Models in Machine Learning for Decision Making

What is Machine Learning?

You’ve probably heard before about machine learning but let’s quickly remind what ML actually is. It’s an application of artificial intelligence that learns from experience without direct programming.

There are some crucial machine learning models that we’ll delve into exploring today. Ready to find out? Keep reading!

What are Machine Learning models?

The machine learning model is a file that is developed to recognize specific types of patterns. The model is trained over a set of data and provided with an algorithm to learn over and over from the training data.

Machine learning models can be divided into three categories: supervised learning, unsupervised learning, and reinforcement learning. Over 70% of machine learning is supervised learning while unsupervised reaches up to 20%. The rest is reinforcement learning.[1]

#1. Supervised Learning

In supervised learning, the labeled data is used for the training data and directed into successful processing. When the model is trained with the known data, the unknown data can be put into the model and you can receive a new response.

The real-life example of supervised learning is when you receive a file of photos with information about what is on them. In the next steps, you train the model to recognize the photos. The machine is trained to classify something into some class.

There are a few major algorithms used for supervised learning:

  • KNN
  • Linear Regression
  • Logistic regression
  • Naïve Bayes
  • CART

#2. Unsupervised Learning

Now, let’s move on to explaining what unsupervised learning is. It helps in finding all kinds of unknown patterns in data. The training data there is unlabeled and unknown, so the input can’t be navigated to the algorithm. The trained model searches for the pattern and gives a welcome response.

You can describe unsupervised learning by having molecules, where part of them are drugs and the others are not. You don’t know which are which, so you’d like the algorithm to discover the drugs from the molecules. The machine learns through observation and finds structures in data.

From the various unsupervised learning algorithms, you can distinguish some main ones:

  • the Apriori algorithm
  • K-Means
  • SVD
  • PCA

#3. Reinforcement Learning

Last but not least, reinforcement learning. In this type of machine learning model, the algorithm explores data through a trial and error process. Then it chooses the action that leads up to higher rewards. Reinforcement learning has three components: the agent, who is the learner or decision-maker, the environment, everything the agent interacts with and actions, what the agent can do. There is also a reward, which is a reinforcement signal for behavior.

You can describe how reinforcement learning works by example with the dog. Your dog is “an agent” exposed to the “environment”, which is your house. The agent reacts by performing “an action”, which is the transition from standing to sitting, for example. After the transition, the agent either receives a reward or penalty in return. The strategy is to choose an action in expectation of better outcomes.

As for the reinforcement learning models, there are two principal ones:

  1. Q learning
  2. Markov Decision Process.

Supervised learning, unsupervised learning, and reinforcement learning are the most widely used ML models.[2]

You might find it interesting – Machine Learning Models

How to choose the right Machine Learning model

How to choose the right Machine Learning model?

It can be tough to choose the desirable machine learning model. There are a few steps that you should take in order to make the right decision. First, you should categorize the problem. It will help you determine where to categorize the issue.

Next, understand your data. You need to learn how to derive insights to perform better decisions. Understanding the role of data is the key to choosing the right algorithm for the problem. After categorizing the problem and understanding the data you should find the available algorithms. The algorithms should be relevant and practical to implement.

Now it’s time to actually implement machine learning algorithms. The most efficient solution is to use a service, which compares the performance of algorithms once new data is added. The last step is to optimize hyperparameters. You can do so by grid searching, random searching, and Bayesian optimization. Completing all the steps enables you to perform the most rational decision.[3]

Which Machine Learning model is the best?

So now you recognize the ML models and you’d like to know which is the best. Actually, it depends on the different characteristics of data. It can be the quality of data, outliers, feature engineering, or the volume of available data. The preferred action is to start with the simplest model and introducing more complex ones by parameter tuning and cross-validation.

Choosing the right machine learning model for a certain use case is significant to receive the desired result of a machine learning task. If you’re unsure which model to use, hit us up! Our experts will gladly help you develop the best machine learning solution!



About the Author!

Edwin Lisowski – Experienced Information Management Consultant with a demonstrated history of working in the information technology and services industry. Skilled in Data Warehousing, Business Intelligence, Big Data, Cloud Computing and Advanced Analytics.

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