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Top 5 Machine Learning Algorithms

Both Data Scientists and Machine Learning Engineers can benefit from Machine Learning algorithms. Even there are som many machine learning courses available at online tutoring sites. A number of companies are operating algorithms based on machine learning. My observations of others in educational and professional settings are similar to mine, including both myself in developing similar algorithms. There are a few algorithms that I believe are the best in terms of Machine Learning, which I am sharing with you along with their use cases so that you can receive a refresher of your knowledge on these algorithms.  

Compilation of Top 5 machine learning algorithms: 

  • Linear regression 
  • Logistic regression 
  • Decision tree 
  • SVM algorithm 
  • Naive Bayes algorithm 

Linear Regression 

An analyst uses linear regression to predict or show the relationship between continuous variables and is a quiet and simple method for statistical analysis. Using the linear regression model, an independent variable (X-axis) and a dependent variable (Y-axis) shown to have a linear relationship, which is why it called linear regression. Such a linear regression known as simple linear regression if there only one input variable (x). There also cases where the linear regression called multiple linear regression if there more than one input variable used. Based on the linear regression model, the relationship between the variables described by a sloped straight line. 

If we were estimating an employee’s salary based on their years of experience, what would we do? There is a correlation between experience and salary based on your recent company data. An employee’s salary is a dependent variable, as it depends on the employee’s experience. In this example, the year of experience is an independent variable and the salary of an employee is an independent variable. Using current and past information, we can predict the future salary of an employee. 

Positive linear relationships can be positive or negative, and the regression line can be either positive or negative. 

Logistic Regression 

Using logistic regression, we can assign observations to discrete classes when a discrete set of observations is available. There are many different types of classification problems, including Email spam or not spam, Internet transactions fraudulent or not fraudulent, and tumors malignant or benign. A logistic sigmoid function used to transform the input into a probability value in logistic regression. Supervised Learning one of the main algorithms in Machine Learning, which also referred to as logistic regression. An indicator value calculated by taking a set of independent variables and predicting a categorical dependent variable. 

Based on a categorical dependent variable, logistic regression predicts its outcome. Therefore, the variable must have a discrete or categorical outcome. Rather than giving the exact value as 0 or 1, it provides probabilistic values that lie between 0 and 1. 

There is much in common between linear regression and logistic regression except for how they differ in their use. Regression problems solved using linear regression, and classification problems solved using logistic regression. 

Decision Tree 

This category of algorithms falls under the general heading of the Decision Tree algorithm, a supervised learning algorithm. Contrary to most other algorithms that can solve classification and regression problems, this algorithm also allows for the solution of classification and regression problems. 

In the context of such training models, a Decision Tree used to create a training model which used to learn simple decision rules that derived from previously collected data (training data) in order to predict the class or value of the target variable. 

For decision trees, we start from the root of the tree and work our way up to predict a class label for each new item. A record’s attribute value compared with that of the root attribute by comparing the attribute values of each record. On the basis of comparison, we follow the branch corresponding to that value and jump to the next node. 

Types of Decision Trees 

There are different types of decision trees based on the target variables. Generally, they fall into two categories: 

  1. Categorical Variable Decision Tree: When a decision tree has a categorical variable in its target, then it called a Categorical variable decision tree. 
  1. Continuous Variable Decision Tree: If the target variable in the decision tree is continuous, then it is called Continuous Variable Decision Tree. 

SVM algorithm 

The technique used for both classification and regression problems. Support Vector Machines one of the most popular supervised learning algorithms. However, it primarily used Machine Learning in Classification problems. 

SVM algorithm creates the best decision boundary to organize n-dimensional spaces into classes so that future data points can be easily sorted into the right category by following the line. Hyperplanes are logical boundaries for organizing n-dimensional spaces into classes. 

Hyperplanes are created using the extreme points/vectors selected by the SVM. Therefore, support vectors are termed support vector machines, as a result of their extreme cases.  

However, it is mostly used to solve classification problems. In the SVM algorithm, each data point is plotted as an n-dimensional point (where n is the number of features you have) with its value being determined by the coordinates. We then perform classification by finding the hyper-plane that provides a strong differentiation between the classes. 

Naive Bayes algorithm 

The Bayes’ Theorem is the basis for this classification technique, which assumes the predictors are independent. As a result, a Naive Bayes classifier assumes that any given feature in a class is not related to any other feature in the class. Students at universities receive assignments on algorithms, online homework help sites are a savior.

When some fruit is red, round, and about 3 inches in diameter, then it is considered to be an apple. The fact that all of these features contribute to the probability that this fruit is an apple, regardless of whether they depend on each other or on the presence of the other properties, is why it is called ‘Naive’. 

A Naive Bayes model can be quickly built and can be very useful when dealing with very large sets of data. Along with being simple, Naive Bayes actually outperforms even highly sophisticated classification methods when it comes to classification performance. 

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