10 Easy Yet Effective Rules of Machine Learning

Most people are very confused when they are trying to find out the meaning and rules of machine learning program algorithms while other IT professionals think machine learning program algorithms are basically set of the effective rules and protocols that are followed in machine learning.  Now it has to be noted that there is no fixed algorithm for machine learning. In simple terms, there is no fixed theorem known as no free lunch, as every algorithm is different for the different problem.
For example, one cannot say that the neural network performs better than decision trees or the other way around. Since everything is linked with each other like for instance the structure and size of a database are linked with both network and decision trees. Thus, for getting the favorable outcome, one should try out variable tests to select the winner. One should not directly jump to any conclusion. This article will enlighten the reader about some of the algorithm that has to be considered in machine learning. Some of them are:

Big Principle

This is the principle which states that machine learning can be defined in the terms of the target function. This means that input variables are a target function of the output variable.  Considering x as the input variable and y as the output variable then the target function would be Y=F(X). Thus it can be said that with the help of a big principle in machine learning it would help the system in making future predictions. The most common form for future prediction is through mapping a target function, meaning with the help of the equation Y=F(X) predictions of Y could be made keeping the variable x in consideration.  This type of mapping in general terms is known as predictive analysis or predictive modelling.

Linear Regression

Linear regression tends to form the foundation of machine learning. While big principle deals with only two variable x and y to make predictions, linear regression makes use of many different algorithms from various fields to drive a coefficient (B).  One of simplest example of this type of format can be expressed as y=B0+B1*X. Now it has to be noted that there is no fixed method to study linear regression. Some researchers say that linear regression can be learned by analyzing linear algebra solution while others say they can be easily learned by studying the optimization of gradient descent.

Logistic regression

Logistic regression is another algorithm to study machine learning. In logistic regression, the main goals are to find the worth of the coefficients by studying the weights on each input variable. Now this logistic regression is very different from linear regression as the as in linear regression the prediction is completely based on logistic function.

Linear discriminant analysis

This discrimination is based on two classes. Now if the problem requires discrimination of more than two classes then the linear classification technique is followed. For linear discrimination analysis, two things are calculated, first the variance of all classes and second the mean value of each class.

Classification of regression trees

Classification of regression trees is one of the modern ways of machine learning. For this model, a binary tree is considered where is each node is the representation of the split variable and the input variable.

Naïve Bayes

This is also a modern-day algorithm model in this case two probabilities are calculated. The first is calculated on the basis of each class and the second probability is calculated considering the value of x. once the calculation is done and the final outcome is given on the basis of Bayes theorem.

K Nearest Neighbors

In this algorithm, the prediction is given after calculating the entire set of similar instances (k) and then the output is provided for the same. Now with the help of Euclidean distance, a number is derived which basically shows the distance between the two input variables.

Learning Vector Quantization

With the help of learning vector Quantization, IT professionals can choose a variety of training instances. In learning vector quantization there is a presence of coded vectors. The IT professionals choose these codes randomly to determine the number of iterations in the training set.

Support vector machines

This algorithm takes hyperplane in consideration meaning a hyperplane is used to differentiate variables. The variables are separated on the basis on 1 and 0. With the use of this algorithm, IT professionals are able to find the coefficient of variables which separated by a hyperplane

Bagging and random forest

Machine learning training is one of the best ways to learn about bagging and random forest since machine learning training teaches the user about the benefits of using this algorithm. This algorithm uses the bagging method for calculating a data from a sample.

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