Machine learning can be overwhelming with its variety of tasks. Most tasks can be solved with a few ML algorithms. You need to be aware of which algorithms to select, when to apply them, what parameters to take into consideration, and how to test them. This guide was crafted to provide you with a straightforward and actionable solution to the issue at hand.
Choosing the right model for production is important. Not all algorithms should be used for every problem, even though we have many performance metrics to evaluate models. It’s time-consuming and labor-intensive. Selecting the right algorithm is important.
Data governance strategy and features are important when choosing a machine learning algorithm. ML models work with various datasets that differ in data points and features, depending on the use case. Model selection depends on how the model handles different dataset sizes.
Neural networks work well with big data and many features. SVM can handle limited features. Consider data size and feature count when choosing an algorithm.
What is a machine learning algorithm?
Let’s start with the basics in case you’re unsure about what this is and why you might need it. We’ll discuss machine learning and its algorithms. Skip to the guide on choosing ML algorithms if you already know this.
Learning algorithms examine data to recognize patterns and formulate hypotheses. ML algorithms are computers that have been trained. These are the different classifications of ML algorithms, which can be broken down into three and a half groups.
Humanity generates increasing amounts of data daily. It comes from many places, like business data, social media, IoT sensors, and more. ML algorithms turn data into useful information for automation, personalization, and complex predictions.
ML algorithms specialize in specific tasks based on the data features and project requirements. Principal categories of machine learning algorithms, along with illustrative examples for various kinds of jobs.
Steps to choosing the right machine learning algorithm
Define the problem
Define the problem you want to solve with machine learning. Business objective, target audience, current situation, desired outcome? How to measure success and evaluate performance? Assumptions, risks, and constraints impacting the problem? Define the problem, scope the project, identify data sources and features, and choose machine learning techniques.
Understand the data
Next, understand your data and the data needed for your machine learning project. Data is crucial for machine learning. It should be relevant, reliable, and representative of your problem domain. Clean and preprocess the data for machine learning. Perform descriptive and inferential stats, visualize data, check for missing values, outliers, and anomalies. Consider the ethical and legal implications of data usage and sharing.
Build the model
Build the model to learn from data and make predictions or recommendations. Choose the correct algorithm, architecture, and parameters for your machine learning task. Split data into training, validation, and test sets. Apply cross-validation, regularization, and other techniques to avoid overfitting or underfitting. Use libraries and frameworks to simplify and speed up model building.
Evaluate the model
Evaluate the model’s performance on the test set and new data. Choose the right metrics for your machine learning task and business goal, like accuracy, precision, recall, F1-score, ROC curve, or others. Compare models with baseline models, analyze errors, biases, and uncertainties. Consider the interpretability, explainability, and fairness of the model and its impact on stakeholder trust and satisfaction.
Deploy the model
Deploy the model into production for customer use. Ensure the model is scalable, robust, secure, maintainable, and can handle real-world data and scenarios. Monitor and update the model regularly. Collect feedback and metrics on its impact on business goals and outcomes. Use tools and platforms for ML model deployment and management.
Communicate the results
Step six: Communicate results to stakeholders. Tell a story that shows the value and benefits of your machine learning solution, and how it solves the problem and meets the business goals. Present data, model, and evaluation clearly and engagingly with visualizations, charts, tables, and other elements. Address limitations, challenges, and improvement opportunities for your machine learning project.
Types of machine learning algorithms
- Supervised learning
- Unsupervised learning
- Reinforcement learning
Supervised learning
Supervised learning involves human guidance to teach the algorithm. Supervised learning needs known outputs and labeled training data. If output is real, it’s regression. If the output is from a limited number of unordered values, it’s classification.
Unsupervised learning
Unsupervised machine learning is aligned with true artificial intelligence, as it allows computers to learn complex processes and patterns without human guidance. Less info about objects, train set unlabeled. Objects can be grouped based on similarities. Some objects can be anomalies.
Reinforcement learning
Reinforcement learning is about algorithms that learn to achieve goals or maximize outcomes over many steps. Maximize points won in a game. Supervised learning uses labeled training data, while reinforcement learning does not have labeled data. Without training data, it learns from experience.
Commonly used machine learning algorithms
1-Linear Regression
Linear regression summarizes relationships between two continuous variables. X is the independent variable. Y is the dependent variable. Linear regression uses X to predict y, while multiple regression uses multiple X variables to predict y based on a loss function like MSE or MAE. Use a regression algorithm to predict future values of a running process. This algorithm works well with thousands of features, like bags of words or n-grams in natural language processing. Complex algorithms overfit features, while linear regression is decent. Unstable if features are redundant.
2-Logistic Regression
Don’t mix up classification algorithms with regression methods. Logistic regression does binary classification with binary label outputs. Logistic regression is a special case of linear regression for categorical output variables, using a log of odds as the dependent variable. What’s great about logistic regression? It combines features and applies a sigmoid function, like a small neural network.
4-KNN
- They have different goals. K-nearest neighbors is a classification algorithm in supervised learning. K-means is a clustering algorithm in unsupervised learning.
- To assign positions to football players in a new dataset without positions but with measurements, we can use K-nearest neighbors.
- For football player grouping based on similarity, we can use K-means. The K in each case means different things.
- K-nearest neighbors uses K to represent the number of neighbors involved in determining a new player’s position. Example: K=5. To assign a position to a new football player, we select the five players with measurements closest to the new player and have them vote on the position.
- K-means uses K to determine the number of clusters. If K=7, I’ll have seven clusters of football players after running the algorithm on my dataset. Two algorithms with different purposes, but both using K, can be confusing.
Tips for choosing the right algorithm for your business
- Identify your goals: Identify your goals to choose the right algorithm. What’s your goal with machine learning? What do you want to do with the data? Understanding goals helps narrow options and choose suitable algorithms.
- Consider the type and quality of your data: Consider data type and quality when choosing an algorithm. Some algorithms need labels, others don’t. Consider data size, complexity, and missing/corrupted points.
- Evaluate the available algorithms: Evaluate algorithms once you understand your goals and data. Online resources can help you compare algorithms and their pros and cons.
- Test and evaluate: Test and evaluate algorithms to find the best performer for your data. Use cross-validation and holdout sets to test algorithm accuracy and performance and compare results.
- Consider scalability: Consider algorithm scalability. Choose an algorithm that can handle large data or future expansion.
Conclusion
Selecting the right machine learning algorithm for your business can greatly impact the success of your project. Follow these blog post tips to choose an algorithm that meets your needs and goals. The right algorithm can maximize machine learning and business success.
This guide simplifies machine learning algorithms for decision-making. Choosing the right model is crucial as not all algorithms are suitable for every problem. Data and features are important for algorithm selection. NNs are good for big data and many features, SVM is good for limited features.
