It is a classification algorithm in machine learning that uses one or more independant variables to determine an outcome. The outcome is measured with a dichotomous variable meaning it will have only two possible outcomes.
Only name confuses us, but this
It is a classification algorithm based on Bayes's theorem which gives an assumption of independence among predictors. In simple tearms, a Naive Bayes classifier assumes that the presence of a feature in class is unrelated to the presence of any other feature.
It is a very effective and simple approach to fit linear models. Stochastic Gradient Descent is particularly useful when the sample data is in a large number. It supports different loss fuctions and penalties for classification.
This is useful for large data set.
It is a lazy learning algorithm that stores all instances corresponding to training data in n-dimensional space. It is a lazy learning algorithm as it does not focus on constructing a general internal model, instead, it works on storing instances of training data.
The decision tree algorithm builds the classification model in the form of a tree structure. It utilizes the if-then rules which are equally exhausetive and mutually exclusive in classification.
Random decision trees or random forest are ensemble learning method for classification, regression etc. It operates by constructing a multitude of decision trees at training time and outputs the class that is the mode of the classes or classification or mean prediction(regression) of the individual tree.
A neural network consists of neurons that are arranged in layers, they take some input vector and convert it into an output. The process involves each neuron taking input and applying a function whic is often a non-linear function to it and then passes the output to the next layer.
use cases are hand writing analysis and colourization black and white images
The support vector machine is a classifier that represents the training data as points in space seperated into categories by a gap as wide as possible. New points are then added to space by predicting which category they fall into and which space they will belong to.