Classification Algorithms

1. Logistic Regression

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

Logistic Regression

2. Naive Bayes

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.

Naive Bayes

3. Stochastic Gradient Descent

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.

Stochastic Gradient Descent

4. K-Nearest Neighbors

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.

K-nearest neighbors

5. Decision Tree

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.

Decision Tree

6. Random Forest

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.

Random Forest

7. Artificial Neural Network

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

Artificial Neural Network

8. Support Vector Machine

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.

K-nearest neighbors