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International Data Corporation (IDC) forecasts that spending on AI and Machine Learning Algorithms will grow from $12B in 2017 to $57.6B by 2021.
Computing has undergone major transformations from large mainframes to personal computers to the cloud. The progress in technology and constant evolution in computing has given rise to automation.
In this article, let’s understand the few commonly used machine learning algorithms. which are helpful for solving any type of data problem.
- Decision Tree
- Linear Regression
- Logistic Regression
- Support Vector Machine (SVM)
- Naive Bayes
- Dimensionality Reduction
Decision Tree
This is a supervised learning algorithm, which is mainly used for classification problems. This algorithm best fits both categorical and continuous dependent variables. With the help of this algorithm, the population is divide into two or more homogeneous sets. Depending on the most significant independent variables/ attributes.
The Decision tree algorithm is very helpful in the banking industry for the classification of loan applicants.
Linear Regression
We can use it to estimate the real values like the cost of properties, number of calls, total sales. There many more base on a continuous variable. In this process, a relationship are form between the independent and dependent variables by fitting the best line. This best fit line is called the regression line and is represented by a linear equation Y= a *X + b.
In this equation:
- Y – Dependent Variable
- a – Slope
- X – Independent variable
- b – Intercept
The coefficients a & b are derived based on decreasing the sum of squared difference of distance between the regression line and data points.
This machine learning algorithm is mostly used for risk assessment in the insurance sector. Linear regression uses to find the number of claims for customers of multiple ages. Which is use to reduce the high risk on the age of the customer.
Logistic Regression
This is used to review discrete values (mainly Binary values like 0/1, yes/no, true/false) based on the available set of the independent variable(s). In simple words, it is useful for predicting the probability of occurrence of an event by fitting data to a logit function. It is also known as logit regression.
The following list can be use in order to improve the logistic regression model
- including interaction terms
- removing features
- regularize techniques
- using a non-linear model
The Logistic Regression is using for highly in the political sector to predict if a specific candidate will win or lose a political election.
Support Vector Machine (SVM)
This algorithm is a classification method where the raw data gets plot as points in n-dimensional space (Here n is the number of features that are available). The value of every feature is being the value to a particular coordinate. This makes it quite easy to classify the data. For example, if we take two features like the height of a person and hair length. First, these two variables will be insert into the two-dimensional space. Where each point has two coordinates these are Support Vectors.
The Support Vector Machine algorithm is using for the comparison of stock performance for stocks in the same sector. This is also helpful in making decisions for managing investments by the financial institutions.
Naive Bayes
The Naive Bayes classifier learning algorithm is based on the Bayes Theorem of Probability. In this, it assumes that the availability of a certain feature in a class is unrelated to the availability of any other feature. The Naive Bayes classifier will take into account all of these properties independently. While calculating the probability of a certain result.
The Naive Bayes classifier algorithm is beneficial for Email Spam Filtering. Gmail mainly uses this algorithm to classify an email as Spam or Not Spam.
Dimensionality Reduction Algorithms
Over the last few years, large amounts of data are store at every possible stage and are getting analyse by various industries. The raw data also consists of many features but the bigger challenge is in identifying highly significant variable(s) and patterns. This machine learning algorithm like PCA, Decision Tree. And Factor Analysis helps find the relevant details depending on the correlation matrix, missing value ratio.
End Notes
If one wants to build a stellar career in machine learning then they should start right away. It is an emerging sector, the sooner one gains knowledge on these algorithms. The better they can perform the tasks that involve complex problems. Having an in-depth knowledge of these algorithms is very helpful to enhance one’s career.
