Random Forest

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  Decision Tree Algorithm o    Supervised machine learning algorithms. o    It is used for both a classification problem as well as for regression problem. o    It   predicts the value of a target variable(dependent variable) o    A decision tree represents a procedure for classifying   categorical data based on their attributes. o    The construction of decision tree does not require any   domain knowledge or parameter setting, and therefore   appropriate for exploratory knowledge discovery. o    Decision tree uses the tree representation to solve the problem in which the leaf node corresponds to a class label and attributes are represented on the internal node of the tree Decision Tree learning is one of the most widely used and practical methods for inductive inference over supervised   data. o    Their representation of acquired knowledge in tree form   is intuitive...
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Non Linear SVM and Kernal function

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  Non-Linear SVM and Kernal function Non Linear Data : If a data set or given sample can not be separated by a single line then it is non linear data. Non Linear SVM: To separate non linear data , non linear SVM is used. Trick Kernel Function: To separate non linear data Trick Kernals are used. Both non linear data and Trick Kernals are related to each other. Non Linear Data: Figure 2 shows non linear data. Single line or straight line can not separate these data. Straight line gives less than 50% accuracy.         Figure1: Non Linear Data Solution to this problem is use of Trick Kernel. Trick Kernel takes input low dimensional feature space and convert into high dimensional feature space so that non separable data get separated   Two dimensional data converted into three dimensional data using kernel as shown in figure 1 and it become seperable. ·          Following are the different kernel used in SVM: Ø ...
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