A Beginner's Guide to Evaluating Machine Learning Models

Automation and machine learning have come to dominate the data science industry, and most big companies in the science and technology industries require data science professionals to be skilled in these areas. Data scientists use programming languages like Python to develop machine learning models that automate data cleaning, web scraping (extracting and collecting data), and recommendation systems. Beginner data scientists not only use web scraping and data mining for research and projects but also to further their training or find jobs, internships, and freelance work. Additionally, experience in training, testing, and evaluating machine learning models is a must for data scientists looking to break into Big Tech. Beginner data scientists can learn how to evaluate and select machine learning models by practicing with data science tools and taking courses. 

Introduction to Evaluating Machine Learning Models

Machine learning models are files trained to iterate a specific processor to recognize patterns within a dataset. Before evaluating a machine learning model, data scientists must first develop, train, and test the model. This process includes feeding the model the data needed to make predictions or decisions. While some models are trained based on aggregated data, the machine extracts information and makes meaning of that dataset. In some instances, machine learning models influence decision-making such as training a model to organize or sort information. In addition, these models can be used for predictive analysis based on dataset patterns. 

Once the machine learning models are trained, they are tested, and their performance is evaluated. Generally, the evaluation of a model prioritizes three classification models: accuracy, precision, and recall. An accuracy test evaluates pattern-finding or simple-selection models and calculates how many of all the model’s predictions were correct. Precision tests evaluate decision-making or classification models, looking for how many of the model’s positives were true positives. Similarly, recall analyzes how well a model correctly identifies true positives when there is a mixture of true and false negatives.

Consequently, evaluating machine learning models relies on determining the reliability and validity of a model's decisions. Different evaluation methods are applied based on the project and the purpose of using a particular machine learning model. For example, evaluating models based on accuracy is especially useful for business intelligence. Data scientists and analysts use data to determine unseen patterns or trends in a dataset. Additionally, pairing a precision evaluation with a recall test fine-tunes a machine learning model. Therefore, it is common to see specific tests applied at different stages of model development or to the same dataset.

Why Data Scientists Use Machine Learning Models

Data patterns are not easily discoverable to the human eye, but they are to automation and machine learning models, helping data scientists work with big data. Through in-depth discernment, these models can be used to solve challenging business problems or make meaning out of data that may be too complex for data scientists and analysts to understand. It follows then that automation, machine learning, and artificial intelligence are prevalent in the data science industry. Students and beginner data scientists should be skilled in these areas to include them in their resume skills. 

Data scientists use machine learning models to complete various tasks, generally centered on data cleaning and wrangling, web scraping, data mining, and creating recommendation systems and artificial intelligence. Data cleaning and wrangling are considered tedious and time-consuming yet necessary aspects of working as a Data Scientist. For beginner data scientists, learning how to develop machine learning models will economize the time spent in these beginning stages of project development. 

Web scraping and data mining are also repetitive, time-consuming processes for data scientists. Web scraping involves programming a tool, like a web crawler, to collect information from websites. Data mining is a similar process in which a program collects and analyzes information from a dataset. Web scraping and data mining are essential to data collection and analysis for data scientists across fields, making it easier to glean insights from the information culled on a subject or idea. 

Data scientists working at big tech companies use machine learning models to develop recommendation systems and artificial intelligence. By developing digital platforms, mobile applications, and social media, data scientists use automation and machine learning models to train recommendation systems on how to select content. Data engineering and development positions involve teaching artificial intelligence and robots to make decisions and complete tasks, which also requires knowledge of machine learning tools. Therefore, developing and evaluating machine learning models is essential for beginner data scientists.

Interested in Machine Learning Models?

Noble Desktop offers a range of data science classes, including automation and machine learning. Noble Desktop's Python Classes teach students to test, train, and evaluate machine learning models. The Python for Data Science Bootcamp provides beginners with an introduction to the programming language and automated machine learning. More advanced students can take the Python Machine Learning Bootcamp, which covers the programming and evaluation of machine learning models.