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