Machine learning is an effective tool that can be used to make predictions. It is often seen as too complex for mere humans to understand.
Machine learning provides the self-driving car of business models. Technology companies can incorporate it’s capabilities into their products to provide significant competitive advantages.
The first thing that machine learning can do is find patterns in data. Machine Learning’s ability to spot these patterns is integral for making predictions.
***Example: what we’ll have for dinner tonight
What is Machine Learning
Before we get into how machine learning as a service works, let’s talk about the technology a little.
Machine learning is a broad field that covers techniques for systems to learn tasks without being explicitly programmed.
This can include everything from personalizing apps to making credit card fraud detection more accurate.
Machine learning has several forms, but the two most common are supervised and unsupervised learning.
Supervised machine learning requires that you have training data. This data identifies patterns from which systems can learn to make decisions or predictions in new situations.
The results of supervised learning can build predictive models, such as credit card and fraud detection systems.
Unsupervised machine learning does not require training data but is also not purely classification-based.
These models can work for a wide variety of tasks that focus on data association or prediction.
For complex problems such as image analysis and text classification, unsupervised learning is often more effective than the supervised model because it does not have access to most of the hypothesis space available for supervised learning.
Supervised Machine Learning: Scoring Recommendations
Machine learning is a supervised machine that is used to build a prediction model from training data.
This kind of machine learning aims to find a specific attribute for your system – the right hamburger, the right product for your website, or the correct price to charge for an airline ticket.
To use machine learning to construct a recommendation system, you must provide training data – data that has been labeled with instances of what customers like and what they do not like.
This guidance data can be obtained from experience or through testing. Two approaches can be used to build a recommendation system with machine learning: collaborative filtering and logistic regression.
It is known for building a recommendation system with machine learning, and it is still common in many contexts today.
The goal is to create parameterized models that can be learned from existing customers or users, much like you use to predict which candidate wins an election based on who has voted previously.
This method works by finding patterns from customer’s past behavior and then finding comparable customers for new users with similar values.
Example – similar for the customer (i.e., similar interests, like similar music) and similar for the user (i.e., similar past behavior, like similar website preference).
Logistic regression is a standard solution that combines both supervised and unsupervised learning techniques. It is based on logistic functions, a general form of hyperbolas—each parameter adjusts the curve for better prediction accuracy.
Using this type of curve, a model may detect if a number is more than or less than a particular threshold. The key value by dividing the data into value bins and evaluating how many times each sample fell into each bin.
The basic logistic regression model can be considered a classification-based model that uses predictions based on a threshold value.
It also is used as regression problems, in which the data is from past experiences and examples. A common form of logistic regression goes like this:
where y represents the dependent variable (i.e., prediction), c represents the independent variable (i.e., input), and b represents the output. If the value is less than or equal to zero, then the data falls into one of the c bins:
Suppose there are enough bins (typically 50). In that case, this model goes into a linear regression model, similar to using logistic regression but uses the actual value of y instead of just prediction.
This form can be used to build a recommendation system for pricing products or anything else with clear values and ranges.
Unsupervised Machine Learning: Generating Recommendations
Unsupervised machine learning tries to take a different approach for finding associations between data. To do this, you must rely on the data itself as the training data.
An unsupervised machine learning model looks for patterns in the data itself instead of in its labels. There are two common kinds of unsupervised models: clustering and association.
Clustering is a form of unsupervised machine learning designed to group similar items or attributes into clusters based on data similarity.
Unlike supervised learning, there is no expectation of prior data and no training data necessary.
Various clustering algorithms can be used to group items into different clusters: dendrometers, k-means, hierarchical clustering, and self-organizing maps.
Dendrometers are one of the simplest types of clustering algorithms. They create a hierarchical structure by finding all possible pairwise relationships between points on a graph, then assigning each end to the closest cluster based on these relationships.
This algorithm works best for small data sets with few clusters – fewer than five distinct groups of points.
However, it works best when the data is relatively dense, meaning more points than areas to put them. In this case, the algorithm can still be very flexible by simply placing similar items into one cluster and moving on to the next group of data.
K-means clustering assign points to clusters based on a specific number of groups (k) divided by the total number of points. This algorithm is simple but requires a bit more math than dendrometers, which can make it easy to mess up during implementation.
Hierarchical clustering is an advanced version of k-means clustering. This algorithm starts with individual data points and works its way up to a specific number of clusters, using basic distance metrics to group similar data.
This algorithm out performs than k-means because it can be used if there are more clusters than data points, or if the clustered data set is extensive.
However, it remains complex and requires a lot of prior knowledge about the dataset – what direction to go in and how the clusters should look.
In addition, there is no clear way of estimating the number of clusters in hierarchical clustering, so it is better to set the number of clusters first with an initial guess based on the statistical analysis used to determine this value.
Self-organizing maps (SOMs) are popular for natural language processing tasks like machine translation between different languages.
To create a good SOM, you need to know what you will map ahead of time and how many layers you will be using.
For example, if you want to use a three-layer map and have four countries as your mapped features, they would be divided into regions (e.g. southern Europe versus northern Europe).
You would then choose the number of clusters based on what kind of data you will use and how many regions you want to include. Then, you would put all of the points (i.e., language pairs) into each region that best fits its location.
Association-In Machine Learning
Suppose a machine learning model is trying to predict an association between two different items. In that case, it is called unsupervised machine learning.
The goal here is to find positive and negative relationships between data, which may not fit a strict label or category.
For example, suppose there were two strongly related items but not identical (e.g., Coke and Pepsi but not Coke and Diet Pepsi). In that case, the model could use that information to predict a possible third item. There are two forms of association models: correlation and covariance.
In correlation, one item is represented by a column in the dataset. In contrast, another item is modeled as the rest of the columns (i.e., it appears in each dataset row).
The model learns how strongly related these two items are based on their respective values across all rows in their columns.
This algorithm does not need any data beyond the rows; it can learn patterns using thousands or millions of rows and does not need to know what they mean.
Covariance is just an advanced version of correlation. This kind of algorithm can do the same thing as the simple version. It is easier to compare multiple columns at a time since you are not limited to one column. Still, it also normalizes data to compare values from different columns in the same row.
Note: You might notice that covariance is similar to metrics like Pearson’s r; however, they are not the same thing. Covariance uses every pair of data points in its calculations.
At the same time, Pearson’s r only calculates the relationship between two points directly instead of how other data affects these values.
How to Read Machine Learning Data
To do machine learning, you need to be able to read the data in a meaningful way. That means you must know how to make sense of the data and what it means.
This section will give you some understanding and tips on machine learning data.
Two common ways of representing machine learning data are feature vectors or table/array of values.
Feature vectors vs. table/array: Which is better?
Feature vectors are suitable for visually explaining your data because they highlight your dataset’s important parts without distorting any interpretation. They can also be plotted as a scatter plot to demonstrate relationships between variables.
For example, suppose you have two related columns in your dataset (e.g., high gas prices are related to the time of year). In that case, you might want to plot these two columns as two separate vectors instead of planning both of their values on one scatter plot.
However, feature vectors can be hard to understand and visualize because they contain many different numbers with different meanings.
For example, a 100-point vector indicating a temperature between 0°C and 30°C could be interpreted as a comparable scale for all temperature values (e.g., 70 is like -1, 86 is like 0, 105 is like 5). This makes reading and understanding a large set of feature vectors very difficult.
Table/array data, on the other hand, is much easier to understand. It breaks down each dataset value into different or similar-sized columns, making it easy to compare different values in one place (e.g., which countries have the lowest gas prices?). In addition, you can also use an array to interpret the data yourself through some simple arithmetic.
For example, if you had three columns that contained the same value (e.g. all of them were $1).00), you could average those values together in an array and then print them as a single number ($1.00/3 = 0.33333).
Machine learning is still in the early stages of development in today’s technology. The research will pave the way for what machine learning might look like tomorrow!
Be sure to keep an eye on this field because it may have some exciting applications that you weren’t thinking about before.