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