How to Choose Right Machine Learning Algorithm?

Machine Learning is the field of study that gives computers the capability to learn without being explicitly programmed. ML is one of the most exciting technologies that one would have ever come across. A machine-learning algorithm is a program with a particular manner of altering its own parameters, given responses on the past predictions of the data set.

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 :

1. Understand Your Problem : Begin by gaining a deep understanding on the problem you are trying to solve. What is your goal? What is the problem all about classification, regression , clustering, or something else? What kind of data you are working with?
2. Process the Data: Ensure that your data is in the right format for your chosen algorithm. Process and prepare your data by cleaning, Clustering, Regression.
3. Exploration of Data: Conduct data analysis to gain insights into your data. Visualizations and statistics helps you to understand the relationships within your data.
4. Metrics Evaluation: Decide on the metrics that will measure the success of model. You must choose the metric that should align with your problem.
5. Simple models: One should begin with the simple easy-to-learn algorithms. For classification, try regression, decision tree. Simple model provides a baseline for comparison.
6. Use Multiple Algorithms: Try to use multiple algorithms to check that one performs on your dataset. That may include:
   • Decision Trees
   • Gradient Boosting(XGBoost, LightGBM)
   • Random Forest
   • k-Neasrest Neighbors(KNN)
   • Naive Bayes
   • Support Vector Machines(SVM)
   • Neural Networks(Deep Learning)
7. Hyperparameter Tuning: Grid Search and Random Search can helps with adjusting parameters choose algorithm that find best combination.
8. Cross- Validation: Use cross- validation to get assess the performance of your models. This helps prevent overfiting .
9. Comparing Results: Evaluate the models's performance by using the metrics evaluation. Compare their performance and choose that best one that align with problem's goal.
10. Consider Model Complexity: Balance complexity of model and their performance. Compare their performance and choose that one best algorithm to generalize better.

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.