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Pruning is the procedure that decreases the size of decision trees. It can decrease the risk of overfitting by defining the size of the tree or eliminating areas of the tree that support little power. Pruning supports by trimming the branches that follow anomalies in the training information because of noise or outliers and supports the original tree in a method that enhances the generalization efficiency of the tree.Various methods generally use statistical measures to delete the least reliable departments, frequently resulting in quicker classification and an improvement in the capability of the tree to properly classify independent test data.There ... Read More
A decision tree is a flow-chart-like tree mechanism, where each internal node indicates a test on an attribute, each department defines an outcome of the test, and leaf nodes describe classes or class distributions. The highest node in a tree is the root node.Algorithms for learning Decision TreesAlgorithm − Create a decision tree from the given training information.Input − The training samples, samples, described by discrete-valued attributes; the set of students attributes, attribute-list.Output − A decision tree.MethodCreate a node N;If samples are all of the same class, C thenReturn N as a leaf node labeled with the class CIf the ... Read More
There are two types of statistical-based algorithms which are as follows −Regression − Regression issues deal with the evaluation of an output value located on input values. When utilized for classification, the input values are values from the database and the output values define the classes. Regression can be used to clarify classification issues, but it is used for different applications including forecasting. The elementary form of regression is simple linear regression that includes only one predictor and a prediction.Regression can be used to implement classification using two various methods which are as follows −Division − The data are divided ... Read More
There are the following pre-processing steps that can be used to the data to facilitate boost the accuracy, effectiveness, and scalability of the classification or prediction phase which are as follows −Data cleaning − This defines the pre-processing of data to eliminate or reduce noise by using smoothing methods and the operation of missing values (e.g., by restoring a missing value with the most generally appearing value for that attribute, or with the best probable value established on statistics). Although various classification algorithms have some structures for managing noisy or missing information, this step can support reducing confusion during learning.Relevance ... Read More
To change the color of line in xyplot, we can use col argument.For example, if we have two vectors say X and Y and we want to create a red colored xyplot between X and Y then we can use the following command −xyplot(x~y,type="l", col="red")Check out the below example to understand how it works.ExampleTo change the color of line in xyplot, use the code given below −set.seed(123) library(lattice) xyplot(1:5~rpois(5,5),type="l",col="blue")OutputIf you execute the above given code, it generates the following output −
To fill the outliers in boxplot with different color in base R, we can use outpch argument for the shape and outbg argument for colors.For example, if we have a vector called X that contains some outliers then we can create a boxplot of X with different color outliers by using the below mentioned command −boxplot(X,outpch=21,outbg="blue")ExampleTo fill the outliers in boxplot with different color in base R, use the code given below −x
To display outliers in boxplot with different shape in base R, we can use outpch argument in boxplot.For example, if we have a vector called X that contains some outliers then we can create a boxplot of X with different shape of outliers by using the below given command −boxplot(X,outpch=17)ExampleTo display outliers in boxplot with different shape in base R, use the code given below −x
Classification is a data mining approach used to forecast team membership for data instances. It is a two-step procedure. In the first step, a model is built defining a predetermined set of data classes or approaches. The model is developed by considering database tuples defined by attributes.Each tuple is considered to belong to a predefined class, as decided by one of the attributes, known as the class label attribute. In the framework of classification, data tuples are also defined as samples, examples, or objects. The data tuples analyzed to develop the model jointly form the training data set. The single ... Read More
To change the color of box of boxplot in base R, we can use col argument inside boxplot function.For example, if we have a vector called V and we want to create a boxplot of V without red colored box then we can use the following command −boxplot(x,col="red")ExampleTo change the color of box of boxplot in base R, use the code given below −x
Genetic algorithms are mathematical structures using the procedure of genetic inheritance. They have been successfully used to a broad variety of analytic issues. Data mining can connect human understanding with automatic analysis of information to find a design or key relationships.Given a large database represented over several variables, the objective is to effectively find the most interesting design in the database. Genetic algorithms have been used to recognize interesting designs in some software. They generally are used in data mining to enhance the execution of other algorithms, such as decision tree algorithms, another association rule.Genetic algorithms needed a specific data ... Read More