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