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