As artificial intelligence (AI) and machine learning (ML) reshape industries and redefine possibilities, mastering the art of AI and ML model development and evaluation has never been more crucial. Behind every powerful AI or ML model lies strategic decisions, intricate algorithms and critical evaluations. Within the Model and Evaluate phases of the Columbus AI Innovation Lab, we delve into these aspects and how to use diverse metrics to evaluate a machine learning model’s performance, along with its strengths and weaknesses.
To create an artificial intelligence (AI) solution that’s effective at solving your business challenges, you must first define valuable use cases for AI. Then, select AI use cases that would help your business succeed. The third step in making AI work for your business is transforming your data, and after that, it’s time to train and evaluate your AI/machine learning model, which we’ll cover here.
Model evaluation plays a pivotal role in assessing a model’s efficacy and in achieving the defined business goal. A statistical or machine learning model’s performance and efficacy are measured quantitatively using evaluation metrics. These metrics aid in comparing various models or algorithms and offer information on how well the model is working.
Although ML models are becoming more popular, it is essential to understand that ML is not a panacea for all issues. A ML model is a graphical representation of an algorithm that sorts through enormous amounts of data to find trends or make predictions. AI’s data-driven mathematical algorithms are machine learning models. As AI analyzes business use cases, the model evolves based on behaviors.
Only some business challenges require ML techniques to solve them. For example, if you can compute a value using simple arithmetic operations that can be programmed, you do not need machine learning.
The most frequent but resolvable problem with machine learning is a lack of data. Collecting data from your business or obtaining data from open sources is typically not sufficient for generating data for machine learning. Synthetic data is the cheapest and the most convenient data generation type for machine learning. Unlike authentic data collected from real-world events such as customer purchases, consumer feedback or reviews, synthetic data, whether manually generated or created through computer algorithms, enables secure testing of your project by generating data that might not even exist. For example, Google's Waymo uses synthetic data to train its self-driving cars.
Machine learning can be used with non-tabular data but requires some data manipulation. For example, data from various sources like sensors, web crawlers and near-infrared detection instruments can be transformed into a tabular representation by extracting windowed features using common statistical metrics (mean, median, standard deviation, skewness, kurtosis, etc.). The features can then be used with traditional machine learning techniques.
Writing and running machine learning algorithms to produce an ML model is the central component of the ML workflow. A data science team typically uses the model engineering pipeline, which consists of several procedures, such as model testing, model evaluation and model packaging, to create the final model.
You can streamline these activities in several ways. For example, you can automate the machine learning model training process by building a pipeline, which makes it simpler to scale the solution to larger datasets and maintain and update the model over time.
It is important to understand how crucial and interconnected the stages of model training, evaluation and testing are in the machine-learning workflow. Model creation is followed by model training, assessment of the model’s performance on a different dataset and testing of the model on fresh or previously unexplored data. Since this process is iterative, it may be necessary to repeat model training several times before the model’s performance on the testing data is acceptable.
The model training phase includes these steps/actions:
The effectiveness and accuracy of machine learning models are also influenced by feature engineering, which is the method of converting unprocessed data into features appropriate for machine learning models. To create more precise and effective machine learning models, the most pertinent features from the given data are chosen, extracted and transformed. Users must provide the correct data that the algorithms can understand for machine algorithms to function successfully. This input data is changed by feature engineering into a single aggregated form that is best for machine learning.
For instance, in a model predicting the price of a certain house, the outcome variable is the data showing the actual price. The predictor variables are the data showing such things as the size of the house, number of bedrooms and location — features thought to determine the value of the home.
Feature engineering use cases:
The two methods used for ML model training are:
Finding the best settings for hyperparameters is a crucial part of training a model and tuning them can improve model performance. Hyperparameters can significantly affect a machine-learning model’s performance. Here are a few typical training approaches.
Machine learning can be applied to promote products and services, identify cybersecurity breaches, enable self-driving cars and more to save costs, manage risks and enhance overall quality of life. Machine learning is becoming more commonplace every day and will soon be incorporated into many aspects of daily life due to increased availability of data and computing capacity.
Model evaluation measures how well a trained machine learning model works to make sure it meets the original business objectives. The goal of model evaluation is to assess a model’s ability to predict outcomes correctly and to pinpoint areas for improvement. To assess a model, a variety of methods can be applied, such as:
It is critical to remember that the evaluation procedure should be customized to the unique aspects of the business objective and the dataset. Different evaluation metrics might be more suitable across various models. To assess a ML model and make sure that its performance is understood, it is typical to use a combination of methods.
Model testing in machine learning is the process of assessing how well a trained model performs on a collection of data that it has never seen before. Model testing is done to determine how well a model generalizes to new, unforeseen data and to predict how well it will work in practice.
To prevent bias, the testing set should not be used in the training process and should be an accurate representation of the real-world data that the model will meet. To provide a statistically significant evaluation of the model’s performance, the testing set must be big enough. Decisions about the model’s suitability for deployment in the actual world are then made based on the outcomes of the model testing stage.
Model packaging is the practice of combining a trained machine learning model with its related pre- and post-processing steps, configuration files and other essential resources into a unique package that can be readily distributed, deployed and used by others. The trained model can be shared and used in various contexts, such as cloud-based or on-premises systems, thanks to model packaging in the machine learning workflow. Depending on the unique requirements of the use case, a model can be packaged using a variety of techniques.
In this blog, we touched upon model training, evaluation and testing. The features of ML testing include the need to verify the quality of the data as well as the model and the need to iteratively tune the hyperparameters to achieve the best results. You can be certain of its performance if you follow all the steps outlined.
The stages of Deploy and Support in the Columbus AI Innovation Lab will be covered in the last blog in our AI blog series, "How to deploy and support trained AI and ML models."