Machine Learning & Algorithms

Introduction to Machine Learning

So far in the book, we have had an analyst at the heart of the data analysis. In a perfect environment, the analyst may go through the following steps:
  1. Step 1. Analyze data on a certain business problem. Let us say that the analyst is trying to predict which insurance customers will make a claim. The analyst will usually start with past data to see – perhaps through a variant of regression analysis – whether the chance of a customer making a claim can be explained using existing data.
  2. Step 2. Build a model for business predictions and decisions in the area. The analyst may use the analysis in Step 1 to build a predictive model that can be used to predict claims, based on other data (such as gender or age).
  3. Step 3. Evaluate the model against new data. As new claims come in, the analyst would evaluate how accurately his or her model is making predictions.
  4. Step 4. If necessary, make changes to the model and reapply. If the model seems to need elaboration or tweaking, the analyst may change the model to see if he or she can get better predictions of insurance claims than before.
  5. Step 5. Repeat Steps 3 and 4, if necessary, many times over, as you constantly seek better results.
However, increasingly, we are automating the serious thought and decision making, leaving the analysis to computers. The process seen above, when performed by a computer, would be one example of machine learning.
In the example above, we may automate analysis of the insurance datasets by telling the computer what analyses to apply to the data and what to look for (in this example, better prediction of claims).
The next section discusses a few types of machine learning.

Some Types of Machine Learning

There are two main types of machine learning.
  • Supervised learning algorithms are where the computer is told what outcomes to look out for, and the computer is given data and modeling inputs. The computer will compare its results to the real results, and increasingly train its modeling to get its results as close as possible to reality. The insurance example above – where the computer is ”told” that accuracy of predicting claims is what counts – is a good example.
  • Unsupervised learning is used when you give the computer data but no clues as to any particular output or aim. The computer algorithm is structured to find certain patterns in the data that may exist. For example, unstructured learning could be applied to Facebook posts of customers to help identify clusters of posts that tend to appear together.
There are other types of machine learning, including semi-supervised learning and reinforcement learning.
There are certain other techniques that are similar or related to machine learning. These include:
  • Data mining, as discussed earlier in the book, is designed to seek out previously unknown patterns in data.
  • Artificial neural networks (ANN) are algorithms that are also search-and-learn algorithms, where the design of the algorithm mimics some aspects of the brain. Artificial neural networks can often get good results for a particular dataset, but often do not give models that can be replicated on future data.
  • Deep learning is an exciting new area of neural networks, which is showing very good results for artificial learning.
This brief introduction serves merely to introduce the reader to this topic, which merits substantial further investigation.
Last updated: April 18, 2017
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