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Naïve Bayes Classifier
This is a statistical technique that is based on the Bayes' theorem. Naïve Bayes classifier offers an extremely fast way of predicting the class of unknown data sets compared to other classification algorithms. This method assumes that the predictors are independent. In other words, the feature observed in a specific class is in no way related to any other feature observed. The naïve Bayes classifier can be used for any large dataset and is also easy to build. Regardless of its simplicity, this method can outperform even the most complex classification methods.
Decision Tree
The decision tree draws its name from its tree-like structure. It is used to model likelihood outcomes such as the cost of raw materials and the consequences of making a particular decision. It is an effective way of presenting conditional control statement algorithms. The branches in the decision tree represent the steps of making a decision. These branches can lead you to a favorable result. A decision tree is like a flowchart. Each stage has internal nodes that inform us of its characteristics or tests.
Correlation Analysis
This is a type of test that is used to determine if there is a significant relationship between categorical and quantitative variables. Example of real-world data that can be analyzed using correlation analysis are:
- The type of food you take and your weight
- Amount of time you dedicate to studying and your GPA
- The eye color of your parents and that of your siblings
If you want to put a value on a particular relationship, then you would use a correlation coefficient. These coefficients have a value range of -1 and 1. If there is no relationship, the correlation coefficient value will be zero. The -1 and 1 correlation value describes the type of graph produced by the relationship.