How Does Machine Learning Work | TrendSpider Learning Center (2024)

11 mins read

Machine learning is transforming how we interact with technology allowing computers to learn from data and improve their performance over time. Unlike traditional programming where specific instructions are coded to perform tasks machine learning leverages algorithms to identify patterns make decisions and predict outcomes autonomously.

This powerful approach is at the heart of many modern innovations from recommendation systems and speech recognition to financial forecasting and trading. Understanding how machine learning works provides insight into the mechanisms driving these intelligent systems highlighting the blend of data algorithms and computational power that fuels their success.

Data Collection

Data collection is a critical step in the machine learning pipeline as the quality and quantity of data directly affect the performance of machine learning models. It involves gathering raw data from various sources from a variety of channels both structured and unstructured to build a robust dataset for training and evaluation. Here are some key sources for acquiring data:

A. Databases (Structured)

• Relational Databases (RDBMS): These include MySQL PostgreSQL Oracle and SQL Server which store data in structured tables with predefined schemas. They are commonly used for business applications customer relationship management (CRM) systems and transactional data.
• NoSQL Databases: These include MongoDB Cassandra and Redis which store data in flexible formats such as key-value pairs documents or graphs. They are suitable for handling large volumes of unstructured or semi-structured data.

B. Data Warehouses and Data Lakes (Structured)

• Data Warehouses: Central repositories like Amazon Redshift Google BigQuery and Snowflake designed for analytical querying and reporting. They store structured data from various sources optimized for read-heavy operations.
• Data Lakes: Storage repositories like Amazon S3 and Hadoop that hold vast amounts of raw data in its native format including structured semi-structured and unstructured data. They support large-scale data processing and analytics.

C. APIs (Structured)

Many organizations and services provide APIs to access their data. Examples include social media APIs (Twitter Facebook) financial APIs (Alpha Vantage Quandl) and e-commerce APIs (Amazon eBay). These APIs allow for automated data retrieval in real-time or batch modes.

D. Web Scraping (Unstructured)

Web scraping involves extracting data from websites. Tools and libraries such as BeautifulSoup Scrapy and Selenium are commonly used to scrape HTML content images and other media. This method can gather data from news sites blogs e-commerce sites and more.

E. Government & Open Data Portals (Unstructured)

Many governments and organizations provide open access to datasets. Examples include data.gov (U.S.) data.europa.eu (EU) and the World Bank Open Data. These portals offer datasets on demographics economics environment and more.

Data Labeling

Once the data is acquired it needs to be labeled especially for supervised learning tasks (Whang 2019). Data labeling can be done manually by human annotators or through automated methods. Manual labeling though accurate is time-consuming and expensive. To address this several automated and semi-automated labeling techniques are employed:

A. Crowdsourcing

Platforms like Amazon Mechanical Turk enable large-scale data labeling by distributing tasks to a crowd of workers. Techniques like majority voting and quality control measures help ensure the reliability of the labeled data.

B. Active Learning

This approach focuses on selecting the most informative examples for labeling. Techniques like uncertainty sampling and query-by-committee help prioritize which data points should be labeled to maximize the learning efficiency of the model.

C. Weak Supervision

Data programming as implemented in systems like Snorkel generates large amounts of weak labels using multiple labeling functions. These labels though not perfectly accurate are sufficient when used in large quantities to train robust machine-learning models.

Data Preprocessing

Improving the quality of data is essential for building effective machine-learning models. Once the data is collected it must be pre-processed to ensure its quality and suitability for training the model (Whang 2019). Techniques like data cleaning and re-labeling help enhance the accuracy and reliability of the dataset:

A. Data Cleaning

Cleaning the data involves removing duplicates correcting errors handling missing data and more. Systems like HoloClean use quality rules value correlations and reference data to repair and clean datasets. This ensures that the data fed into machine learning models is as accurate and consistent as possible.

B. Re-labeling

Improving the quality of existing labels through techniques like repeated labeling and using high-quality annotators can significantly enhance model performance. Studies have shown that increasing label quality is more beneficial than merely increasing the quantity of labeled data.

Choosing the Right Algorithm

After preparing the data the next step is selecting machine-learning algorithm(s). There are various algorithms to choose from such as linear regression decision trees and neural networks. However simply learning about the different types of machine learning algorithms is not enough to understand how to choose the one that best fits your specific purpose. Some factors must be considered before you narrow your selection.

A. Expected Output

Each machine learning algorithm is designed to address specific problems. Therefore it is important to consider the type of project you’re working on. Ask yourself: What kind of output do you need? If you need predictions based on historical data supervised learning algorithms (e.g. – Random Forests Support Vector Machines Neural Networks) are the way to go. If you require an image recognition model that can handle poor-quality photos consider using dimensionality reduction combined with classification. For teaching a model to play a new game a reinforcement learning algorithm will be your best choice.

B. Size of Data

Different machine learning algorithms are tailored for varying data set sizes. Some algorithms perform better with large data sets while others are more efficient with smaller ones. For instance support vector machines (SVMs) excel with large datasets due to their ability to handle high-dimensional spaces and their robust performance with numerous data points. Conversely algorithms like K-means clustering are more suitable for smaller datasets. K-means clustering can quickly converge and find optimal clusters without requiring extensive computational resources making it ideal for smaller-scale data analysis.

C. Performance

Consider the speed and accuracy of the algorithm. Some algorithms are faster and more efficient while others may be more accurate but take longer to run. Determine which is more important for your specific needs and choose accordingly.

Training the Model

Once an algorithm has been selected the next crucial step is to train it using the prepared data (the dataset that has been processed and refined in the previous stages). Training a machine learning algorithm is a delicate balance of feeding it data adjusting internal parameters and ensuring that the trained algorithm (model) is neither too complex nor too simple.

A. Avoiding Overfitting & Underfitting

During the training phase it is essential to strike a balance to prevent overfitting and underfitting. Overfitting occurs when the model learns the training data too well including its noise and outliers leading to excellent performance on the training data but poor generalization to new unseen data. This means the model becomes too complex and tailored to the specific training dataset failing to perform well in real-world scenarios. Techniques to prevent overfitting include cross-validation pruning in decision trees and regularization methods such as L1 and L2 regularization.

On the other hand underfitting happens when the model is too simple to capture the underlying patterns in the data resulting in poor performance on both the training data and new data. This indicates that the model has not learned enough from the data often due to an overly simplistic algorithm or insufficient training. To avoid underfitting one can use more complex models ensure adequate training time and provide sufficient and relevant features to the model.

B. Performance Monitoring

Effective training requires monitoring the model’s performance continuously and adjusting hyperparameters accordingly. This process often involves splitting the data into training and validation sets. The training set is used to train the model while the validation set helps tune the model and check for overfitting or underfitting by providing a separate dataset to evaluate performance during the training process.

Evaluating the Model

Performance evaluation in machine learning is a critical step to assess how well a machine learning model performs. Key metrics such as accuracy precision recall F-score and the Area Under the ROC Curve (AUC) are used to asses the performance. Each measure provides insights into different aspects of the model’s performance and suitability for specific tasks.

However a single aggregated measurement such as predictive accuracy is insufficient to accurately reflect the performance of a machine learning algorithm. Instead multiple evaluation measures such as ROC analysis precision recall F-score and AUC are necessary to capture the nuances of model performance. It is also to be noted that the accuracy F-score and AUC each metric assume different use cases and can lead to misleading conclusions if not properly contextualized. It is advocated for a more explicit and transparent approach where the choice of evaluation measures (metrics) is clearly linked to the experimental objectives. (Flach 2019).

Hyperparameter Optimization

Hyperparameter optimization (HPO) is a crucial process in machine learning that involves tuning hyperparameters to achieve optimal model performance. Hyperparameters are settings that define the architecture of a machine-learning model and control the learning process. Unlike model parameters which are learned during training hyperparameters are set before the training process begins.

The primary objective of HPO is to identify the best hyperparameter configuration that maximizes or minimizes a predefined objective function such as accuracy or error rate. This process is essential because the choice of hyperparameters significantly affects the performance of the model.

A. Grid Search & Random Search

Grid Search and Random Search are basic methods for finding the best hyperparameters for a machine-learning model.

Grid Search works by trying every possible combination of hyperparameters in a specified range. Imagine you have a grid or table and you systematically test each point in the grid to see which combination works best. While this method is thorough it can be very slow and require a lot of computing power especially if there are many hyperparameters or if the range of values to test is large.

Random Search on the other hand does not test every single combination. Instead it picks random combinations of hyperparameters to try. This method is faster and often just as effective as Grid Search because it explores a broader range of possibilities without getting bogged down by systematically testing every single option.

Both methods are straightforward and easy to understand but they can be inefficient and time-consuming when dealing with a lot of hyperparameters or large datasets (Yang 2022).

B. Gradient-Based Optimization

Gradient-Based Optimization is a method that uses the gradients (or slopes) of the objective function to iteratively adjust hyperparameters. Think of it like climbing a hill where you use the steepness of the slope to decide your next step to reach the top faster.

This technique works well for hyperparameters that can take on a range of continuous values like learning rates or regularization strengths. It uses the direction of the steepest ascent (or descent) to find the best values efficiently.

However this method has limitations with hyperparameters that are discrete (like the number of layers in a neural network) or categorical (like choosing between different types of algorithms). Since these types of hyperparameters do not have a smooth gradient it is harder for the optimization process to make effective adjustments (Yang 2022).

C. Bayesian Optimization

Bayesian Optimization is a smart probabilistic method for finding the best hyperparameters. Instead of trying out hyperparameters randomly or exhaustively it uses past results to make informed guesses about which hyperparameters might work best next. It balances exploration (trying new hyperparameters) and exploitation (refining known good hyperparameters).

Here’s how it works:

• Surrogate Model: It creates a simple model called a surrogate model to approximate the objective function (how well the model performs with certain hyperparameters).
• Informed Guesses: Based on this surrogate model Bayesian Optimization makes educated guesses about the most promising hyperparameters to test next focusing on those likely to improve performance.

D. Multi-fidelity Optimization

Multi-fidelity Optimization is a method that dynamically allocates resources to evaluate hyperparameters focusing more on the promising ones as the process continues. This makes it particularly efficient for large-scale problems where fully evaluating every possible hyperparameter configuration would be too costly and time-consuming.

Here’s how it works:

• Dynamic Allocation: Instead of evaluating all hyperparameters fully right from the start this method begins with a rough evaluation of many configurations using fewer resources.
• Focus on Promising Hyperparameters: As the optimization progresses it identifies which hyperparameters show the most promise and allocates more resources to evaluate these further gradually eliminating less promising ones.

E. Metaheuristic Algorithms

Metaheuristic Algorithms are advanced methods that use strategies inspired by natural processes to find the best hyperparameters for a machine learning model. These algorithms are highly flexible and can handle a wide range of hyperparameter types and complex search spaces.

Here’s how two popular metaheuristic algorithms work:

• Genetic Algorithms: This method mimics the process of natural selection. It starts with a group of hyperparameter configurations (called a population). Each configuration is evaluated for performance. The best-performing configurations are then combined and modified (like breeding) to create a new generation of configurations. This process repeats gradually improving the hyperparameters over successive generations.
• Particle Swarm Optimization: Inspired by the behavior of birds flocking or fish schooling this method involves a group of candidate solutions (called particles) moving around the hyperparameter space. Each particle adjusts its position based on its own experience and the experience of neighboring particles converging on the best solutions over time.

Making Predictions

Once the model is trained and optimized it’s ready to make predictions on new data. This process involves several steps to ensure that the model can effectively generalize and provide accurate predictions. Firstly the trained model which has learned patterns and relationships from the training data is presented with new unseen data. This new data is fed into the model in the same format as the training data ensuring consistency and allowing the model to apply its learned parameters effectively.

The model processes this new data through its algorithm applying the patterns and relationships it has learned during training. This involves using the model’s internal parameters such as weights in a neural network or decision rules in a decision tree to evaluate the new input data and generate predictions.

Moreover it is crucial to continually monitor the model’s performance on new data to ensure it remains accurate and reliable. This involves checking the predictions against actual outcomes when they become available and adjusting the model as necessary. This ongoing evaluation helps to identify any degradation in model performance over time which can be addressed through retraining the model with new data or fine-tuning its parameters.

Example: A stock market prediction model is trained on historical stock price data and optimized for accuracy. Once deployed the model is used to predict future stock prices based on new data. The model’s output is used to determine whether to invest in a particular stock or not. The model is continuously monitored and updated to ensure its performance remains high allowing it to make more accurate predictions over time.

Deployment and Monitoring

The final step in the machine learning workflow is deploying the model into a production environment where it can make real-time predictions. This stage involves integrating the trained model into the systems and workflows where it will be used operationally. The deployment process must ensure that the model can handle real-world data inputs and deliver predictions promptly.

Once deployed the model begins to interact with live data providing valuable insights and predictions that drive decision-making processes. For instance in an e-commerce setting the model might predict customer preferences in real time to personalize recommendations or in a financial context it might assess transaction risks instantly to prevent fraud.

Continuous monitoring is a critical aspect of maintaining the model’s performance and accuracy over time. Several factors necessitate this ongoing vigilance:

I. Data Drift

The data used in real-time predictions may gradually change from the data the model was trained on a phenomenon known as data drift. Continuous monitoring helps detect these changes early ensuring that the model remains relevant and accurate.

II. Model Degradation

Over time the model’s performance may degrade due to various reasons such as evolving data patterns or changes in the underlying relationships within the data. Regularly assessing the model’s performance metrics can identify signs of degradation prompting timely intervention.

III. Scalability and Efficiency

Monitoring helps ensure that the model scales effectively under different loads and remains efficient in terms of response times and resource utilization. This is particularly important for applications requiring high throughput or low-latency predictions.

To maintain the model’s effectiveness it may be necessary to retrain it with new data. Retraining involves updating the model’s parameters by incorporating recent data that reflects current trends and patterns. This process can be automated or scheduled at regular intervals depending on the application and the rate at which the data changes.

In addition to retraining it may also be necessary to fine-tune the model’s hyperparameters or even redesign parts of the model to adapt to new challenges or opportunities. For example introducing new features or removing obsolete ones can significantly enhance the model’s predictive power.

The Bottom Line

Machine learning transforms technology by enabling computers to learn from data and improve over time without explicit programming. It relies on algorithms to identify patterns make decisions and predict outcomes driving innovations like recommendation systems speech recognition and financial forecasting. The process begins with data collection from various structured and unstructured sources followed by data labeling and preprocessing to ensure quality and reliability. Choosing the right algorithm based on the problem and data size is crucial after which the model is trained avoiding overfitting and underfitting.

After training the model’s performance is evaluated using multiple metrics and hyperparameter optimization fine-tunes the settings for optimal results. The model is then ready to make predictions on new data and it is deployed into a production environment for real-time use. Continuous monitoring ensures the model remains accurate and effective with regular retraining and adjustments as necessary. In stock trading for instance machine learning models can predict future stock prices based on historical data with ongoing updates to maintain performance.