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