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problem of decision Tree is two: one is to use training data to complete decision tree generation process, and the other is to use testing data to complete the simplification process of decision tree. As we mentioned earlier, there are often too many inference rules generat
regression method, backward regression method, stepwise regression method4. The steps of linear regression analysis (1) To do the basic analysis of the data, the analysis is the potential of the interpretation of the variables and the underlying relationship between the variables to be interpreted;(2) The candidate mo
=650; "src=" http://img.blog.csdn.net/20160129144637155 "alt=" here write a picture describing "title=" "style=" border:none; "/ >Summary Data Statistics various indicators support the summation of the average maximum, the minimum value and so on a series of statistical methods to provide choices.the way metrics that support multiple calculation indicators can come from a field and can be calculated from a
everyone to ponder:Data Mining Accuracy Chart:We have the rest of the test data to make a validation chart, there are ideal best model, the worst random prediction model, and their probability, of course, this is our decision tree Prediction model, the chart of the dimensions and values are not analyzed, their own taste.We can also draw a profit chart based on our predictive model:The so-called profit char
Original: (original) Big Data era: a summary of knowledge points based on Microsoft Case Database Data Mining (Microsoft Decision Tree Analysis algorithm)With the advent of the big data age, the importance of data mining becomes a
Basic use of RapidMiner (a simple decision tree algorithm analysis of a medical data)Files that need to be analyzed:Right-click to create a few processes that read Excel data, select Properties, set objects, decision tree algorithms, and then connect themRead Excel
, naive Bayesian, decision tree, SVM, integration method, GBDT ...). ), regression model and regression thought classification (linear regression, logical return "also called Reggie Regression, logistic regression. Transliteration difference ", neural network, regression tree), cluster model (K-means, DBSCAN, hierarchical clustering 、... 6-1 machine learning and data mo
. From the actual situation, the Android version of domestic users is generally newer. The Representative machine of version 1.5 is Motorola's me600, and the Representative machine of version 1.6 in China is Lenovo happy phone. If you are a newly developed application, we recommend that you do not consider the old version. From app development completion, release, and promotion until the target user uses your product, the amount of 1.5 and 1.6 is very low. Finally, you need to consider based on
FlowFour, according to the sales data to the customer hierarchical clustering calculation1. connect to query customer's consumer informationSetting connection and key columnsQuery results2. standardization before cluster computingSet up columns and standardized algorithms that require normalizationStandardized results3. Compute Hierarchical ClusteringSpecify distance functions, connection types, and columns that participate in cluster calculationsHie
variance of chi-square The mean value of the distribution is degrees of freedom N, $$ E (x^2) = n$$The variance of the distribution is twice times the degree of freedom (2n), recorded as $$ D (x^2) = 2n$$ Properties 1) in the first quadrant, the Chi-square value is positive, positive-biased (right-biased), with the increase of the parameter n, the distribution tends to normal distribution, the area under the chi-square distribution density curve is 1.2) the mean and variance
I. Background Introduction Why do we have the best and most experienced staff to leave prematurely. The data came from the Kaggle and tried to predict what the next valuable employee would leave. Analyze the data to see what factors affect the resignation of employees, as well as the main reasons for predicting which outstanding employees will leave. Variable Description: second, descriptive
distance, etc.3. Dispersion and variability full range (very poor): the use of a full-distance data set, only describes the width of the data, there is no description of the distribution of data patterns. Four min. four min.-Lower four-bit number, which is less affected by outliers than full-distance. (Bottom four: N/4, if an integer, take n/4 this position and
equation of the original variable y and x: (Situps-mean (situps))/sd (situps) =-0.13846688* (Weight-mean (weight))/sd (weight)- 0.52444579* (Waist-mean (waist))/sd (waist) -0.08542029* (Pulse-mean (Pulse))/sd (Pulse)--GT;SITUPS=SD (situps) [- 0.13846688* (Weight-mean (weight))/sd (weight) -0.52444579* (Waist-mean (waist))/sd (waist) -0.08542029* (Pulse-mean ( Pulse))/sd (pulse)]+mean (waist)SD (y[,2]) *-0.1384668393/sd (x[,1]) #weight的回归系数SD (y[,2]) *-0.52444579/sd (x[,2]) #waist的回归系数SD (y[,2])
When it comes to data mining, we tend to focus on algorithms during modeling while ignoring other steps. In real world data mining projects, other steps are the key to determining project success or failure. Guide to intelligent data analysis is the book recommended by the k
Python data analysis: two-color ball statistics of which combination of red and blue balls is high, python Data Analysis This article describes how to calculate the ratio of two red and blue balls in a two-color ball statistical method based on Python
Python data analysis: two-color ball statistics method with a high proportion of a single red and blue ball, python Data Analysis This article describes how to calculate the ratio of a single red ball to a blue ball by using the two-color ball in Python
How should we optimize the DB2 data statistics and analysis system? Many people may have mentioned this issue. The following describes how to optimize the DB2 data statistics and analysis system for your reference. Combined with t
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
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