Choose Right Machine Learning Algorithm

Choose Right Machine Learning Algorithm

In this article, we will be going to learn How we can choose the right Machine Learning Algorithm and where to use that correct algorithm....

Simple Steps to Choose Best Machine Learning Algorithm

Here is a step-by-step procedure to choose correct machine learning algorithm :...

Most used Machine Learning Algorithms

Linear Regression: It is essential in searching for the relationship between two continuous variables. One is an independent variable and other is the dependent variable. Logistic Regression: Logistic regression is one of the common methods to analyse the data and explain the relationship between one dependent binary variable and one or more independent variables of the nominal, ordinal, interval, or ratio level. KNN: KNN can be used for classification and regression predictive problems. K-means: K-means clustering is an unsupervised learning algorithm, which is used when we are dealing with the data which is not labelled(without proper categories or groups). The aim of the algorithm is to search the groups in the data set, with the number of groups being represented by the variable K. Support Vector Machines(SVM): It is a supervised machine learning algorithm which can be used for classification or regression tasks. It uses a technique called the kernel trick to transform your data and then based on these transformations it finds an optimal boundary between the possible outputs. Random Forest: It can be used for regression and classifications task. It results in greater accuracy. Random forest classifier can manage the missing values and hold the accuracy for a significant proportion of the data. If there are more number of trees, then it won’t permit the trees in the machine learning model that are overfitting....

Factors to Choose Correct Algorithm

The kind of model in use (problem) Analyzing the available Data (size of training set) The accuracy of the model Time taken to train the model (training time) Number of parameters Number of features Linearity...

Conclusion

By selecting the best machine learning algorithm for your problem is a crucial step in building effective predictive models. It involves a systematic approach that starts with understanding your problem, preprocessing your data, exploring the dataset, and selecting appropriate evaluation metrics....