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