Know Food Machine Learning


The basic concept of food machine learning in data science involves using statistical learning and optimization methods that let computers analyze datasets and identify patterns (view a visual of machine learning via R2D3 External link ). Machine learning techniques leverage data mining to

The basic concept of food machine learning in data science involves using statistical learning and optimization methods that let computers analyze datasets and identify patterns (view a visual of machine learning via R2D3 External link ). Machine learning techniques leverage data mining to identify historic trends to inform future models.

The typical supervised machine learning algorithm consists of (roughly) three components:

A decision process: A recipe of calculations or other steps that takes in the data and returns a “guess” at the kind of pattern in the data your algorithm is looking to find.
An error function: A method of measuring how good the guess was by comparing it to known examples (when they are available). Did the decision process get it right? If not, how do you quantify “how bad” the miss was?
An updating or optimization process: Where the algorithm looks at the miss and then updates how the decision process comes to the final decision so that the next time the miss won’t be as great.
For example, if you're building a movie recommender, your algorithm’s decision process might look at how similar a given movie is to other movies you’ve watched and come up with a weighting system for different features.

During the training process, the algorithm goes through the movies you have watched and weights different properties. Is it a sci-fi movie? Is it funny? The algorithm then tests out whether it ends up recommending movies that you (or people like you) actually watched. If it gets it right, the weights it used stay the same; if it gets a movie wrong, the weights that led to the wrong decision get turned down so it doesn’t make that kind of mistake again.

Since a machine learning algorithm updates autonomously, the analytical accuracy improves with each run as it teaches itself from the data it analyzes. This iterative nature of learning is both unique and valuable because it occurs without human intervention — providing the ability to uncover hidden insights without being specifically programmed to do so.

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