Classification Algorithms in Machine Learning

In this section, we will examine classification algorithms in machine learning.

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1. Logistic Regression

📌 The aim is to establish a linear model for the classification problem that describes the relationship between dependent and independent variables.

2. Naive Bayes

📌 It is a probability-based modeling technique. The aim is to calculate the probability that a particular sample belongs to each class, based on conditional probability.

3. k-Nearest Neighbors(KNN)

📌 Predicts are made based on observation similarity

4. Support Vector Classification(SVC) ===> Linear & RBF

📌 The goal is to find the hyperplane that will allow the separation between the two classes to be optimal.

5. Artificial Neural Network(ANN)

📌 It is one of the powerful machine learning algorithms that can be used for classification and regression problems that refer to the way the human brain processes information

6. Classification and Regression Trees(CART)

📌 The aim is to transform the complex structures in the data set into simple decision structures.

7. Random Forests(RF)

📌 It is based on the evaluation of the predictions produced by multiple decision trees.

8. Gradient Boosting Machines(GBM)

📌 It is a generalized version of AdaBoost that can be easily adapted to classification and regression problems. A series of models in the form of a single predictive model are constructed on the residuals.

9. Extreme Gradient Boosting(XGBoost)

📌 XGBoost is optimized to increase the speed and prediction performance of GBM. It is scalable and can be integrated into different platforms.

10. LightGBM

📌 LightGBM is another type of GBM developed to increase the training time performance of XGBoost.

11. Category Boosting(CatBoost)

📌 It is another fast, successful type of GBM that can automatically deal with categorical variables.