In a nutshell:
- Machine learning can empower organizations to make informed decisions and gain a competitive edge.
- Pecan AI offers a simple 5-step process to go from zero to hero in machine learning.
- Steps include understanding data, defining the problem, building the model, training, and deploying it.
- Features like data collection, preprocessing, model selection, and parameter tuning are crucial.
- Low-code platforms like Pecan AI can accelerate ML projects and empower teams to build predictive models efficiently.
Machine learning empowers organizations to make informed decisions and gain a competitive edge. However, the process of building, training, and deploying a machine learning model can often seem daunting, especially for data professionals who may not have an advanced background in data science. Being hard isn’t the same as being impossible, though, and we're here to help.
In this post, we'll outline a simple 5-step process to help you go from zero to hero in the world of machine learning. Leveraging a low-code platform like Pecan AI can accelerate your ML projects and empower your teams to build predictive models more efficiently.
From understanding the data and defining the problem statement to deploying the model into production systems, we'll show you how to navigate each step with confidence.
Understanding Your Data and Defining the Problem Statement
Before discussing the mechanics of building a machine learning model, we need to review how to best develop an understanding of the underlying data and define the problem statement.
This critical step lays the foundation for our entire machine-learning journey and ensures that the subsequent steps will be effective and meaningful.
Data Collection and Preprocessing
Data is the lifeblood of any machine learning model. The quality, quantity, and relevance of the collected data directly impact the model's ultimate performance.
Data can come from various sources such as databases, spreadsheets, text files, online sources, and more. All data collection needs to follow a methodical and strategic approach to ensure the relevance and usefulness of the data. This involves identifying the data sources most likely to provide information pertinent to the problem at hand. It could be internal sources within your organization or external sources.
For instance, if you're trying to build a predictive model for customer churn, you might need to gather data from your customer relationship management system, sales databases, customer support logs, and social media feeds.
Once the data is collected, preprocessing steps are needed to prepare the data for machine learning algorithms. This stage includes activities such as cleaning the data (handling missing values or outliers), normalizing the data (ensuring a common scale for all features), and encoding categorical features (transforming nominal variables into a format that ML algorithms can understand). By doing this, your data will be in a usable form that can benefit your model.
It’s also a good idea to consider the privacy and legal aspects associated with data collection. Your process must comply with all relevant data protection regulations and ethical guidelines. Ignoring these considerations might result in legal consequences and damage to your organization's reputation, not to mention the risk of having to backtrack with your model and waste precious time.
Defining the Problem and Setting Objectives for the Model
With your data in order, you’ll have to define the problem you’re trying to solve. This definition should include the goal of the model or what prediction you aim to make the features that will contribute to this prediction, and the form of the output (a binary classification, a number, a category, etc.). (Hint: Pecan's Predictive Chat can help you with this process!)
Defining your problem also involves identifying and understanding the constraints and limitations you might face. These could be related to the available resources – such as time, budget, or data accessibility – the restrictions imposed by regulatory bodies, the organization's strategic priorities, or even the technical infrastructure available for model deployment.
Understanding these constraints early on can greatly influence the choice of machine learning methods and techniques to be employed, the complexity of the model that can be deployed, and the expectations about the timeline and the possible outcomes of the project.
Remember, the goal is to formulate a clear, concise problem statement that everyone on the team can understand. This would enable your team to set appropriate objectives for your machine learning model and determine the metrics you will use to evaluate its performance. That makes the problem easier to understand and, naturally, easier to solve.
Building the Machine Learning Model
Now that we have collected and cleaned our data, as well as defined our problem statement, it's time to start building the machine learning model. This occurs over a few steps, and several decisions are made along the way.
Here’s the basic framework for how to build your ML model in just five easy steps:
1. Choosing the Model Type
Not all machine learning models are the same, so deciding which will fit your data and problem statement best is the first step. Depending on the problem statement and the type of data you have, you can either opt for a regression or a classification model.
Regression models are used when you want to predict a number, such as the price of a house or the sales revenue for the next quarter. They’re particularly effective when a straight-line relationship exists between the input and output variables. They work by predicting a continuous output variable based on one or more input variables. For example, in a real estate context, you might use a multiple regression model that considers variables such as location, size, and age of the property to predict the price.
Classification models, however, are used when you want to predict a class or a label. Just like regression models, classification models have practical implementations in many fields, such as whether an email is spam or whether a customer will churn or stay. Different types of classification models, like logistic regression, decision trees, or support vector machines, allow you to fine-tune the model as needed to best fit your data and objectives.
(Again, if you're using Pecan — you don't have to worry about this step. Our automated platform will make this decision for you based on your predictive question and your data!)
2. Selecting an Algorithm
Choosing the right algorithm for your task determines whether your model will succeed or fail once put into use. This choice depends on the relationship between your features and the target variable, the size and quality of your dataset, and the computational resources at your disposal.
Algorithms can range from simple linear regression to more complex ones like decision trees, support vector machines, or deep learning algorithms. Each of these is best suited for different tasks.
A basic algorithm like linear regression might suffice if you work with a small dataset or a relatively simple problem. On the other hand, if you're dealing with a large dataset, complex relationships, or a high-stakes prediction, you might need to consider more advanced algorithms.
Keep in mind, though, that there tends to be an inverse relationship between the complexity of your models and their interpretability. Finding the balance between the detail and accuracy of your model and how easy it is to interpret and actually put into practice is the key to success.
Pecan's platform will automatically find the right algorithms to test for your specific predictive question — no fuss required.
3. Feature Selection
Feature selection is the process of picking the most relevant features – columns in your dataset – that will contribute to the accuracy of the model.
Often, datasets can have many features, but not all of them will contribute significantly to the predictive power of your model. Some features might be strongly correlated with others, leading to redundancy, while others might contain noise that can confuse the model. Feature selection helps you reduce the dimensionality of your data, making your model simpler, faster, and less prone to overfitting.
Several techniques can be used for selection, including filter methods (such as variance threshold and correlation coefficient), wrapper methods (like recursive feature elimination), and embedded methods (like LASSO and ridge regression).
Applying these techniques lets you identify the key features that will contribute most effectively to your machine-learning model and trim the rest.
Once again, Pecan's platform takes care of this step behind the scenes, automatically engineering dozens or hundreds of features with your data and then selecting those of greatest predictive value in your model.
4. Parameter Tuning
Before training your model, you’ll need to fine-tune the parameters of your chosen algorithm. Each algorithm has a set of parameters that can be tweaked to optimize performance. This process, known as hyperparameter tuning, involves adjusting the settings that control the learning process.
The primary aim of hyperparameter tuning is to find the sweet spot between underfitting and overfitting. Underfitting occurs when the model is too simple to capture the underlying structure of the data, leading to poor performance.
Overfitting happens when the model is too complex and ends up learning the noise in the data, causing it to perform poorly on unseen data.
There are several strategies for hyperparameter tuning, including grid search, random search, and gradient-based optimization, so make sure to experiment with all your options to find the best settings for your model.
After model training in Pecan, you'll be able to examine your model's risk of overfitting/underfitting, so no need to worry about this step.
5. Model Training
Once you’ve chosen your features and tuned the parameters, you can start training your model. Model training involves showing your model examples of the input data and the corresponding correct output so that it can learn the relationship between them.
To train your model efficiently and with high precision, you should choose an appropriate training method, with some of the best options being supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.
In supervised learning, the model is trained on a labeled dataset that provides both the input and the desired output. This allows the model to learn the relationship between the input and the output data and predict the output for new input data.
The opposite of this is unsupervised learning, where the model needs to find patterns and structures in the input data on its own. This method is often used for clustering and association tasks.
Semi-supervised learning is somewhere in between these two. The model is given a small amount of labeled data and a large amount of unlabeled data. The labeled data guides the learning process while the model learns to classify and predict the unlabeled data.
You can also try reinforcement learning, which involves an agent (in this case, the model) learning to make decisions by taking actions in an environment. The agent receives rewards or penalties based on the quality of its decisions and learns to maximize the total reward over time.
Training and Evaluating the Model
Your model training will vary based on your data and needs (as covered previously), but there are a few things you can do that are universal across basically every method. To make the most of your training, try:
Splitting the Data into Training and Testing Sets
It’s essential to split your dataset into training and testing sets. The training set is used to teach your model, and the testing set is used to evaluate its performance on unseen data. A common split is 80% for training and 20% for testing, but this can vary depending on your specific needs and the size of your dataset.
Training the Model and Evaluating Performance Metrics
After training your model on the training set, you evaluate it using the test set. You measure its performance using appropriate metrics like accuracy, precision, recall, and F1-score for classification tasks or mean absolute error, mean squared error, and R-squared for regression tasks. Based on the results, you can repeat training and testing until the metrics improve to your liking.
Deploying the Model
All the training in the world won’t matter if you never get to use your model. This is deployment; there’s even more to know once you get to this step. Make sure to consider:
Integrating the Model into Production Systems
Integrating the model into your organization’s production systems means it can start making real-world predictions. This step will likely involve your IT or software engineering team to ensure the smooth integration of the model with existing infrastructure.
Monitoring and Updating the Deployed Model
After deployment, you need to monitor the model's performance in real time and update it as necessary. This lets you keep your model relevant as new data comes in and the business environment changes.
Empowering Teams With Low-Code ML
Even if your team doesn't have deep technical expertise, you can still participate in ML projects with a low-code AI platform like Pecan AI. This enables all team members to contribute to model building and share their findings with others.
Low-code platforms also empower non-technical team members to participate actively in ML projects, fostering a culture of data-driven decision-making. All in all, it’s a recipe for success that doesn’t require nearly as much technical knowledge.
Build, Train, and Deploy Your MLM Today
Designing a machine learning model and putting it through the deployment process can seem daunting, but the reality is far simpler. As long as you follow the five simple steps outlined here – and with a little help from a low-code ML platform like Pecan – you’ll be able to build, train, and deploy a machine-learning model in no time.
Join us for a quick chat to experience firsthand the power of low-code machine learning. We'll show you how it can transform your organization's decision-making process today.
