Data Science Publication

A decision tree is a supervised machine learning algorithm mainly used for Regression and Classification. It breaks down a data set into smaller and smaller subsets while at the same time an associated decision tree is incrementally developed. The final result is a tree with decision nodes and leaf nodes. A decision tree can handle both categorical and numerical data.  

 There are four types of kernels in SVM. Linear Kernel Polynomial kernel Radial basis kernel Sigmoid kernel

  In the diagram, we see that the thinner lines mark the distance from the classifier to the closest data points called the support vectors (darkened data points). The distance between the two thin lines is called the margin.

SVM stands for support vector machine, it is a supervised machine learning algorithm which can be used for both Regression and Classification. If you have n features in your training data set, SVM tries to plot it in n-dimensional space with the value of each feature being the value of a particular coordinate. SVM uses hyperplanes to separate out different classes based on the provided kernel function.    

The Naive Bayes Algorithm is based on the Bayes Theorem. Bayes’ theorem describes the probability of an event, based on prior knowledge of conditions that might be related to the event. The Algorithm is ‘naive’ because it makes assumptions that may or may not turn out to be correct. 

Eigenvectors  are used for understanding linear transformations.  In data analysis, we usually calculate the eigenvectors for a correlation or covariance matrix.  Eigenvectors are the directions along which a particular linear transformation acts by flipping, compressing or stretching. Eigenvalue  can be referred to as the strength of the transformation in the direction of eigenvector or the factor by which the compression occurs.

  Univariate  analyses  are descriptive statistical analysis techniques which can be differentiated based on the number of variables involved at a given point of time. For example, the pie charts of sales based on territory involve only one variable and can the analysis can be referred to as univariate analysis. The  bivariate  analysis  attempts to understand the difference between two variables at a time as in a scatterplot.  For example, analyzing the volume of sale and spending can be considered as an example of bivariate analysis. Multivariate analysis  deals with the study of more than two variables to understand the effect of variables on the responses.