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