Classifying Objects

The classification problem is about identifying the category an object belongs to. In this context, an object is a data item and is fully represented by an array of values (known as features). Each value refers to a measurable property that makes sense to consider in the scenario under analysis. It is key to note that classification can predict values only in a discrete, categorical set.

Variations of the Problem

The actual rules that govern the object-to-category mapping process lead to slightly different variations of the classification problem and subsequently different implementation tasks.

Binary Classification. The algorithm has to assign the processed object to one of only two possible categories. An example is deciding whether, based on a battery of tests for a particular disease, a patient should be placed in the “disease” or “no-disease” group.

Multiclass Classification. The algorithm has to assign the processed object to one of many possible categories. Each object can be assigned to one and only one category. For example, classifying the competency of a candidate, it can be any of poor/sufficient/good/great but not any two at the same time.

Multilabel Classification. The algorithm is expected to provide an array of categories (or labels) that the object belongs to. An example is how to classify a blog post. It can be about sports, technology, and perhaps politics at the same time.

Anomaly Detection. The algorithm aims to spot objects in the dataset whose property values are significantly different from the values of the majority of other objects. Those anomalies are also often referred to as outliers.

The Dev DataOps Agile cycle

 

For data science and development teams to work together (and along with domain experts), it is necessary that skills tend to merge: data scientists learning about programming aspects and user experience and, more importantly, developers learning about the intricacies and internal mechanics of machine learning.

Algorithms are presented in seven groups or kingdoms distilled from the broader fields of study

1. Stochastic Algorithms that focus on the introduction of randomness into heuristic methods.
2. Evolutionary Algorithms inspired by evolution by means of natural selection.
3. Physical Algorithms inspired by physical and social systems.
4. Probabilistic Algorithms that focus on methods that build models and estimate distributions in search domains.
5. Swarm Algorithms that focus on methods that exploit the properties of collective intelligence.
6. Immune Algorithms inspired by the adaptive immune system of vertebrates.
7. Neural Algorithms inspired by the plasticity and learning qualities of the human nervous system.

Time Dilation

 

Accuracy

 

A Pale Blue Dot

Look again at that dot. That's here. That's home. That's us. On it everyone you love, everyone you know, everyone you ever heard of, every human being who ever was, lived out their lives. The aggregate of our joy and suffering, thousands of confident religions, ideologies, and economic doctrines, every hunter and forager, every hero and coward, every creator and destroyer of civilization, every king and peasant, every young couple in love, every mother and father, hopeful child, inventor and explorer, every teacher of morals, every corrupt politician, every "superstar," every "supreme leader," every saint and sinner in the history of our species lived there--on a mote of dust suspended in a sunbeam.

The Earth is a very small stage in a vast cosmic arena. Think of the rivers of blood spilled by all those generals and emperors so that, in glory and triumph, they could become the momentary masters of a fraction of a dot. Think of the endless cruelties visited by the inhabitants of one corner of this pixel on the scarcely distinguishable inhabitants of some other corner, how frequent their misunderstandings, how eager they are to kill one another, how fervent their hatreds.

Our posturings, our imagined self-importance, the delusion that we have some privileged position in the Universe, are challenged by this point of pale light. Our planet is a lonely speck in the great enveloping cosmic dark. In our obscurity, in all this vastness, there is no hint that help will come from elsewhere to save us from ourselves.

The Earth is the only world known so far to harbor life. There is nowhere else, at least in the near future, to which our species could migrate. Visit, yes. Settle, not yet. Like it or not, for the moment the Earth is where we make our stand.

It has been said that astronomy is a humbling and character-building experience. There is perhaps no better demonstration of the folly of human conceits than this distant image of our tiny world. To me, it underscores our responsibility to deal more kindly with one another, and to preserve and cherish the pale blue dot, the only home we've ever known.

— Carl Sagan, Pale Blue Dot, 1994

Copyright © 1994 by Carl Sagan, Copyright © 2006 by Democritus Properties, LLC.

All rights reserved including the rights of reproduction in whole or in part in any form.

 

Hierarchy of evidence pyramid.

 

Content-based filtering, Above Right: Collaborative filtering.

 

Methods of internet empowered informational distribution

 

* as a Service

SAAS - Software as a Service

PAAS - Plataform as a Service

IAAS - Infrastructure as a Service

CAAS - Containers as a Service 

FAAS - Function as a Service

ByteDance Technology System Infrastructure

 

Contrasting push and pull models of “people looking for information” and “information looking for people.”

 

Usage of mobile internet was highly fragmented - small pockets of time spread throughout the day. Desktop usage, in contrast, tended towards longer set blocks of time.

 

You think becoming an online influencer is simply a matter of making interesting videos? Come on, don’t be so naïve!

A typical click farm equipment set up

Example input and output pair for supervised classification (top) and regression (bottom)

 

Relative rankings of popular classification models on the basis of accuracy and interpretability.

 

An example of concept drift types

 

Head to Head Comparison between Data Science and Data Mining (Infographics)

Data Science Road Map

 

Machine learning combines three main components: data, model and loss. Machine learning methods implement the scientific principle of “trial and error”. These methods continuously validate and refine a model based on the loss incurred by its predictions about a phenomenon that generates data.