A) classification B) both a and b C) None of these D) prediction
A) low diamesional data B) None of these C) High dimensional data D) medium dimensional data
A) steam B) leaf C) root D) None of these
A) None of these B) Information Gain C) Gini Index D) Entropy
A) None of these B) What are the advantages of the decision tree? C) Non-linear patterns in the data can be captured easily D) Both
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) Data Mining B) Data Selection C) Warehousing D) Text Mining
A) Knowledge Data definition B) Knowledge data house C) Knowledge Discovery Database D) Knowledge Discovery Data
A) To obtain the queries response B) For data access C) In order to maintain consistency D) For authentication
A) Cluster analysis and Evolution analysis B) Prediction and characterization 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) 5 D) 2
A) Find good features to improve your clustering score B) Find the explained variance C) Find which dimension of data maximize the features variance D) Avoid bad features
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) MCRS C) All of the mentioned D) MCV
A) None of the mentioned B) plotsample C) levelplot D) featurePlot
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) False B) True |