Analytics Vidhya

MAY 21, 2021

ArticleVideo Book This article was published as a part of the Data Science Blogathon Introduction Model Building in Machine Learning is an important component of. The post Importance of Cross Validation: Are Evaluation Metrics enough? appeared first on Analytics Vidhya.

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Metrics Machine Learning Data Science Publishing 264

Analytics Vidhya

MAY 19, 2021

ArticleVideo Book This article was published as a part of the Data Science Blogathon Introduction Machine Learning is a branch of Artificial Intelligence. The post Know The Best Evaluation Metrics for Your Regression Model ! It contains. appeared first on Analytics Vidhya.

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Analytics Vidhya

OCTOBER 12, 2020

This article was published as a part of the Data Science Blogathon. Introduction Machine learning is about building a predictive model using historical data. The post Quick Guide to Evaluation Metrics for Supervised and Unsupervised Machine Learning appeared first on Analytics Vidhya.

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Machine Learning Metrics Predictive Modeling Data Science 301

Domino Data Lab

MARCH 3, 2021

It’s often stated that nothing changes inside an enterprise because you’ve built a model. It’s often stated that nothing changes inside an enterprise because you’ve built a model. Align on key indicators of success during the initiation phase of a data science project.

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Dataiku

DECEMBER 5, 2023

Ever wondered if your model could improve from a performance score of.38 At Everyday AI NYC 2023 , Emma Huang and Cassie Chuljian, senior data scientists at Dataiku, unraveled the secrets behind optimizing model metrics.

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Analytics Vidhya

OCTOBER 28, 2021

This article was published as a part of the Data Science Blogathon The objective of any machine learning model is to understand and learn patterns from the data which can further be used to make predictions or answer questions or simply just understand the underlying pattern that is otherwise not evident candidly.

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Modeling Machine Learning Data Science Publishing 279

KDnuggets

APRIL 19, 2022

Statistical performance metrics aren’t enough to pick the right models to bring to market.

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Analytics Vidhya

NOVEMBER 25, 2022

This article was published as a part of the Data Science Blogathon. Source:pixabay.com Introduction State-of-the-art machine learning models and artificially intelligent machines are made of complex processes like adjusting hyperparameters and choosing models that provide better accuracy and the metrics that govern this behavior.

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Machine Learning Metrics Data Science Publishing 295

Analytics Vidhya

JULY 5, 2022

This article was published as a part of the Data Science Blogathon. Introduction to Confusion Matrix In a situation where we want to make discrete predictions, we often wish to assess the quality of our model beyond simple metrics like the model’s accuracy, especially if we have many classes.

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Analytics Vidhya

DECEMBER 16, 2020

This article was published as a part of the Data Science Blogathon. The post Decluttering the performance measures of classification models appeared first on Analytics Vidhya. Introduction There are so many performance evaluation measures when it comes to.

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Measurement Modeling Data Science Publishing 296

Analytics Vidhya

FEBRUARY 4, 2020

The post How to Create a Test Set to Approximate Business Metrics Offline appeared first on Analytics Vidhya. Introduction Most Kaggle-like machine learning hackathons miss a core aspect of a machine learning workflow – preparing an offline evaluation environment while building an.

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Metrics Testing Machine Learning Analytics 258

IBM Big Data Hub

SEPTEMBER 19, 2023

Though you may encounter the terms “data science” and “data analytics” being used interchangeably in conversations or online, they refer to two distinctly different concepts. Meanwhile, data analytics is the act of examining datasets to extract value and find answers to specific questions.

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Data Science Data Analytics Prescriptive Analytics Analytics 77

Analytics Vidhya

OCTOBER 22, 2021

This article was published as a part of the Data Science Blogathon Introduction Working as an ML engineer, it is common to be in situations where you spend hours to build a great model with desired metrics after carrying out multiple iterations and hyperparameter tuning but cannot get back to the same results with the […].

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Machine Learning Metrics Data Science Publishing 319

Smart Data Collective

AUGUST 18, 2021

Although widely used, keyword scanning software alone simply doesn’t generate sufficient success metrics when sifting through candidate resumes. If your recruitment process takes longer than this average, data science can help you speed it up while providing better results. Speed up the recruitment process. Retaining staff.

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Data Science Management Big Data Metrics 136

Analytics Vidhya

NOVEMBER 26, 2021

This article was published as a part of the Data Science Blogathon. We will train various classification models and compare the performance metrics to extract useful insights. In this post, we will discuss the sentiment analysis problem. We have taken the Twitter US airline sentiment dataset for this empirical study.

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Machine Learning Metrics Data Science Publishing 314

CIO Business Intelligence

JUNE 29, 2022

In essence, MLOps analysts are a section of the data science team. Whereas data scientists focus on algorithm design and the interpretation of information, MLOps concentrate on making sure the model is working at its best amid changing demands. We monitor algorithms metrics, improve performance, and set best practice. .

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Analytics Vidhya

SEPTEMBER 10, 2021

This article was published as a part of the Data Science Blogathon Introduction With ignite, you can write loops to train the network in just a few lines, add standard metrics calculation out of the box, save the model, etc. Well, for those who have moved from TF to PyTorch, we can say that the ignite […].

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Testing Metrics Data Science Publishing 289

Smart Data Collective

SEPTEMBER 21, 2021

A growing number of businesses are starting to look for new data-driven approaches to streamline their business models. However, your data-driven business model won’t be very helpful if you don’t focus on the right metrics. Targeting the Right Variables for Your Data-Driven Retail Business Model.

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DataRobot

FEBRUARY 10, 2021

Even if all the code runs and the model seems to be spitting out reasonable answers, it’s possible for a model to encode fundamental data science mistakes that invalidate its results. These errors might seem small, but the effects can be disastrous when the model is used to make decisions in the real world.

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Domino Data Lab

AUGUST 6, 2021

Data science is an incredibly complex field. When you factor in the requirements of a business-critical machine learning model in a working enterprise environment, the old cat-herding meme won’t even get a smile. Deploy: includes validating, publishing and delivering working models into a business environment.

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IBM Big Data Hub

JULY 6, 2023

While data science and machine learning are related, they are very different fields. In a nutshell, data science brings structure to big data while machine learning focuses on learning from the data itself. What is data science? This post will dive deeper into the nuances of each field.

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IBM Big Data Hub

AUGUST 11, 2023

Because ML is becoming more integrated into daily business operations, data science teams are looking for faster, more efficient ways to manage ML initiatives, increase model accuracy and gain deeper insights. MLOps is the next evolution of data analysis and deep learning. How the models are stored.

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Data Science Machine Learning Cost-Benefit Deep Learning 53

O'Reilly on Data

DECEMBER 12, 2019

Not least is the broadening realization that ML models can fail. And that’s why model debugging, the art and science of understanding and fixing problems in ML models, is so critical to the future of ML. Because all ML models make mistakes, everyone who cares about ML should also care about model debugging. [1]

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O'Reilly on Data

JULY 28, 2020

The first step in building an AI solution is identifying the problem you want to solve, which includes defining the metrics that will demonstrate whether you’ve succeeded. It sounds simplistic to state that AI product managers should develop and ship products that improve metrics the business cares about. Agreeing on metrics.

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DataRobot Blog

JULY 28, 2022

Companies are emphasizing the accuracy of machine learning models while at the same time focusing on cost reduction, both of which are important. As a DataRobot data scientist , I have worked with team members on a variety of projects to improve the business value of our customers. Sensor Data Analysis Examples.

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Domino Data Lab

SEPTEMBER 10, 2020

Building models requires a lot of time and effort. Data scientists can spend weeks just trying to find, capture and transform data into decent features for models, not to mention many cycles of training, tuning, and tweaking models so they’re performant. What’s going on? Boosting your odds of success.

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Modeling IT Data Science Experimentation 101

Rocket-Powered Data Science

DECEMBER 16, 2018

In a related post we discussed the Cold Start Problem in Data Science — how do you start to build a model when you have either no training data or no clear choice of model parameters. The above example (clustering) is taken from unsupervised machine learning (where there are no labels on the training data).

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Machine Learning Metrics Optimization Data Science 203

DataRobot Blog

SEPTEMBER 1, 2022

For example, a recent IDC study 1 shows that it takes about 290 days on average to deploy a model into production from start to finish. Once you move your model into production, you need to monitor and manage your models to ensure that you can trust predictions and turn them into the right business decisions.

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Alation

JULY 28, 2021

As data science processes continue to become operationalized and embedded within business processes, the importance of governing those processes continues to rise. While governance has been a major focus for many years when it comes to managing data, governance focused on data science processes is still far less mature.

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Data Science Data Governance Modeling Software 52

Decision Management Solutions

MARCH 16, 2021

If you are working in data science, you may be frustrated with the progress and ROI of your data science and artificial intelligence projects. The first step in achieving success with data science is recognizing these problem areas in your approach. by James Taylor, CEO, Decision Management Solutions.

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Cloudera

FEBRUARY 16, 2022

In this example, the Machine Learning (ML) model struggles to differentiate between a chihuahua and a muffin. Will the model correctly determine it is a muffin or get confused and think it is a chihuahua? The extent to which we can predict how the model will classify an image given a change input (e.g. Model Visibility.

Machine Learning 87

Machine Learning Modeling Metadata Recreation/Entertainment 87

Domino Data Lab

JULY 22, 2019

Paco Nathan presented, “Data Science, Past & Future” , at Rev. At Rev’s “ Data Science, Past & Future” , Paco Nathan covered contextual insight into some common impactful themes over the decades that also provided a “lens” help data scientists, researchers, and leaders consider the future.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

Rocket-Powered Data Science

JULY 19, 2023

The results showed that (among those surveyed) approximately 90% of enterprise analytics applications are being built on tabular data. The ease with which such structured data can be stored, understood, indexed, searched, accessed, and incorporated into business models could explain this high percentage.

Data-driven 188

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Domino Data Lab

JULY 28, 2021

Data science is a field at the convergence of statistics, computer science and business. Its value is so significant that scaling data science has become the new business imperative with organizations spending tens of millions of dollars on data, technology and talent. Data Science Techniques.

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Data Science Machine Learning Statistics Deep Learning 45

IBM Big Data Hub

NOVEMBER 29, 2023

With the emergence of new advances and applications in machine learning models and artificial intelligence, including generative AI, generative adversarial networks, computer vision and transformers, many businesses are seeking to address their most pressing real-world data challenges using both types of synthetic data: structured and unstructured.

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Metrics Machine Learning Statistics Risk 81

AWS Big Data

OCTOBER 26, 2023

Data analysts and database developers want to use this data to train machine learning (ML) models, which can then be used to generate insights on new data for use cases such as forecasting revenue, predicting customer churn, and detecting anomalies.

Modeling 62

Modeling Data Warehouse Machine Learning Testing 62

O'Reilly on Data

MAY 1, 2019

Cait O’Riordan discusses the North Star metric the Financial Times uses to drive subscriber growth. Making data science useful. Cassie Kozyrkov explains how organizations can extract more value from their data. Watch " Making data science useful.". Watch " The unstoppable rise of white box data.".

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