Machine Learning (ML) algorithms drive an organization’s automation process. These are essential for deriving artificial intelligence solutions and are used to increase business efficiency by making intelligent business decisions. Transforming business operations and successfully applying the automation strategy requires a good machine learning model. The machine learning model of an organization determines the direction the business is taking towards digital transformation.
Machine Learning Models
Here are the three types of machine learning models available to business organizations for finding automation solutions:
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
Supervised learning algorithms are mainly used in ML applications and software. The algorithms in a supervised learning model can map an input to output by deriving patterns between input-output pairs. This machine learning model uses structured algorithms that machines can identify quickly.
The supervised learning model can be further divided into two:
- Regression Model
- Classification Model
In this supervised learning model, the output is continuous and is used to find a fixed value using independent predictors. The regression model helps establish the relationship between dependent and independent variables. The four types of regression models are as follows:
- Linear Regression – In this regression model, the extensions can be multiple linear regression where a plane of best fit is established and polynomial regression where a curve of best fit is determined.
- Decision Tree – This regression model represents a tree with multiple branches leading to squares called nodes. The decision tree is known to be more accurate when there are more nodes. Thus, helping derive more possible solutions to the problem.
- Random Forest – Multiple decision trees combine to form a random forest regression model. Here, the best possible solution from each decision tree is selected, reducing the possibility of error from using the decision tree model only.
- Neural network – The neural network regression model is a multi-layer model that mimics the human neural system. This model contains an input and output layer with nodes and specific functions.
The output is discrete in this supervised learning model and is further classified into – Logistic Regression, Support Vector Machine, and Naïve Bayes.
In this machine learning model, unsupervised learning algorithms are used, where machines are free to identify patterns and derive meaning from them without any fixed structure. This model is great at discovering hidden patterns.
There are two methods of unsupervised learning; they are as follows:
- Clustering – This method helps group data and is best suited for classifying various data and detecting fraud.
- Dimensionality Reduction – This unsupervised learning method involves reducing the dimensions of a feature used for feature extraction or elimination.
The reinforcement machine learning model is based on the interaction of machines with the environment. As a result, the machine learns from the interaction without specific instructions. Thus, the reinforcement learning model provides machines the opportunity to choose or learn on their own. This machine learning model helps businesses generate results based on the trial-and-error method. Hence, better results will lead to better automation solutions.
Which ML Model is right for your business?
Finding the best machine learning model for your business requires analysis of the business needs and then deciding which model to use. Testing is the best way to decide on the right machine learning model. After testing a machine learning model, businesses can evaluate the results with pre-defined data set.
Our Machine Learning Consulting Services advisors assist businesses in finding the machine learning model that best suits their business purpose. They also help organizations leverage the best machine learning applications and software to find meaningful information that helps them expand their business and promote long-term growth.