(A) Logistic regression
Logistic regression is a statistical method for developing machine learning models using binary dependent variables. For describing data and the correlation between a dependent and one or more independent variables, logistic regression is used. The independent variables may be of the interval, nominal, or ordinal types. The term "logistic regression" is derived from the logistic function that it uses. Logistic function is also called as sigmoid function. This logistic function has a value between zero and one.
(B) k-nearest neighbors (KNN)
The k-nearest neighbor is a data categorization technique for calculating the probability that a data point will join one group or another depending on the group to which the data points are closest. KNN is a supervised ML model which is used for resolving classification as well as regression issues.
KNN is a lazy learning algorithm as it doesn’t execute any training when we provide training data. It does not do any computations during the training period, merely saves the data. It does not build model until any query is executed on the dataset. KNN is hence perfect for data mining.
(C) Support Vector Machine (SVM)
SVM is the most often used supervised learning technique for handling both classification and regression issues. SVM method aims to define the best decision boundary which splits the n-dimensional space in classes that enable us to categorise new data points with ease in future. The term "hyperplane" refers to this decision boundary. SVM selects the extreme vector points for building the hyperplane. The SVM method is based upon support vectors, which are used for representing such extreme situations.
(D) Naive Bayes Classifiers
Naive Bayes Classifiers are supervised learning algorithm based on Bayes Theorem in which one assumption is taken as the strongest independence assumptions between the features. They make the assumption that the value of one feature is unrelated to value of other feature.
Gaussian Naive Bayes is a variant of Naive Bayes that works with continuous data and adheres to Gaussian normal distribution. When dealing with continuous data, it is common to make the assumption that continuous values of each class are distributed using Gaussian distribution.
(E) Decision Tree
Decision Trees are supervised learning methods that are used to solve classification as well as regression issues, however they are often chosen for classification problems. DT is a tree-structured classifier in which inside nodes represent the data characteristic features and the leaf nodes represent the classification results. A decision tree has two nodes- i) Decision Node ii) Leaf Node. Decision nodes makes the decisions and may have several branches, while Leaf nodes record the outcomes of such decisions and does not have any further branch.
(F) Random Forest
Random Forest is a machine learning technique that uses several decision trees to make decisions. The different random forest trees gives out a class prediction and the class which obtains the maximum votes is the prediction result generated by our model. The best results come when a large number of very dissimilar models (trees) collaborate as a committee.
(G) Extra Trees Classifier
Extra Trees Classifier is a method of Ensemble Learning that integrates the findings of various uncorrelated decision trees gathered in a "forest" to get the classification result. The only way it varies conceptually from Random Forest is the construction of decision trees in the forest.
The original training samples are used to build every Decision Tree of Extra Tree Classifier’s forest. Then, k random features out from feature set are distributed to every tree for each test node. Among these, the tree must choose which characteristic will best classify the data as per the specified mathematical rule/criteria. There are several de-correlated decision trees produced as a result of this random sampling of characteristics.
(H) Gradient Boosting
Boosting is a technique for turning weak learners to strong ones. Each new tree in boosting is a fit on an altered version of the initial data set. It is predicated that when combined with earlier models, the new model will provide predictions with lower error rates. To reduce errors, the fundamental goal of this forthcoming model is to define desired outcomes.
Gradient boosting is a method for gradually, additively, and sequentially training multiple models. The phrase "gradient boosting" came up because each case's intended outcomes are determined by the gradient's inaccuracy relative to the predictions. Every model makes progress in the right way by lowering prediction errors.
(I) AdaBoost
A machine learning technique called AdaBoost, often referred to as Adaptive Boosting, is used in an ensemble environment. The most common technique used with AdaBoost is decision trees with a single level or with a single split. These trees are also known as Decision Stumps. AdaBoost develops a model that assigns equal weights to all of the data points. Then, it gives larger weights to the points that were misclassified. Now, in the following model, all of the points with higher weights are assigned more significance. It will keep training models up until and until an error is received.