Classification Of Machine Learning Models

Machine Learning space has been explored for a while now but moreover, there are many aspects of it which can still be implemented in many profitable ways. In order to achieve this, the basic understanding needs to clear to people especially for the ones who come from a non-technical background. This article is drawn to help them in the understanding of Machine Learning.

Founders and business owners would want to collaborate with a top mobile app development company so as to improvise their products with ML and deliver better solutions to their clients or customers. Let’s move forward and get you acquainted with either familiar or unfamiliar terms i.e, supervised and unsupervised algorithms which seem to be confusing for most of the people. Furthermore, we will also see another source of confusion that is ‘reinforced learning’. As it is utmost necessary to understand these concepts rather than get stuck and tend to get mix these types of problems/algorithms.

Without waiting for further ado, let’s categorically understand the basic 3 models of machine learning i.e, supervised, unsupervised and reinforced learning.

Supervised Algorithms:

Supervised learning based models are labelled datasets collected in advance. Models will then call upon the knowledge from these datasets. These data sets are called as training dataset. The acquired knowledge is then implemented to apply and assign a probability value against the unseen datasets.

Let’s understand supervised learning from an example. Let’s say you have a 1000 user base and need to predict who they will cancel their subscriptions. As you know that the output of your model is already defined and that is, “will X amount of user(s) cancel their subscription”. What you need to figure out from the model in which the user will cancel the subscription. The 1000 user base is your training datasets and with that existing set of data, you need to create, build and train a model that can predict this particular aspect about your users. During the training of the model, part of the datasets is used to ‘learn’ and the other half is used to ‘validate’ the accuracy of the model. From the training datasets, build a model of let’s say 400 who are still using the product and 400 who have already cancelled the subscription. And run the same trained model on the rest of the users left out, i.e, on 200 users. These 200 users are unseen datasets for your model and will predict the status of the users. Based on the model output data result, you can calculate the accuracy of the model.

Some supervised learning algorithms:

- Linear and logistic regression

- Support vector machine

- Naive Bayes

- Neural network

- Gradient boosting

- Classification trees and random forest

 

Supervised learning is often used for expert systems in image recognition, speech recognition, forecasting, and in some specific business domain (Targeting, Financial analysis, etc)

Unsupervised Algorithm:

Supervised learning tasks find patterns where we have a dataset of “right answers” to learn from. Unsupervised learning tasks find patterns where we don’t. This is mostly because with the unsupervised learning you don’t know what you want to get output from the model. Due to the complexity of data, the best you probably try to get out is in the form by deriving some kind of relation between the data. To do this, you’ll need to bring the datasets to a common platform or in a format to work which get you some meaning to it. The model will present you with various ways of a pattern of datasets but then again it is up to you to bring something new with the data presented.

Reinforced Learning:

The algorithms which do not fit the supervised, as well as unsupervised, are the ones fall under Reinforced Learning.RL observes and identifies the problem area and applies techniques to improve the solution. In Reinforcement Learning, the model isn’t provided with the input-output datasets but instead, it is given a method to measure its performance via a reward signal.

In a way, RL tries to maximize these rewards: with the machine trying different things and is rewarded when excelled at something.

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