exploratory data analysis tukey

columns, where the random number is generated by the standard uniform distribution (U (0,1)).RNG (' Default '); % for ReproducibiltyX = rand (20000,3);Use Ward's linkage to generate hierarchical clustering trees. Set ' savememory ' to ' on ' to construct the cluster but not to calculate the distance matrix.c = Clusterdata (X, ' linkage ', ' ward ', ' savememory ', ' on ', ' Maxclust ', 4);Plot the data into a graphic, where each category corresponds

Example Compare Cluster Assignments to ClustersImport the sample data.Load FisheririsFrom the Anderson Iris Floral Data set, the ward linkage calculates four clusters and ignores the type information.Z = Linkage (MEAs, ' Ward ', ' Euclidean ');c = Cluster (Z, ' Maxclust ', 4);The relationship between cluster results and three species was observed.Crosstab (c,species)Print the first 5 lines of Z.firstfive = Z (1:5,:)Generates a system tree graph

following conditions are available:Linkage is ' centroid ', ' median ' or ' ward 'Distance is ' Euclidean ' (default)When Savememory is ' on ', the linkage run time and the number of dimensions (number of columns in x) are proportional. When Savememory is ' off ', the demand for linkage memory is proportional to N2, where n is the number of observations. The best (and least time-consuming) savememory settings for all choices depend on the dimension of the problem, the number of observations, or

information from existing data (science and art). The data does not increase, but it makes the existing data more useful.For example, from the date information in the dataset, the corresponding week and month information can be obtained, which may make the model more efficient.The process of characteristic engineeringBefore feature engineering, you need to compl

=-0.10993962467082698 View the statistical distribution of data Val Colarray = Array ("Age", "yearsmarried", "religiousness", "Education", "occupation", "rating")//view the statistical distribution of the data Val DESCRDF = Data.describe ("Age", "yearsmarried", "religiousness", "Education", "occupation", "rating") DESCRDF: Org.apache.spark.sql.DataFrame = [Summary:string, age:string ... 5 more

Exploratory analysis referred to as EDAI. Basic DESCRIPTIVE statistics1.summary functionMaximum, minimum, median, and mean values can be obtained2. Four decimal pointsThe quaternion can be obtained by quantile function, and diff gets the difference of each sub-number.> Library (RSADBE)> Data ("Thewall")> quantile (Thewall$score)> diff (quantile (thewall$score))3.

this approach). In fact, the first version of the SPSS and SAS Analytics contains subroutines that can be tuned from one (Fortran or other) program to populate and test a model in a model toolbox. In the framework of this normative and penetrating theory, John Tukey put into the concept of exploratory data analysis (

1.R language important data set analysis needs to be collated and analyzed to clarify the concept of?In the previous section, we talked about the R language mapping, and this section is about how to analyze the data when you get a data set, the first step in the analysis, an

analysis. GenerallyExperimental learning(Experimental Study) we can control the box to manipulate the data generation steps. HoweverObservation Learning (In observational study), we cannot control the data generation process. However, whether the learning process is experimental or observed, it usually contains independent assumptions, and the

gradually disappear (fewer and fewer actors on this stage ). Of course, a good advantage of this strategy is that it has very few requirements for our innovation. We only need to stick to the rules. Another idea was proposed by John Tukey [Tukey (1962)] as early as 1962. He believes that statistics should focus on data analy