The Most Important Types of Machine Learning Models

What does a model even mean in machine learning?

It is the actual equation or computation that is developed by applying sample data to the parameters of the algorithm. To continue the simple linear regression example, the model is the equation of the line of best fit of the x and y values in the sample set plotted against each other.

Machine-learning algorithms continue to grow and evolve. In most cases, however, algorithms tend to settle into one of two models for learning. The models exist to adjust automatically in some way to improve their operation or behavior.

There are 2 main learning models in machine learning:

1. Supervised Learning

Learning in the supervised model entails creating a function that can be trained by using a training data set, then applied to unseen data to meet some predictive performance. Supervised learning involves learning a function that maps an input to an output based on example input-output pairs

Supervised learning algorithms try to model relationships and dependencies between the target prediction output and the input features such that we can predict the output values for new data based on those relationships that it learned from the previous data sets.

There are generally two sub-categories of supervised learning:

• Regression
• Classification

Regression

A regression problem is when the output variable is a real or continuous value, such as “salary” or “weight”. Many different models can be used, the simplest is linear regression. It tries to fit data with the best hyper-plane which goes through the points.

This model includes algorithms like Linear Regression, Logistic Regression, etc.

Classification

A classification problem is when the output variable is a category, such as “red” or “blue” or “disease” and “no disease”. A classification model attempts to draw some conclusions from observed values. Given one or more inputs a classification model will try to predict the value of one or more outcomes.

This model includes algorithms like Naive Bayes, Random Forest, Decision Trees, etc.

2. Unsupervised Learning

Unsupervised learning is also a relatively simple learning model, but as the name suggests, it lacks a critique and has no way to measure its performance. In this model there’s no teacher at all, actually, the computer might be able to teach you new things after it learns patterns in data, these algorithms a particularly useful in cases where the human expert doesn’t know what to look for in the data. In contrast to supervised learning which usually makes use of human-labeled data, unsupervised learning, also known as self-organization allows for the modeling of probability densities over inputs.

The goal is to build a mapping function that categorizes the data into classes based on features hidden within the data.

There are generally two sub-categories of supervised learning:

• Clustering
• Dimensionality Reduction

Clustering

Clustering is a branch of machine learning that groups the data that has not been labeled, classified, or categorized. Instead of responding to feedback, clustering identifies commonalities in the data and reacts based on the presence or absence of such commonalities in each new piece of data. This approach helps detect anomalous data points that do not fit into either group.

This model includes algorithms like k-means, Hierarchical clustering, mean shift, etc.

Neural Networks

Artificial neural networks (ANN) are computing systems vaguely inspired by the biological neural networks that constitute animal brains. Such systems "learn" to perform tasks by considering examples, generally without being programmed with task-specific rules. The original goal of the ANN approach was to solve problems in the same way that a human brain would.

Oh! so you made it till the end. A little bonus video for you.

Conclusion: Choosing the Best Model

The model you choose for machine learning depends greatly on the question you are trying to answer or the problem you are trying to solve. Additional factors to consider include the type of data you are analyzing (categorical, numerical, or maybe a mixture of both) and how you plan on presenting your results to a larger audience.

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