/ Machine learning basics: Predictive analytics vs. Machine learning
Machine learning basics: Predictive analytics vs. Machine learning
Predictive analytics and machine learning are tools for problem solving, but they approach problems differently. This means each is appropriate for different types of tasks. Often machine learning is used as a tool of predictive analytics to provide companies with all manner of classifications, forecasting, and modeling.
What are predictive analytics and machine learning?
Predictive analytics predates machine learning with its origins in the 1940s. It uses statistical techniques, data mining, machine learning, and artificial intelligence to enable companies to anticipate trends and behaviors. Predictive analytics tools create models based on large historical data sets.
Machine learning is a newer, more agile tool for predictive analytics. Machine learning is the preferred tool for approaching most modeling because its learning feature allows it to fine tune models’ parameters to fit the data exactly. This saves a large amount of time and resources because companies don’t need data scientists to apply advanced methods or perform iterative tasks.
Why are predictive analytics and machine learning important?
Both predictive analytics and machine learning allow companies to predict future trends and consumer behaviors. Businesses can use them to make better predictions based on large volumes of data.
How do predictive analytics and machine learning work
Predictive analytics is based on the creation of a predictive model. Models use classifiers and detection theory to predict a probable outcome. Once applied to data, this mathematical function predicts problems, such as potential supply chain issues. For example, predictive analytics can help determine whether to increase production of a pharmaceutical based on consumer behavior.
Predictive analytics can include machine learning in its approach to modeling. Machine learning is a way to apply artificial intelligence. It begins with “training” a machine on data sets. Based on that data, the machine fine tunes parameters for the models and creates the best model for the problem.
Machine learning creates the most accurate models when machines are periodically retrained on updated data. Its statistical models then update automatically. Predictive modeling, on the other hand, requires data scientists to run the model manually.
Just as predictive analytics and machine learning have their own strengths, each has disadvantages. Predictive analytics’ accuracy relies on huge amounts of historical data. It needs all past trends and patterns to create a rigorous model.
Machine learning can create models with data sets as small as an Excel spreadsheet; however, the problem needs to be very descriptive to find the right algorithm.
When should you use predictive analytics vs. machine learning?
Machine learning is a tool of predictive analytics. It’s more flexible than most other predictive analytics tools because it is more adaptive, especially in the area of sentiment analysis.
Because machines automatically fine tune parameters, machine learning is more flexible than many forms of predictive analytics in its problem solving. Automated machine learning can use ensemble models to combine multiple models and improve results further.
Machine learning can solve any problem that predictive analytics can. Unlike machine learning, though, predictive analytics works with a specific audience in mind. It considers how consumers will interact with a product.
Machine learning is often used for:
Predictive analytics typically are used for:
How do different industries use predictive analytics and machine learning?
Predictive analytics and machine learning have a wide range of applications across industries. Because machine learning enables non-data scientists to create models, it is used in everything from marketing to security.
When it comes to the different types of machine learning, supervised learning and unsupervised learning play key roles. While supervised learning uses a set of input variables to predict the value of an output variable, unsupervised learning discovers patterns within data to better understand and identify like groups within a given dataset.
Supervised learning is the most common type of machine learning and is used by most machine learning algorithms. This type of learning, also known as inductive learning, includes regression and classification. Regression is when the variable to predict is numerical, whereas classification is when the variable to predict is categorical. For example, regression would use age to predict income, while classification would use age to predicate a category like making a specific purchase.
Within supervised learning, various algorithms are used, including:
Artificial neural networks
Unsupervised learning is useful when it comes to identifying structure in data. There are many situations when it can be near impossible to identify trends in data, and unsupervised learning is able to provide patterns in data which helps to inform better insights. The common type of algorithm used in unsupervised learning is K-Means or clustering.
What is there to consider when it comes to machine learning?
With machine learning, you want to understand the basics, and you also want to be aware of the algorithms that underpin machine learning. To get started, you want to:
Collect and prepare data
Choose a training model or algorithm
Evaluate a model
Domo has created a Machine Learning playbook that anyone can use to properly prepare data, run a model in a ready-made environment, and visualize it back in Domo to simplify and streamline this process. Since building and choosing a model can be time-consuming, there is also automated machine learning (AutoML) to consider. AutoML helps to pre-process data, choose a model, and hyperparameter tune.
Domo’s ETL tools, which are built into the solution, help integrate, clean, and transform data — one of the most challenging parts of the data-to-analyzation process.
There are countless opportunities for machine learning to grow and evolve with time. Improvements in unsupervised learning algorithms will most likely be seen contributing to more accurate analysis, which will inform better insights. Since machine learning currently helps companies understand consumers’ preferences, more marketing teams are beginning to adopt artificial intelligence and machine learning to continue to improve their personalization strategies. Additionally, machine learning and deep learning are going to evolve. For instance, with the continual advancements in natural language processing (NLP), search systems can now understand different kinds of searches and provide more accurate answers. All in all, machine learning is only going to get better with time, helping to support growth and increase business outcomes.
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