DEV Community: cynid3301

Predictive Analytics with Ensemble Learning

cynid3301 — Sat, 01 Jul 2023 16:35:14 +0000

Predictive analytics is a type of data analysis that uses statistical, machine learning, and analytical methods to predict future outcomes.
Ensemble learning is a machine learning technique that combines multiple models to create a more accurate prediction than any single model could achieve on its own.
There are many different ensemble learning methods, but some of the most common include:
- Bagging: This method creates multiple models by training each model on a different bootstrap sample of the training data.
- Boosting: This method creates multiple models by training each model on the same training data, but weighting the data points differently in each iteration.
- Stacking: This method creates a meta-model that combines the predictions of multiple base models.
Ensemble learning methods can be used to improve the predictive accuracy of a variety of machine learning models, including:
- Decision trees
- Support vector machines
- Neural networks
Ensemble learning methods can also be used to improve the robustness of a machine learning model, making it less susceptible to overfitting or underfitting.

Feature selection and Engineering in supervised classification and regression

cynid3301 — Fri, 30 Jun 2023 12:46:51 +0000

- Feature selection and engineering:

Feature selection is a process of identifying and selecting the most relevant features from a dataset.
Relevant features are those that are most likely to be predictive of the target variable.
Irrelevant features are those that are not predictive of the target variable or that add noise to the data.
Feature selection can improve the performance of machine learning models by reducing overfitting and improving the interpretability of the models.
There are a number of different feature selection methods available, each with its own advantages and disadvantages.

- Classification and regression using supervised learning

Supervised learning is a type of machine learning where the model is trained on a dataset of labeled data. The labeled data consists of pairs of inputs and outputs, where the input is a vector of features and the output is a scalar value.
Classification is a type of supervised learning where the goal is to predict the class of an input. The class is a categorical variable, such as "red" or "blue".
Regression is a type of supervised learning where the goal is to predict a continuous value. The continuous value can be anything from a number to a probability.
Supervised learning algorithms are trained on a dataset of labeled data. The algorithm learns the relationship between the inputs and outputs, and then uses this relationship to make predictions on new data.
There are many different supervised learning algorithms available, each with its own strengths and weaknesses. Some of the most common supervised learning algorithms include:
- Decision trees
- Support vector machines
- Random forests
- Neural networks

Machine Learning Pipelines

cynid3301 — Mon, 12 Jun 2023 06:57:39 +0000

What is it? A machine learning pipeline is a set of steps that are used to build and deploy a machine learning model which usually includes 8 steps:

Problem Definition: Define the business problem
Data Ingestion: Identify and collect the dataset
Data Preparation: Process and prepare the data
Data Segregation: Split data into training/validation/testing set
Model Training: Train the models against the training dataset
Candidate Model Evaluation: Measure the performance of the models
Model Deployment: Deploy into production
Performance Monitoring: Monitor performance, retrain and calibrate

Use cases of AI

cynid3301 — Mon, 05 Jun 2023 17:50:12 +0000

Digital assistance: Google Assistant, Amazon Alexa, Apple, Siri, Microsoft Cortana, Samsung Bixby, etc. Which could help in controlling simple devices, play music/videos, set timers/reminders, open applications, read notifications, etc

Personal Driver: has 5 levels: level 1 being driver assistance (cruise control), level 2 being partial automation (Tesla autopilot), level 3 is conditional automation (Audi traffic jam pilot), level 4 is high automation ([defunct] Google's Firefly pod-car) this level does not include pedals or steering wheels and could be control automatically under most condition, lastly is level 5 which is Full automation basically a more advance version of level 4 but works under every condition

Item management: One notable example is Amazon sorting facility in which computers take customer orders and decide where to route merchandise, the robots act as mules carrying the pallets and inventory around the warehouse and human are only responsible for the "last mile" problem by handpicking the items and delivering it

Others applications involve smart homes, researching development, gaming, movie making, data analysis and more.

Alternatives to the Turing test.

cynid3301 — Sun, 28 May 2023 13:40:03 +0000

The Lovelace 2.0 test: Created by Mark O. Riedl in 2014. The Lovelace 2.0 Test of creativity as an alternative to the Turing Test as a means of determining whether an agent is intelligent. This test builds off prior tests of creativity and additionally provides a means of directly comparing the relative intelligence of different agents.
The reverse Turing test: Invented Henry S. Baird and a few other researchers in 2002. This test exploit the gap in ability between human and machine vision systems to craft a family of automatic challenges that tell human and machine users apart via graphical interfaces including Internet browsers
The Visual Turing test: First created by Donald Geman (and others) in 2014. In this test an operator-assisted device that produces a stochastic sequence of binary questions from a given test image. The query engine proposes a question; the operator either provides the correct answer or rejects the question as ambiguous; the engine proposes the next question (“just-in-time truthing”). The test is then administered to the computer-vision system, one question at a time. After the system’s answer is recorded, the system is provided the correct answer and the next question.
The Winograd Schema Challenge: created in 2012 by Hector J. Levesque and his colleague. we present an alternative to the Turing Test that has some conceptual and practical advantages. A Winograd schema is a pair of sentences that differ only in one or
two words and that contain a referential ambiguity that is resolved in opposite directions in the two sentences. This test is designed so that the correct answer is obvious to the human reader, but cannot easily be found using selectional restrictions or statistical techniques over text corpora.

The Turing test

cynid3301 — Sun, 21 May 2023 13:28:13 +0000

Proposed by famed scientist and mathematician Alan Turing, this test aims to provide a clear cut definition of Intelligence, specifically for machines and/or computer programs.

The test is as following: on one side are 2 respondents, one machine and the other human. On the other side is a human acting as an interrogator. The machine pass the test if the interrogator can not tell the difference between the two.

This test require high level of NLP, Knowledge representation, Reasoning, and ML. Which is beginning to be achievable for modern AI programs like ChatGPT by OpenAI

One of my favorite quote about the Turing test is: "Any AI smart enough to pass a Turing test is smart enough to know to fail it."