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# Top 10 Machine Learning Algorithms Explained

Machine Learning (ML) is a subset of Artificial intelligence (AI) algorithms. It makes the software applications more efficient and accurate in processing the historical data to predict the outcomes of future data. Machine learning algorithms always try to fine-tune their parameters according to the learning experience gained from the dataset. Also, it considers the feedback from the previous output and improves it. So, it is important to know the concepts behind machine learning algorithms and different types of machine learning algorithms.

The following are the very important machine learning algorithms used widely to solve real-world problems:

• Linear Regression: For statistical techniques, linear regression is used in which the value of the dependent variable is predicted through independent variables. A relationship is formed by mapping the dependent and independent variable on a line, and that line is called the regression line, which is represented by Y= a*X + b where Y= Dependent variable (for example, weight) X= Independent Variable (e.g., height) b= Intercept and a = slope.

• Logistic Regression: In logistic regression, we have a lot of data whose classification is done by building an equation. This method is used to find the discrete dependent variable from the set of independent variables. Its goal is to find the best fit set of parameters. In this classifier, each feature is multiplied by a weight, and then all are added. Then the result is passed to a sigmoid function, which produces the binary output. Logistic regression generates the coefficients to predict a logit transformation of the probability.

• Decision Tree: It belongs to a supervised learning algorithm. The decision tree can be used for classification and regression, both having a tree-like structure. In a decision tree building algorithm first, the best attribute of the dataset is placed at the root, and then the training dataset is split into subsets. The splitting of data depends on the features of the datasets. This process is done until the whole data is classified, and we find the leaf node at each branch. Information gain can be calculated to find which feature is giving us the highest information gain. Decision trees are built for making a training model that can be used to predict the class or the value of the target variable.

• Support Vector Machine (SVM): The support vector machine is a binary classifier. Raw data is drawn on the n-dimensional plane. In this, a separating hyperplane is drawn to differentiate the datasets. The line drawn from the center of the line separating the two closest data points of different categories is taken as an optimal hyperplane. This optimized separating hyperplane maximizes the margin of training data. Through this hyperplane, new data can be categorized.

• Naive-Bayes: It is a technique for constructing classifiers, which is based on the Bayes theorem used even for highly sophisticated classification methods. It learns the probability of an object with certain features belonging to a particular group or class. In short, it is a probabilistic classifier. In this method occurrence of each feature is independent of the occurrence of another feature. It only needs a small amount of training data for classification, and all terms can be precomputed; thus, classifying becomes easy, quick, and efficient.

• KNN: This method is used for both classification and regression. It is among the simplest method of machine learning algorithms. It stores the cases, and for new data, it checks the majority of the k neighbors with which it resembles the most. KNN makes predictions using the training dataset directly.

• K-means Clustering: It is an unsupervised learning algorithm used to overcome the limitation of clustering. To group the datasets into clusters, the initial partition is done using Euclidean distance. Assume if we have k clusters, for each cluster, a center is defined. These centers should be far from each other, and then each point is examined thus added to the belonging nearest cluster in terms of Euclidean distance to the nearest mean until no point remains pending. A mean vector is re-calculated for each new entry. The iterative relocation is done until proper clustering is done. Thus for minimizing the objective squared error function process is repeated by generating a loop. The results of the K-means clustering algorithm are - The centroids of the K clusters, which are used to label newly entered data, and - Labels for the training data.

• Random Forest: It is a supervised classification algorithm. Multiple numbers of decision trees taken together form a random forest algorithm, that is, the collection of many classification trees. It can be used for classification as well as regression. Each decision tree includes some rule-based systems. For the given training dataset with targets and features, the decision tree algorithm will have a set of rules. In a random forest, unlike decision trees, there is no need to calculate information gain to find the root node. It uses the rules of each randomly created decision tree to predict the outcome and stores the predicted outcome. Further, it calculates the vote for each predicted target. Thus, a high voted prediction is considered as the final prediction from the random forest algorithm.

• Dimensionality Reduction Algorithms: It is used to reduce the number of random variables by obtaining some principal variables. Feature extraction and feature selection are types of dimensionality reduction methods. It can be done by principal component analysis(PCA) is a method of extracting important variables from a large set of variables. It extracts the low dimensionality set of features from high dimensional data. It is basically used when we have more than 3-dimensional data.

• Gradient boosting and Ada Boost Algorithms: The Gradient boosting algorithm is a regression and classification algorithm. AdaBoost only selects those features which improve the predictive power of the model. It works by choosing a base algorithm like decision trees and iteratively improving it by accounting for the incorrectly classified examples in the training set. Both algorithms are used the boosting the accuracy of a predictive model.

The above are the top 10 machine learning algorithms used in the industry for solving data science problems.