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