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Classifier is a method where we want to find what class each element belongs to.
For our model, we tried and tested various ‘classifiers’, such as:
Naive Bayes
K Nearest Neighbors (KNN)
Random Forest
Confusion Matrix = a table used to show the performance of various classifiers
Values in the diagonal squares (top left to bottom right) should be higher (i.e. better accuracy)
Naive Bayes:
It is a supervised classification algorithm that uses probability (Bayes theorem) to classify the results. It is called naieve because it makes assumptions about the program that may or may not be correct.
K-Nearest Neighbor
It is a supervised machine learning algorithm that classifies based of the "nearest neighbors" of every data point.
Random Forest
A supervised machine learning algorithm that combines multiple decision trees to make a more accurate decision. A decision tree is an algorithm that takes the data and splits it off row by row similar to a tree.
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