LIST OF TOP 7 MACHINE LEARNING ALGORITHMS TO KNOW
According to Forbes, the world is currently generating every day 2.5 quintillions of data. Over time, there are loads of data which relies on the use of specialized machine learning algorithms.
Machine learning applications are highly automated and self -modifying which can be improved over time with minimal human intervention. It is gaining more importance as they can learn with more data. Since it’s hard to understand the complex nature of various real-world problems, specialized machine learning algorithms list is developed.
Machine learning is an application that utilizes artificial intelligence and offers systems the ability to automatically learn and improve over time. It allows computers to learn automatically without any sort of human intervention or assistance. Its process of learning begins with data, experiences, or understanding the patterns in data that can help better decisions. Each machine learning has its strengths and weaknesses. Some Machine Learning algorithms in python are easier to understand but lack predictive power.
These machine learning algorithms list, can be easily classified as:
1. Supervised Machine learning algorithms
These types of machine learning algorithms that can make predictions on a given set of samples. Supervised machine learning algorithm searches for patterns within the value labels assigned to defined data points.
2. Unsupervised Machine learning algorithms
When there are no labels assigned with data points, it is called unsupervised machine learning algorithms. These algorithms organize the data into different groups of clusters to describe its structure and make the complex data look simple and organized for analysis.
3. Reinforcement Machine Learning algorithms
These algorithms select an action, based upon every data point. Over time, these algorithms change their strategy to learn better and achieve the best reward. The machine or the computer is rewarded when it learns correctly and penalized when it does it incorrectly.
Let’s dive into some of the individual machine learning algorithms, their applications:
- Naive Bayes Classifier Algorithm
Naive Bayes Classifier machine learning algorithm has covered the difficulties which were impossible to classify a web page, document, an email or any other lengthy text notes manually. A classifier is a function that allocates an element’s value from one of the available categories
These types of machine learning algorithms, are one of the popular learning method grouped by similarities which works on the popular Bayes Theorem of Probability- build machine learning models based upon disease prediction and document classification.
For instance, Spam Filtering is a popular classification of the Naive Bayes algorithm. Hence Spam is a classifier that assigns a label” Spam” or “Not Spam” to every mail.
2. Logistic Regression
Logistic Regression is used to estimate the probability of whether an instance belongs to a particular class or not.
For instance, what is the probability that this email is spam or not?
In case the probability is greater than 50%, then it is predicted that it is a positive class labeled “1”, otherwise it is predicted that it belongs to the negative class labeled “0”.
This model computes the weighted sum of the input features. It shows the logistic results instead of outputting the results.
It is used to classify elements into two categories based upon repayment histories such as defaulters and non-de-faulters.
In case, when it is required to predict the probability and the variable will fall into two categories, logistic regression is used.
3. K- Means Clustering
K-means clustering is an unsupervised algorithm that can be utilized for cluster analysis.
It is an iterative algorithm that divides a collection of data containing n values into k clusters or subgroups. This division of data is done with each of the n values belonging to the k cluster with the nearest mean.
In the above statement, K -means clustering is done on a given dataset through a predefined number of k-clusters. Based on their similarities and the distance of each data point in the cluster with the mean of their centroid, the clusters of data are grouped.
These types of machine learning algorithms tend to minimize the Euclidean distance which every point from the centroid of the cluster. It refers to the intra-cluster variance and can be minimized using the squared error function, as shown below:
Let’s dive into the clustering process:
- It initializes randomly and selects k points(k=4)
- The Euclidean distance is used to find the data points closest to the center of the cluster.
- The mean of all points in the cluster, called centroid can be calculated.
- Repeat the steps from 1 to 3 until every data point is assigned to their respective clusters.
4. Support Vector Machine Learning Algorithm
Support Vector Machine is one of the supervised machine learning algorithms for experts which is used for classification or regression problems where the dataset teaches SVM about the classes so that SVM can classify any new data.
Classification of data into various classes is done by finishing a line(hyperplane) separating the training data set into classes.
When there are many such liner hyperplanes, then the SVM algorithm tries to maximize the distance between the various classes which are involved and it is referred to as margin maximization.
In case, the line that maximizes the distance between the classes is identified, then the probability to generalize to unseen data can be increased.
5. Apriori machine language algorithm
Apriori is one of the machine language algorithms for experts that creates association rules from a given dataset. This association rule implies that if item A occurs, then item B also occurs with a certain probability. Generally, the association rules are in the IF-THEN format.
For instance, if people buy an iPad then there is a probability to buy an iPad Case for protection. To derive the results, it observes the number of people buying an iPad case while purchasing an iPad. Henceforth, a ratio is derived out of the given data.
6. Linear Regression Machine Learning Algorithm
This machine learning algorithms example shows the relationship between 2 variables and how a small change in one variable impacts the other. This algorithm shows the impact on the dependent variable when the independent variable is changed. These independent variables are referred to as explanatory variables which explain the factors, it got affected because of the dependent variable.
7. Decision Tree Machine Learning Algorithm
A decision tree is machine learning algorithms for beginners, a visual representation that utilizes branches to demonstrate every possible outcome of a given decision based upon certain conditions.
Every internal node in a decision tree represents a test on the attribute and each branch of the tree represents the outcome of the test, and the leaf node represents a given class label, which shows the final decision after computing every attribute.
The classification rules are represented by a path from the root to the leaf node:
Classification Trees– Based upon the variable response, these default set of decision trees which are used to separate a data-set into different classes.
Regression trees – are used when the response or target variable is continuous or numerical. These are mostly used when there is predictive as compared to classification.
8. Random Forest
A random forest is an association of decision tree classifiers. It utilizes a bagging approach to create a bunch of decision trees with a random subset of data. To gain better prediction accuracy, a model is trained on the subset of the dataset in these types of machine learning algorithms.
To make a final prediction, the output of all the decision trees in the random forest is obtained by averaging or polling the results of each decision tree or by picking the prediction which appears the most.
9. K- Nearest Neighbors (KNN)
KNN is a simple algorithm which predicts unknown data point with its k nearest neighbors. The accuracy can be determined by the value of k. It determines the nearest by calculating the distance using basic distance functions like Euclidean.
New data points are classified by utilizing the majority vote of K and its neighbor. It requires high computation power and we need to normalize data so that to bring every data point to the same range.
10. Dimensional Reduction Algorithms
Many variables are stored in the datasets which may be hard to handle. Due to the existence of more than enough resources, data collection in systems occurs at a detailed level. The data sets may contain thousands of variables and it may be unnecessary as well.
Dimensional reduction algorithms are used in situations where identifying the variables seems to be impossible which has the most impact on prediction. It also utilizes other algorithms like a Decision tree and Random forest to identify the most important variables.