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