1, a2, a3, ., a10; from which we want to select three features based on Factor Analysis. After performing factor analysis using the FactorAnalyzer package, suppose we got the following loading matrix. [[ 0.46 0.12 (-0.12 0.05 0.75] 0.65] 0.13] 0.69] 0.00] 0.65) 0.02] 0.01] 0.08] 0.02j] ( 0.03 0.92 (-0.03 -0.03 0.01 [ 0.75 ( 0.39 [ 0.62 0.08 [ 0.76 0.46 [ 0.07 0.88 [ 0.02 0.76 0.11 Find out which features are grouped into one to create Factor2 if the loading cutoff of 0.6 is used. O a.a1, a2, a4, and a6 O b. a5, a7, and a8 O c.a3, a9, and a10 O d. a1, a2, a3, and a7

If we have TEN different features a1, a2, a3, ., a10; from which we want to select three features based on Factor Analysis. After performing factor analysis using the FactorAnalyzer package, suppose we got the following loading matrix. [[ 0.46 0.12 (-0.12 0.05 0.75] 0.65] 0.13] 0.69] 0.00] 0.65) 0.02] 0.01] 0.08] 0.02j] ( 0.03 0.92 (-0.03 -0.03 0.01 [ 0.75 ( 0.39 [ 0.62 0.08 [ 0.76 0.46 [ 0.07 0.88 [ 0.02 0.76 0.11 Find out which features are grouped into one to create Factor2 if the loading cutoff of 0.6 is used. O a.a1, a2, a4, and a6 O b. a5, a7, and a8 O c.a3, a9, and a10 O d. a1, a2, a3, and a7

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

See similar textbooks

Similar questions

Both pictures are the same question and can you answer parts A-D
Please help explain this code snippet in the second function, why are we taking transpose. also ax[1] plot is confusing. please explain this clearly and maybe with example
Compute the values: D2-2D1, D3-3D2, D4-4D3, D5-5D4, …, D10-10D9, where the D’s are the derangements values described on P.335. What is the pattern?
M In the 4 Queens above, the value of h (number of conflicts) in each cell are given below. A B C D QA-along column A QB - along column B QA 5 5 3 Solve the 4 Queens Problem above. However, show the details in the solution by indicating the number of conflicts (h) if ever the Queen is move in the assigned column as shown in the example above. That is, the Queens are move along the assigned columns only. MOVE 1 MOVE 2 4 QC 5 QB 4 2 5 3 2 2 5 QC-along column C QD - along column D To solve the above 4 Queens, three (3) moves are needed. For each move, please show the number of conflicts in all cells. The sample is shown above wherein if QA is move to row 1 (bottom at column A) the h = 3 and if QB is move in row 4 (top at column B) the h = 4. Please note h is the number of conflicts. Please complete the three (3) moves below as complete answer. Please note after MOVE 3, h = 0. QD MOVE 3
I have a 70000x785 dataset (70000 instances and 785 features) consisting of 10 different types of classes. I need to find a selection algorithm for finding the optimal training set size N as detailed in the question below. What is an example of a selection algorithm that can do this?
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?
Correct and detailed answer will be upvoted else downvoted. You can pick a component from the current succession and eliminate it, then, at that point, connect the leftover components together. For instance, on the off chance that you eliminate the 3-rd component from the grouping [4,2,3,1], the subsequent arrangement will be [4,2,1]. You need to eliminate a few components from a to augment f(a), utilizing at least zero activities. Track down the biggest conceivable f(a). Input :The principal line contains one integer n (1≤n≤2⋅105) — the underlying length of the arrangement. The subsequent line contains n integers a1,a2,… ,an (1≤ai≤2⋅105) — the underlying arrangement a. Output :Output an integer indicating the biggest f(a) that can be acquired by doing at least zero activities.
You have two genes from two species: one is: X = "CAGUAT" and the other is: Y = "GCACUT". You want to compute the similarity between these two genes to understand the evolutionary relationship among them. Which quantity should you compute to do so? Why? How can you compute this quantity using a matrix/2D array? Show both the cost and the arrows for each cell in that matrix.
Need Help writing this in MATLAB. Given three arrays a=[1 2 3; -1 -2 -3], b=[2;4;6] and c=[0 5 7]. Write one single statement in MATLAB to define a matrix, using a, b, and c, that will contain the following elements: 1 -1 2 2 -2 4 3 -3 6 0 5 7
question 2) what is the difference between correlation and convolution filtering methods? Briefly explain with a (your own) suitable example
• Implement and test: • Logistic regression (LR) with L1 regularization • LR is differentiable • But L1 norm is not • Use proximal gradient descent • For L1 norm, that’s soft-thresholding • Use tensorflow library • Dataset – the same as in HW2: • Classify two digits from MNIST dataset • See: tensorflow_minimizeF.py • Performs projected gradient descent on a simple function • The function has global minimum at • w1=-0.25, w2=2 • But the feasible set Q is: w1>=0, w2>=0 • For this function, the best solution is w1=0, w2=2 • The code does the following, in a loop: • Gradient step on the function, followed up by proximal step • Here, the proximal step is just “make w nonnegative” by replacing negative values with 0, the closest non-negative value • Feasible set Q is set of all vectors with nonnegative coordinates, i.e., for 2D, w1>=0, w2>=0 • In your actual code, you should use soft-thresholding instead • See: tensorflow_leastSquares.py • Performs gradient descent on a function…
Q1 Consider an x-by-y logical containing n number of 0's and m number of 1's. Additionally, consider an x-by-y matrix (double). Which of the following outputs is true if the matrix is indexed with the logical? Select one: a. Output is a m-by-1 double b. Output is a n-by-1 double c. Output is an x-by-y double d. Output is an m-by-n double e. Output is an x-by-y logical Q2 Consider a text file that contains 19 rows of header text followed by 23 rows of numerical data. How many times will the fgetl() function need to be called to import the 9th row of numerical data in the text file? Select one: a. 23 b. 28 c. 14 d. 9 e. 1 Q3 Consider x to be an m-by-n matrix with m > n. Which of the following statements is true regarding y = ones(length(x))? Select one: a. The output of y will be an n-by-n matrix b. The output of y will be an m-by-m matrix c. The output of y will be an n-by-m matrix d. The output of y will be a 1-by-n vector e. The…