Statistics > QUESTIONS & ANSWERS > AS_ Quiz 3 - PCA_ Advanced Statistics - Great Learning. Graded Quiz. Score 9/10 (All)

AS_ Quiz 3 - PCA_ Advanced Statistics - Great Learning. Graded Quiz. Score 9/10

Document Content and Description Below

AS_ Quiz 3 - PCA_ Advanced Statistics - Great Learning. Graded Quiz. Score 9/10 Go Back to Advanced Statistics Course Content AS: Quiz 3 - PCA Type : Graded Quiz Marks: 9 Q No: 1 Answer Corr ... ect Marks: 1/1 In PCA, the principal components are orthogonal to each other such that they become highly correlated which inturn reduces multicollinearity within the independent variables. True False Orthogonal Components become uncorrelated and reduce multicollinearity 2/8 Q No: 2 70-75% 100% 80-85% 60-65% Marks: 1/1 The first two principal components explain of the information as per the below image: [Cumulatively] Q No: 3 Marks: 1/1 Principal Component Analysis can also be used to deal with the in the data. extreme values multicollinearity missing values outliers abcd bdca bdac Q No: 4 Arrange the following in order of the steps to perform PCA: Marks: 1/1 a) Sort the eigen-pairs in descending order of eigenvalues and select the one with the largest value. This is the first principal component that covers the maximum information from the original data. b)Begins by standardizing the data. Data on all the dimensions are subtracted from their means to shift the data points to the origin. i.e. the data is centered on the origins c)Perform eigen-decomposition, that is, compute eigenvectors which are the principal components, and the corresponding eigenvalues which are the magnitudes of variance captured d)Generate the covariance matrix/correlation matrix for all the dimensions bcad num_reactions num_sads status_id num_loves -0.094957 0.129017 -0.176010 -0.152587 Q No: 5 Marks: 1/1 Which of the variables in the dataset (FB.csv) is not significant for doing Principal Component Analysis? (Note - Please do not preprocess the dataset provided (outlier treatment/null values) unless specified in the question.) Q No: 6 Marks: 1/1 After doing z-score scaling on the dataset(FB.csv), what is the value of the 2nd observation of the variable ‘ num_hahas’? (Note - Please do not preprocess the dataset provided (outlier treatment/null values) unless specified in the question.) Q No: 7 Apply PCA taking all features and extract 6 components and Find out the eigenvector of the 5th component (Use (n_components=6,random_state=123)) (Note - Please do not preprocess the dataset provided (outlier treatment/null values) unless specified in the question.) Q No: 8 After doing z-score scaling on the dataset (FB.csv), What is the eigenvector associated with the second variable? Marks: 1/1 In Q No: 9 Marks: 1/1 After doing z-score scaling on the dataset (FB.csv), Using the scaled dataset, Find out eigenvalues (Note - Please do not preprocess the dataset provided (outlier treatment/null values) unless specified in the question.) Q No: 10 Marks: 0/1 After doing z-score scaling on the dataset (FB.csv), Using the scaled dataset, What is explained variances ratio? [Show More]

Last updated: 2 years ago

Preview 1 out of 12 pages

Buy Now

Instant download

We Accept: