Machine Learning Algorithms
Algorithms for machine learning are those that can identify hidden patterns in data, forecast results, and enhance performance via independent experience. In machine learning, multiple algorithms may be used to various tasks.
- Linear Regression Algorithm
- Logistic Regression Algorithm
- Decision Tree
- Random Forest
- K-Nearest Neighbour
- Principle component Analysis
- K-Means Clustering
- Naïve Bayes
- Support Vector Machine
- Apriori
1. Linear Regression Algorithm
One of the most widely used and easy-to-understand machine learning methods for predictive analysis is linear regression. Predictive analysis is used here to express predictions about anything, while linear regression forecasts continuous variables like age and salary.
It depicts how the dependent variable (y) varies in response to the independent variable (x) and the linear relationship between the dependent and independent variables.
The regression line is the best fit line that results from trying to find the greatest possible relationship between the independent and dependent variables.
2. Logistic Regression Algorithm
Supervised learning involves the use of algorithms like logistic regression, which is one of the most widely used machine learning techniques. It makes use of a predetermined collection of independent factors to predict the category dependent variable.
A categorical dependent variable's output is predicted via logistic regression. For this reason, the result has to be a discrete or category value. It can indicate Yes or No, 0 or 1, true or false, etc., but rather than providing a precise value between 0 and 1, it provides probabilistic values that fall in that range.
With the exception of how they are applied, logistic regression and linear regression are very similar. While logistic regression is used to solve classification difficulties, linear regression is used to solve regression problems.
In logistic regression, two maximum values are predicted by fitting a "S" shaped logistic function, as opposed to fitting a regression line (0 or 1).
3. Decision Tree Algorithm
Although it may be used to solve regression and classification problems equally, decision trees are a supervised learning technique that are most often employed to solve classification difficulties. The classifier is designed in the form of a tree, with core nodes standing in for dataset attributes, branches for decision rules, and leaf nodes representing individual outcomes.
There are two nodes in a decision tree: the decision node and the leaf node. Decision nodes are used to make decisions and can have many branches, while leaf nodes represent the result of decisions and have only one branch.
The characteristics of the data set given are used to inform the decisions or the test. It is a graphical tool that shows all of the options for solving a problem or making a decision given certain parameters.
It is named a decision tree because, like a tree, it begins with the root node and grows on subsequent branches to form a structure like a tree. The Classification and Regression Tree algorithm, or CART algorithm, is what we use to construct trees.
A decision tree merely poses a question, and it further divides the tree into subtrees according to the response (Yes/No).
4. Random Forest Algorithm
The supervised learning approach that can be applied to machine learning issues including both regression and classification is called random forest. It is an ensemble learning strategy that enhances the model's performance by integrating different classifiers to generate predictions.
Each tree provides the classification result when classifying a new dataset or object, and the algorithm predicts the final outcome based on the votes cast in the majority.
5. k-Nearest Neighbour Algorithm
One of the most basic machine learning algorithms, K-Nearest Neighbour, is based on the supervised learning approach.
The K-NN method places the new case in the category most comparable to the existing categories based on its assumption that the new instance and its data are similar to the examples that are already available.
The K-NN method classifies a new data point based on similarity after storing all the relevant data. This indicates that the K-NN algorithm can quickly classify newly discovered data into a well-suited category. Although the K-NN technique is mostly utilized for classification problems, it can also be used for regression.
K-NN does not make any assumptions about the underlying data because it is a non-parametric algorithm. Because it saves the dataset and acts on it while classifying, it is also known as a lazy learner algorithm. This is because it does not learn from the training set right away.
When new data is received, the KNN algorithm categorizes it into a category that is quite similar to the previously stored dataset, which is all that is needed during the training phase.
6. Principle Component Analysis Algorithm
One method for reducing dimensionality in unsupervised learning is Principle Component Analysis (PCA). It facilitates the reduction of the dataset's dimensionality, which is made up of numerous features that are interrelated. It is a statistical procedure that uses orthogonal transformation to turn a set of correlated feature observations into a set of linearly uncorrelated features. It is a widely used tool for both predictive modelling and exploratory data analysis.
In order to minimize dimensionality, PCA takes into account each attribute's variance. A large variance indicates a successful class split.
7. k-Means Clustering Algorithm
One of the most straightforward unsupervised learning strategies for clustering problems is K-means clustering. Based on similarities and differences, the datasets are divided into K distinct clusters; that is, the datasets with the greatest degree of commonality stay in a single cluster while the datasets with the least amount of commonality or none at all remain in separate clusters. K-means denotes the number of clusters and means denotes the dataset's average used to determine the centroid. Every cluster in the method is linked to a centroid, which is based on centroid theory. The goal of this technique is to shorten the distances between data points and cluster centroids.
8. Naïve Bayes Algorithm
A supervised learning method called the Naïve Bayes classifier is used to predict things based on how likely it is that they will occur. Because it is predicated on the Bayes theorem and operates on the naïve assumption that variables are independent of one another, the algorithm is known as Naïve Bayes.
The Bayes theorem is predicated on conditional probability, which is defined as the chance that event (A) will occur provided that event (B) has already occurred. One of the best classifiers for a given problem that yields good results is the Naïve Bayes classifier. A naïve Bayesian model is simple to construct and works well with large datasets. Text classification is the main method of use.
9. Support Vector Machine Algorithm
Support vector machines, or SVMs, are supervised learning algorithms that are primarily used for classification problems, though they can also be used for regression problems. The objective of an SVM is to create a decision boundary, or hyperplane, that can be used to separate datasets into different classes; the data points that aid in defining the hyperplane are called support vectors, hence the algorithm's name.
10. Apriori Algorithm
The unsupervised learning algorithm known as the apriori algorithm is used to resolve association difficulties. It is intended to operate on databases that contain transactions and generates association rules using frequent itemsets. It establishes the strength or weakness of the relationship between two objects with the aid of these association rules. This approach computes the itemset in an efficient manner by using a Hash Tree and a breadth-first search. The method searches the huge dataset iteratively for the frequently occurring itemset.