5 Machine Learning Models Every Data Scientist Should Know

Here’s a great article from The Crazy Programmer

Whether you’re aware of it or not, you’re surely using artificial intelligence (AI) on a daily basis. That’s because it powers-up a lot of the most commonly used products today. From Google and Spotify to Siri and Facebook, all of them use Machine Learning (ML), one of AI’s subsets.

ML allows those sites to serve better and more personalized content, automate processes, and constantly optimize its workings, among other things. It does so through sophisticated algorithms called models. These are mathematical expressions that stand in as a representation of data in a particular context.

As such, models are essential to analyze the information and get insights out of it. Maybe you’re trying to start a career as a data scientist or want to outsource development of an ML-based application. Whatever your motivation, you’ve come to the right place to learn the basics of the most popular machine learning models.

Two Categories, a Multitude of Possibilities

Depending on the approach you want to take to process the data, you’ll use one of the 2 main categories of machine learning: supervised and unsupervised machine learning.

Supervised machine learning techniques use known inputs and outputs to identify patterns and understand how the results came to be. It uses training sessions and data sets to comprehend the underlying mechanism of the information. Thus, whenever you introduce a new input that wasn’t part of the training data set, you can get a probable outcome calculated with the identified pattern.

ML developers and software outsourcing companies that focus on AI use supervised machine learning in fields as diverse as chemistry, manufacturing, and marketing.

Unsupervised machine learning, on their part, is a more exploratory approach to data analysis. Instead of understanding data relationships from training examples, it uses unlabeled data to detect potential patterns you didn’t previously know. It does so by grouping similar inputs together based on their traits.

A lot of engineers use this approach in different industries. It’s especially useful for research, though you can use it to get insights from your sales department, your product pricing, or your logistics.

The possibilities you can find in both approaches are endless, as you can use them in any way you can imagine. Of course, before you launch yourself to do that, you have to know some of the basic models you have available.

5 Machine Learning Models Every Data Scientist Should Know

1. Classification

This method is a part of the supervised machine learning category. Its basic goal is to explain or predict a class value. In other words, this model defines the probability of something happening according to one or more inputs.

For example, you can use classification in an email client to filter spam. In this scenario, you have 2 possible outcomes: an email is spam or it isn’t. Depending on the inputs, the model could predict that based on how you trained it. In fact, you’re doing just that whenever you flag a message as spam in your email account – you’re training the model to understand the basic traits of spam and enhance its protection.

In short, classification is a method that predicts a class label depending on the training set and its values (which defines the class labels in the first place). This technique encompasses several models, including logistic regression, decision trees, random forests, multilayer perceptrons, and gradient-boosted trees, among others.

2. Clustering

Clustering includes several methods that are part of the unsupervised machine learning category. As such, you can use it on unlabeled data sets to group values according to one or more specific traits or characteristics. The result? The algorithms form groups (called clusters) of similar values.

For that to happen, you need to define a similarity measure, which is simply a metric that looks at one or more features. Once you have this measure, you can apply it to the data set to have clusters. For instance, you could have a lot of music albums that you could categorize by genre, by decade, or by country. Each of these similarity measures would offer different clusters and insights, so it’ll be up to you to define which one works best.

Clustering algorithms such as noise-based application density-based spatial clustering (DBSCAN), cluster hierarchical clustering, and medium-shift clustering, among others, are some of the options you can choose from. Several sectors and activities use them for things such as market segmentation, social network analysis, and medical imagery.

3. Regression

Regression is another method that’s part of supervised machine learning. With it, you use previous data to predict or explain a real or continuous value (such as prices or salaries). Its simplest form is linear regression which is usually more approximate than more complex forms like polynomial regression or neural networks).

Regression techniques begin with a hypothesis, which is a function based on input values and unknown parameters. When training an algorithm to tackle regression, you have to use a data set that allows the algorithm to refine its approach to the hidden parameters. After you refine the results, you can take the process to a real data set to apply your hypothesis.

An approach like this one is useful for things like estimating the value of a house. By combining different input values (such as square footage, age of the building, energy consumption, etc.), you could predict how much it could cost in the future, after renovations, or with any variation on those inputs whatsoever.

4. Dimensionality Reduction

This is another method of supervised machine learning. You should use it to reduce the noise in your data sets, which can get so big that sometimes you might end up processing a lot of useless or redundant data. With dimensionality reduction, you get rid of some of the unwanted information by integrating similar data in larger groups that reduce the amount of detail.

Think of it like this. Imagine you have a market segmentation of a vast majority of 30-year-old women. You could reduce the size of your data set by ignoring the information coming from men from ages that are above or below 30. You’d be losing some data, sure, but the losses would be acceptable enough for the resulting insights.

There are several methods you could use to apply dimensional reduction, including popular ones like principal component analysis and t-stochastic incorporation of the neighbor (t-SNE). These approaches can be linear or non-linear and apply different logic to the reduction. So, you’d better consider the best one according to your data and personal needs.

5. Ensemble Methods

This approach combines several supervised machine learning predictive models into one to refine the resulting predictions. It’s the whole “the strength of the wolf is the pack” kind of perspective that lies beneath the ensemble method approach.

Using different models can lead you to better results as they combine their strengths to reduce the weaknesses you’d find if you used them separately. Besides, the combination reduces the bias and variance of the learning model, which leads to fewer inaccuracies.

You should know that ensemble methods typically require more computation than a single model, so some people see them as a way to compensate for poor learning algorithms by way of computational processing. However, they excel in specific tasks such as face recognition, malware detection, and land mapping.

Closing Comments

Don’t believe for a second that machine learning models end in these 5. There are other powerful models, including deep learning algorithms that are all the rage now. However, learning about the basics might open the door for you to understand the complex world of artificial intelligence in general.

Needless to say, you’ll need a lot of knowledge to create one of these algorithms on your own – let alone building an accurate one. So, in case you are in need of one for your business, you have 2 paths. Either outsource the development of your model or sit down and start learning.

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