- Mastering Machine Learning with R
- Cory Lesmeister
- 481字
- 2021-07-09 21:28:16
Machine learning defined
Machine learning is everywhere! It is used in web search, spam filters, recommendation engines, medical diagnostics, ad placement, fraud detection, credit scoring, and I fear in these autonomous cars that I hear so much about. The roads are dangerous enough now; the idea of cars with artificial intelligence, requiring CTRL + ALT + DEL every 100 miles, aimlessly roaming the highways and byways is just too terrifying to contemplate. But, I digress.
It is always important to properly define what one is talking about and machine learning is no different. The website, machinelearningmastery.com, has a full page dedicated to this question, which provides some excellent background material. It also offers a succinct one-liner that is worth adopting as an operational definition: machine learning is the training of a model from data that generalizes a decision against a performance measure.
With this definition in mind, we will require a few things in order to perform machine learning. The first is that we have the data. The second is that a pattern actually exists, which is to say that with known input values from our training data, we can make a prediction or decision based on data that we did not use to train the model. This is the generalization in machine learning. Third, we need some sort of performance measure to see how well we are learning/generalizing, for example, the mean squared error, accuracy, and others. We will look at a number of performance measures throughout the book.
One of the things that I find interesting in the world of machine learning are the changes in the language to describe the data and process. As such, I can't help but include this snippet from the philosopher, George Carlin:
"I wasn't notified of this. No one asked me if I agreed with it. It just happened. Toilet paper became bathroom tissue. Sneakers became running shoes. False teeth became dental appliances. Medicine became medication. Information became directory assistance. The dump became the landfill. Car crashes became automobile accidents. Partly cloudy became partly sunny. Motels became motor lodges. House trailers became mobile homes. Used cars became previously owned transportation. Room service became guest-room dining, and constipation became occasional irregularity. |
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| --Philosopher and Comedian, George Carlin | ||
I cut my teeth on datasets that had dependent and independent variables. I would build a model with the goal of trying to find the best fit. Now, I have labeled the instances and input features that require engineering, which will become the feature space that I use to learn a model. When all was said and done, I used to look at my model parameters; now, I look at weights.
The bottom line is that I still use these terms interchangeably and probably always will. Machine learning purists may curse me, but I don't believe I have caused any harm to life or limb.