Noemi  Sanford

Noemi Sanford

1592189160

Choosing a Prediction Modeling Technique

We encounter the results of prediction models everyday. We get offered the right incentive to buy a product. We get a text from the bank when something strange is happening with our accounts. And we rarely have to see spam in our inboxes. This is all thanks to the models built on the data that we generate everyday.

Read on to learn more about the business of prediction modeling and how you can develop and train your own.

Is predictive modeling machine learning?
Predictive modeling and machine learning are related, but have slightly different definitions. Predictive modeling is often defined as the use of statistical models to predict outcomes. Machine learning is a subset of artificial intelligence that refers to the use of computers to construct predictive models. In more recent years, however, the terms have been used synonymously.

#machine learning

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Choosing a Prediction Modeling Technique
Ian  Robinson

Ian Robinson

1623223443

Predictive Modeling in Data Science

Predictive modeling is an integral tool used in the data science world — learn the five primary predictive models and how to use them properly.

Predictive modeling in data science is used to answer the question “What is going to happen in the future, based on known past behaviors?” Modeling is an essential part of data science, and it is mainly divided into predictive and preventive modeling. Predictive modeling, also known as predictive analytics, is the process of using data and statistical algorithms to predict outcomes with data models. Anything from sports outcomes, television ratings to technological advances, and corporate economies can be predicted using these models.

Top 5 Predictive Models

  1. Classification Model: It is the simplest of all predictive analytics models. It puts data in categories based on its historical data. Classification models are best to answer “yes or no” types of questions.
  2. Clustering Model: This model groups data points into separate groups, based on similar behavior.
  3. **Forecast Model: **One of the most widely used predictive analytics models. It deals with metric value prediction, and this model can be applied wherever historical numerical data is available.
  4. Outliers Model: This model, as the name suggests, is oriented around exceptional data entries within a dataset. It can identify exceptional figures either by themselves or in concurrence with other numbers and categories.
  5. Time Series Model: This predictive model consists of a series of data points captured, using time as the input limit. It uses the data from previous years to develop a numerical metric and predicts the next three to six weeks of data using that metric.

#big data #data science #predictive analytics #predictive analysis #predictive modeling #predictive models

Mckenzie  Osiki

Mckenzie Osiki

1623906928

How To Use “Model Stacking” To Improve Machine Learning Predictions

What is Model Stacking?

Model Stacking is a way to improve model predictions by combining the outputs of multiple models and running them through another machine learning model called a meta-learner. It is a popular strategy used to win kaggle competitions, but despite their usefulness they’re rarely talked about in data science articles — which I hope to change.

Essentially a stacked model works by running the output of multiple models through a “meta-learner” (usually a linear regressor/classifier, but can be other models like decision trees). The meta-learner attempts to minimize the weakness and maximize the strengths of every individual model. The result is usually a very robust model that generalizes well on unseen data.

The architecture for a stacked model can be illustrated by the image below:

#tensorflow #neural-networks #model-stacking #how to use “model stacking” to improve machine learning predictions #model stacking #machine learning

Michael  Hamill

Michael Hamill

1617331277

Workshop Alert! Deep Learning Model Deployment & Management

The Association of Data Scientists (AdaSci), the premier global professional body of data science and ML practitioners, has announced a hands-on workshop on deep learning model deployment on February 6, Saturday.

Over the last few years, the applications of deep learning models have increased exponentially, with use cases ranging from automated driving, fraud detection, healthcare, voice assistants, machine translation and text generation.

Typically, when data scientists start machine learning model development, they mostly focus on the algorithms to use, feature engineering process, and hyperparameters to make the model more accurate. However, model deployment is the most critical step in the machine learning pipeline. As a matter of fact, models can only be beneficial to a business if deployed and managed correctly. Model deployment or management is probably the most under discussed topic.

In this workshop, the attendees get to learn about ML lifecycle, from gathering data to the deployment of models. Researchers and data scientists can build a pipeline to log and deploy machine learning models. Alongside, they will be able to learn about the challenges associated with machine learning models in production and handling different toolkits to track and monitor these models once deployed.

#hands on deep learning #machine learning model deployment #machine learning models #model deployment #model deployment workshop

Otho  Hagenes

Otho Hagenes

1617419868

Top Five Artificial Intelligence Predictions For 2021

As AI becomes more ubiquitous, it’s also become more autonomous — able to act on its own without human supervision. This demonstrates progress, but it also introduces concerns around control over AI. The AI Arms Race has driven organizations everywhere to deliver the most sophisticated algorithms around, but this can come at a price, ignoring cultural and ethical values that are critical to responsible AI. Here are five predictions on what we should expect to see in AI in 2021:

  1. Something’s going to give around AI governance
  2. Most consumers will continue to be sceptical of AI
  3. Digital transformation (DX) finds its moment
  4. Organizations will increasingly push AI to the edge
  5. ModelOps will become the “go-to” approach for AI deployment.

#opinions #2021 ai predictions #ai predictions for 2021 #artificial intelligence predictions #five artificial intelligence predictions for 2021

Bret Kinley

1583299685

BI Database Modeling Tools

At SqlDBM BI database modeling tool help organizations to improve their decision and Analyze billions of records in seconds. Currently " Data Warehouse” is currently trending topic in the data area. We will covering what a Data Warehouse is and how it is created from a SQL script. Visit us to get know more about BI modeling Tools and how it work with SQL.

#export data model #SQL Server BI Modeling #BI modeling Tools #SQL Server Business Intelligence Modeling Tool