Machine learning is an application of artificial intelligence (AI) that allows systems to automatically learn and refine from that learning while not being programmed explicitly. In other words, the field emphasizes on learning – that is obtaining skills or knowledge from experience; this also means, synthesizing useful notions from historical records. As a practitioner in machine learning, you will encounter various types of learning field. So today, we will go over a few of the most common machine learning models used in practice today. We’ve already discussed the major difference between supervised Vs Unsupervised Learning in detail, let us dive into it shortly!
Supervised learning revolves around learning a function that draws an input to an output based on input-output pairs. This algorithm consists of a target variable that must be predicted from a given set of independent variables. We can generate a function that maps input to projected outputs by using the set of variables. The training process continues until the model attains its desired level of precision on the training data.
In unsupervised learning, we don’t have any target variable to predict. This algorithm maps inferences from datasets, containing input data without labeled responses. In unsupervised learning, the most frequently used method is cluster analysis – it is used for exploratory data analysis to find obscured grouping in data.
By using this algorithm, the machine is trained to make critical decisions, as it is subjected to a condition where it must train itself frequently via trial and error. The machine learns from previous experience and looks forward to absorbing the optimum knowledge to make appropriate business decisions.
Now, let’s have a look at some of the most essential machine learning models!
Based on supervised learning, linear regression performs regression tasks. It models a target prediction value based on independent variables and is primarily used for figuring out the relationship between variables and forecasting. Different regression models vary – based on the type of relationship between dependent and independent variables that they are considering, and the number of independent variables being used. Apart from that, linear regression is one of the most renowned and well-understood algorithms in statistics and machine learning.
Decision trees are used for both classification and regression tasks and lie in a non-parametric supervised learning category. They are popular models – used in strategic planning and machine learning and create a model that forecasts the value of a target variable by learning basic decision rules contingent from the data features. Decision trees are instinctive and quite easy to build however, they hit the skids when it comes to providing accurate results.
Random forest is an ensemble learning technique – a group of decision trees. This technique creates multiple decision trees via bootstrapped datasets of the original data and erratically selects a subset of variables at each phase of the decision tree. Then, it selects the mode of all of the forecasts of each decision tree and predicts the final output. If the number of trees in the forest is high, the output will be accurate and prevent the problem of overfitting.
This multi-layered model is inspired by the human brain as it untangles and disintegrates highly complex relationships between variables. What makes neural networks different from other machine-learning algorithms is that their architecture is inspired by the neurons in the brain. Just like a brain neuron, it receives input and based on that input, lets off an output – used by another neuron. Neuron network uses artificial intelligence and simulates this behavior in learning about synthesized data and predicts the outcome. Also, they are very complex and very mathematical.
Logistic regression was first used in the biological sciences in the early 20th century. But today, it is used when the dependent variable (target) is categorical. It widely scrutinizes and describes the connection between a binary response variable and a set of predictor variables. The main goal of logistic regression is to replicate the mean of the response variable; given a set of predictor variables. However, what differentiates logistic regression from linear regression is that the response variable is binary instead of perpetual.
Following are three types of logistic regression:
- Binary Logistic Regression
- Multinomial Logistic Regression
- Ordinal Logistic Regression
In a nutshell, there are heaps of complications when you turn to any specific model and in this blog, we have provided you the most important machine learning models used for practical purposes. Selecting the right model for a specific use case is crucial to get an accurate outcome of a machine learning task. Comparing the performance between different models, evaluation metrics or KPIs are distinct for certain business problems. So, go ahead and choose the best model for production after applying the statistical performance checking.