A) both a and b B) prediction C) classification D) None of these
A) low diamesional data B) High dimensional data C) None of these D) medium dimensional data
A) None of these B) leaf C) steam D) root
A) None of these B) Information Gain C) Entropy D) Gini Index
A) What are the advantages of the decision tree? B) Non-linear patterns in the data can be captured easily C) Both D) None of these
A) None of these B) Random forest are difficult to interpret but very less accurate C) forest are Random difficult to interpret but often very accurate D) Random forest are easy to interpret but often very accurate
A) Text Mining B) Data Selection C) Warehousing D) Data Mining
A) Knowledge Data definition B) Knowledge Discovery Database C) Knowledge data house D) Knowledge Discovery Data
A) To obtain the queries response B) For data access C) For authentication D) In order to maintain consistency
A) Association and correctional analysis classification B) Prediction and characterization C) All of the above D) Cluster analysis and Evolution analysis
A) K-means clustering can be defined as the method of quantization B) All of the above C) The goal of the k-means clustering is to partition (n) observation into (k) clusters D) The nearest neighbor is the same as the K-means
A) 2 B) 5 C) 3 D) 4
A) Avoid bad features B) Find which dimension of data maximize the features variance C) Find the explained variance D) Find good features to improve your clustering score
A) Find the features which can best predicts Y B) Make the training time more fast C) Use Standardize the best practices of data wrangling D) data allows other people understand better your work
A) MARS B) MCRS C) MCV D) All of the mentioned
A) featurePlot B) levelplot C) None of the mentioned D) plotsample
A) postProcess B) All of the above C) process D) preProcess
A) False B) True
A) PCA B) None of the mentioned C) SCA D) ICA
A) True B) False |