Understanding ML Models: Types, Challenges, & Considerations
Our advent with technology truly began when we started using computers to help us with complicated and large numerical problems. Today, the humble computer has gone beyond what it was originally set out to do and can now use data to make intelligent business decisions without explicit programming.
This can be attributed to Artificial Intelligence (AI), which uses Machine Learning (ML) models to do these things that were originally perceived to be the realm of humans.
Businesses, too, are using AI in more ways than one, and the global machine learning platforms market size is projected to be at $31.36 billion by 2028. This entire revolution is powered by simple ML models, a computational framework that enables these remarkable possibilities for AI.
To understand ML models, let us look at their types, challenges, and ML model selection considerations so you can truly harness their power efficiently.
Understanding Machine Learning Models
ML Models are algorithms that analyze vast amounts of data, identifying patterns, relationships, and correlations. This process allows the models to extract knowledge from the data and apply it to new, unseen data, making them capable of performing tasks such as classification, regression, clustering, and anomaly detection.
ML models are tested or trained on large and diverse datasets, which allows them to recognize patterns and make intelligence choices beyond that of an average human.
While ML model testing helps make the output more refined and accurate, ML models also have the ability to generalize effectively. This means the model can accurately make decisions on new data, even if this was not part of the original data that was used to train the system.
Generalization capability is essential for real-world applications, where systems must perform reliably with constantly flowing and new databases.
Types of Machine Learning Models
ML Models can be broadly categorized into four main types: supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.
Each category employs different techniques to extract knowledge from data and address specific types of problems.
Here is a look at each ML model type in greater detail:
Supervised Learning Models
In supervised learning, the model is trained using a labelled dataset, where each data point has an output value or corresponding label. The model learns to map the input data points to their respective labels, enabling it to make predictions for unlabeled, new data.
This has its limitations, as the ML model follows a fixed pattern to analyze data. For any new data points or data in varied formats, this model may face challenges.
Example: If we want to predict housing prices in a particular area, we can deploy a supervised learning model. Using data on housing prices, house size, the number of rooms, amenities in the area, and other features, we can define how housing prices are fixed for each locality and train the model. This model can then be used to predict housing prices for each area in a city.
Unsupervised Learning Models
Unsupervised learning, as the name suggests, deals with unlabeled data, where the goal is to uncover hidden patterns, groupings, or structures within the data.
The model learns to identify underlying relationships and patterns without explicit guidance, which is beneficial to ensure that the ML truly learns and isn't just fed the answers.
Example: It is possible to detect any outliers in a database using unsupervised learning. This can be used in the logistics and transportation industry for anomaly detection in the logistics process. The clustering algorithm can help group activities and processes with similar traits, helping to identify logistical obstacles or expose gaps in the process.
Semi-Supervised Learning Models
Semi-supervised learning combines elements of both supervised and unsupervised learning. In this approach, the model is trained on a dataset containing both labeled and unlabeled data, leveraging the labeled data for explicit guidance while still extracting patterns from the unlabeled portion.
This approach is beneficial when acquiring labeled data is expensive or time-consuming.
Example: Semi-supervised learning models can be used for speech recognition. Facebook (now Meta) has used this model to train its speech recognition model, using a base model that was trained with 100 hours of human-annotated audio data.
Later, 500 hours of unlabelled speech data were added, and the model was self-trained to do this task. The result - the word error rate (WER) decreased by 33.9%, with the lowered need for manual intervention.
Reinforcement Learning Models
Reinforcement learning differs from supervised and unsupervised learning in that it involves an interactive agent interacting with an environment. The agent receives rewards or penalties for its actions, and it learns through trial and error to maximize its cumulative reward.
Reinforcement learning is well-suited for problems where the optimal action sequence depends on the current state of the environment and the agent's past actions.
Example: In gaming environments, reinforcement learning can be applied to teach an agent to play and optimize strategies in games like chess or video games. The model receives rewards or penalties based on its actions, learning to make optimal decisions through trial and error.
Challenges of the ML Models
Despite their remarkable capabilities, ML models are not without flaws, and one needs to work on these flaws for successful real-world implementation. These ML model challenges include:
Data Quality and Bias
The quality of the data used to train an ML model is paramount to its performance. Poor-quality data, such as noisy, incomplete, or biased data, can lead to inaccurate predictions and unreliable models.
Additionally, it is crucial to ensure that the data is representative of the real-world problem being addressed, as biased data can perpetuate existing biases and lead to unfair outcomes.
Overfitting and Underfitting
Overfitting happens when a model learns the training data way too well, memorizing the data points rather than generalizing it to new data. This can result in bad performance on unseen data.
Underfitting occurs when a model fails to capture the underlying patterns in the data, leading to poor performance on both training and testing data.
Explainability and Interpretability
As ML models become more complex, it becomes increasingly difficult to understand how they make decisions. This lack of explainability can make it challenging to trust the model's outputs, particularly in high-stakes applications where transparency is crucial.
Training and deploying ML models can be computationally expensive, requiring significant hardware resources and computational time. This can be a limiting factor, especially for resource-constrained environments.
Considerations in Model Selection
Selecting the appropriate ML model for a given task is a crucial decision that significantly impacts the project's success. To do this, you need to consider a few aspects, which include:
- Dataset Size: The size and quality of the available data play a critical role in model selection. For small datasets, simpler models may be more appropriate to avoid overfitting, while larger datasets may support more complex models.
- Model Complexity: The complexity of the model should be tailored to the specific task and the available computational resources. Simpler models are generally easier to interpret and less computationally expensive, while more complex models may achieve higher accuracy but require more data and computational resources.
- Interpretability: The interpretability of the model is essential for understanding its decision-making process and building trust in its outputs. Interpretable models are more transparent and easier to debug, making them preferable for applications where explainability is crucial.
- Computational Resources: The computational resources available for training and deploying the model must be considered. If resources are limited, simpler models or distributed computing strategies may be necessary.
- Domain Expertise: The expertise of the team developing and deploying the model is also an important factor. If domain expertise is limited, simpler models or off-the-shelf solutions may be more appropriate.
- Evaluation Metrics: The choice of evaluation metrics depends on the specific task and the desired outcome. Common metrics include accuracy, precision, recall, and F1-score.
- Cost-Benefit Analysis: The potential benefits of improved performance must be weighed against the costs of training, deploying, and maintaining the model. A cost-benefit analysis can help determine if the model is worth pursuing.
Case Study: ML Model For Detecting Financial Frauds
Financial institutions face the challenge of identifying fraudulent transactions in real time to protect their customers and prevent financial losses. ML models have emerged as powerful tools for fraud detection, analyzing vast amounts of transaction data to identify suspicious patterns and anomalies.
An example of this can be seen in implementing AI-based solutions for identification and fraud detection in check-processing times.
Banks risk losing millions annually due to errors in electronic payments and automated clearinghouse (ACH) transactions. Since the funds are made available to the depositors, it is crucial to identify counterfeit checks quickly.
To track counterfeits and detect any anomalies, the model was trained on a large dataset of historical transactions labeled as either fraudulent or legitimate. With each transaction, the accuracy of detection increases, helping the system to come close to near-human expertise.
This can result in <70 sec response time, processing 1200 checks per second. Meanwhile, banks benefit from a 50% reduction in fraudulent transactions and annual savings of $20 million on frauds, making the process not just faster but also more efficient.
ML models have revolutionized various industries, enabling computers to learn from data and make intelligent decisions. From supervised learning to reinforcement learning, these ML model types demonstrate remarkable capabilities in tackling complex tasks such as classification, prediction, and anomaly detection.
As AI continues to evolve, ML models will play an increasingly central role in shaping our world, driving innovation, and addressing critical challenges. To ensure that your ML projects aren’t just nurtured but supercharged, consider using MarkovML.
The platform includes features like No-Code Auto-EDA, Collaborative Reporting, and Intelligent Data Catalog, which simplify data analysis, speed up data evaluation, and make data organization easier, improving your overall ML project outcomes.