ML Algorithms and Models
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- Overview
Machine learning (ML) involves the use of ML algorithms and models. An algorithm in ML is the process that is run on the data to create a ML model. It is usually said to fit a dataset, which means that it is applied to the dataset.
A model in ML is the output of a ML algorithm run on the data. It represents what has been learned from the algorithm on the learning data and contains a set of specific features of the algorithm.
The model can be saved for later use and acts as a program, using the previously stored features of the algorithm to make new predictions. If the model has been effectively and adequately trained, it can be used to make more predictions on similar data with a certain level of accuracy and confidence.
Algorithms are methods or procedures for completing a task or solving a problem, whereas models are well-defined computations performed by an algorithm that takes a value or set of values as input and produces a value or set of values as output.
- ML Algorithms
ML algorithms are procedures that run on datasets to recognize patterns and rules. ML models are the output of the algorithm. Models act like a program that can be run on data to make predictions.
Simply put, a ML algorithm is like a recipe that allows computers to learn and predict based on data. Rather than explicitly telling the computer what to do, we feed it large amounts of data and let it discover patterns, relationships, and insights on its own.
ML algorithms are sets of rules or processes used by an AI system to perform tasks. These tasks often involve discovering new data insights and patterns, or predicting output values from a given set of input variables.
There are many different types of algorithms with many different functions and uses. There are three main ones:
- Regression: used to make predictions where the output is a continuous value, such as logistic regression.
- Classification: algorithms used to classify between categorical values.
- Clustering: used to group similar items or cluster data points, such as K-Means.
- ML Models
The ML model serves as the underlying core component of ML, representing the link between inputs and outputs to produce accurate and fresh data. It is trained on data sets to identify underlying patterns and produce accurate results.
After training, the ML model is tested to determine whether it can provide fresh and accurate data; if the test is successful, it is used in real-world applications.
Let us take an example to understand this further. You want to build a model that takes into account characteristics such as age, body mass index (BMI), and blood sugar levels to identify whether a person has diabetes.
We had to first compile a dataset of diabetes patients and related health indicators. The algorithm uses a dataset of diabetic patients and considers their health indicators to analyze patterns and relationships in the data and produce accurate results. It identifies potential relationships between outcomes (diabetes status) and input characteristics (blood glucose levels, BMI, and age).
After training, the model can use information such as blood sugar levels, weight and age to predict whether a new patient has diabetes.
- ML Algorithms vs ML Models
Machine learning (ML) algorithms are the brains behind any model, allowing machines to learn, making them smarter. The way these algorithms work is that they are fed an initial batch of data and, over time, additional data is fed as the algorithm improves its accuracy. This process of regularly exposing algorithms to new data and experiences increases the overall efficiency of the machine.
A ML algorithm refers to the mathematical procedure or set of rules used to analyze data and identify patterns, ML model is the concrete output of that algorithm, essentially a program that can make predictions based on the learned patterns from the data; in simpler terms, the algorithm is the recipe, and the model is the finished dish created using that recipe.
Key Differences between ML Algorithm and ML Model:
- Function: An ML algorithm defines the process for learning from data, while an model is the actual representation of that learned knowledge, ready to be used for predictions.
- Output: An algorithm produces a model as its output after being applied to data.
- Flexibility: Algorithms can be applied to different datasets, while a specific model is tailored to the data it was trained on.
Example:
- Algorithm: Linear regression - a mathematical formula that calculates the relationship between variables.
- Model: A specific linear regression equation with calculated coefficients, generated after training on a particular dataset.
- [ML Algorithms and Models]
- ML Model Training
Model training is a stage in the data science development lifecycle. It's the process of running a ML algorithm on a dataset, and then optimizing the algorithm to find certain patterns or outputs.
Model training involves learning good values for all the weights and bias from labeled examples. The resulting function with rules and data structures is called the trained ML model.
The process of training ML models can be divided into four steps:
- Data set split for training and evaluation
- Algorithm selection
- Hyperparameter tuning
- Model training
The model's performance during training will eventually determine how well it will work when it is eventually put into an application for the end-users.
Before training your model, you can:
- Identify the problem and candidate algorithms.
- Identify data required to train the algorithms.
- Collect initial data.
- Identify its quality and suitability for the task.
- Plan what is needed to make the dataset suitable for the project.
- How to Use ML Algorithms and Models
When a ML algorithm learns from data using one of the ML methods, it builds a ML model. The model is the result of running an algorithm on the data.
Once you have a model, you can use it to make new predictions on data or similar data sets. Depending on how effectively the algorithm is trained, the model will make predictions with a certain level of accuracy and confidence.
So, what do algorithms and models mean in the context of data science? The goal of ML is to build predictions that can be used to make data-driven decisions for your business.
To do this, you need ML models that can produce high-confidence predictions. The algorithm to produce a model with 90% accuracy is very simple. Training an algorithm to improve accuracy to 95% or higher can be very difficult. When making decisions based on data generated by ML models, a percentage increase in accuracy can make a huge difference.
When choosing a ML algorithm, you can consider:
- Your project goal
- Your data's size, processing, and annotation requirements
- The speed and training time
- Your data's linearity
- The number of features and parameters
To assess the performance of ML algorithms, it is essential to establish evaluation criteria. These criteria typically include accuracy, precision, recall, F1-score, training time, model complexity, and interpretability.
