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 […].

Machine Learning 348

Machine Learning Metrics Data Science Publishing 348

O'Reilly on Data

JUNE 9, 2020

The biggest problem facing machine learning today isn’t the need for better algorithms; it isn’t the need for more computing power to train models; it isn’t even the need for more skilled practitioners. It’s getting machine learning from the researcher’s laptop to production.

Machine Learning 347

Machine Learning Metrics Modeling IT 347

Analytics Vidhya

SEPTEMBER 30, 2022

Introduction Evaluation metrics are used to measure the quality of the model. Selecting an appropriate evaluation metric is important because it can impact your selection of a model or decide whether to put your model into production. The mportance of cross-validation: Are evaluation metrics […].

Metrics 396

Metrics Measurement Data Science Publishing 396

Analytics Vidhya

APRIL 16, 2020

Have you been in a situation where you expected your machine learning model to perform really well but. The post Everything you Should Know about Confusion Matrix for Machine Learning appeared first on Analytics Vidhya. Confusion Matrix – Not So Confusing!

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Machine Learning Modeling Analytics Metrics 400

Analytics Vidhya

NOVEMBER 26, 2021

We will train various classification models and compare the performance metrics to extract useful insights. The post An Empirical study of Machine Learning Classifiers with Tweet Sentiment Classification appeared first on Analytics Vidhya. We have taken the Twitter US airline sentiment dataset for this empirical study.

Machine Learning 343

Machine Learning Metrics Data Science Publishing 343

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.

Metrics 304

Metrics Machine Learning Data Science Publishing 304

O'Reilly on Data

NOVEMBER 13, 2018

As the data community begins to deploy more machine learning (ML) models, I wanted to review some important considerations. We recently conducted a survey which garnered more than 11,000 respondents—our main goal was to ascertain how enterprises were using machine learning. Let’s begin by looking at the state of adoption.

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Machine Learning Risk Management Statistics 277

Analytics Vidhya

FEBRUARY 4, 2020

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

Metrics 290

Metrics Testing Machine Learning Analytics 290

David Menninger's Analyst Perspectives

NOVEMBER 9, 2021

How does your organization define and display its metrics? I believe many organizations are not defining and displaying metrics in a way that benefits them most. A number, by itself, does not provide any indication of whether the result is good or bad.

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Metrics KPI Measurement Reporting 328

Analytics Vidhya

JANUARY 27, 2022

Introduction The basic idea of building a machine learning model is to assess the relationship between the dependent and independent variables. The post Evaluation Metrics With Python Codes appeared first on Analytics Vidhya. In doing so, we need to optimize the model performance.

Metrics 252

Metrics Machine Learning Data Science Publishing 252

Analytics Vidhya

SEPTEMBER 3, 2020

Overview Precision and recall are two crucial yet misunderstood topics in machine learning We’ll discuss what precision and recall are, how they work, and. The post Precision vs. Recall – An Intuitive Guide for Every Machine Learning Person appeared first on Analytics Vidhya.

Machine Learning 304

Machine Learning Analytics Metrics Structured Data 304

Analytics Vidhya

OCTOBER 11, 2020

The post HOW TO CHOOSE EVALUATION METRICS FOR CLASSIFICATION MODEL appeared first on Analytics Vidhya. This article was published as a part of the Data Science Blogathon. INTRODUCTION Yay!! So you have successfully built your classification model. What should.

Metrics 329

Metrics Modeling Data Science Publishing 329

O'Reilly on Data

DECEMBER 12, 2019

For all the excitement about machine learning (ML), there are serious impediments to its widespread adoption. There are several known attacks against machine learning models that can lead to altered, harmful model outcomes or to exposure of sensitive training data. [8] 2] The Security of Machine Learning. [3]

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Machine Learning Modeling Testing Risk Management 276

Rocket-Powered Data Science

DECEMBER 16, 2018

So, you start by assuming a value for k and making random assumptions about the cluster means, and then iterate until you find the optimal set of clusters, based upon some evaluation metric. The above example (clustering) is taken from unsupervised machine learning (where there are no labels on the training data).

Machine Learning 203

Machine Learning Metrics Optimization Data Science 203

Analytics Vidhya

FEBRUARY 16, 2023

Unlike traditional AUC scores, partial AUC scores concentrate on a specific region of the ROC (Receiver Operating Characteristic) curve, offering a more detailed evaluation of the model’s […] The post Partial AUC Scores: A Better Metric for Binary Classification appeared first on Analytics Vidhya.

Metrics 291

Metrics Modeling Analytics Statistics 291

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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Metrics Modeling Machine Learning Data Science 176

Analytics Vidhya

JANUARY 28, 2022

Introduction A Machine Learning solution to an unambiguously defined business problem is developed by a Data Scientist ot ML Engineer. The Model development process undergoes multiple iterations and finally, a model which has acceptable performance metrics on test data is taken to the production […].

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Modeling Machine Learning Metrics Testing 344

Analytics Vidhya

NOVEMBER 25, 2022

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.

Machine Learning 328

Machine Learning Metrics Data Science Publishing 328

Analytics Vidhya

AUGUST 3, 2022

Introduction Previous articles on this data science interview series have discussed interview questions related to Regression Analysis, Classification Metrics, and Ensemble Approaches. This article will cover interview questions about Machine Learning concepts like ROC-AUC curves and tunning of Hyperparameter.

Data Science 349

Data Science Machine Learning Metrics Publishing 349

Analytics Vidhya

AUGUST 25, 2024

Introduction Assessing a machine learning model isn’t just the final step—it’s the keystone of success. Evaluation is more than ticking off metrics; it’s about ensuring your model consistently performs in the wild.

Modeling 289

Modeling Machine Learning Metrics Analytics 289

O'Reilly on Data

MARCH 31, 2020

If you’re already a software product manager (PM), you have a head start on becoming a PM for artificial intelligence (AI) or machine learning (ML). AI products are automated systems that collect and learn from data to make user-facing decisions. We won’t go into the mathematics or engineering of modern machine learning here.

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Management Machine Learning Experimentation Metrics 363

Analytics Vidhya

MAY 17, 2023

How can we sift through many variables to identify the most influential factors for accurate predictions in machine learning? Recursive Feature Elimination offers a compelling solution, and RFE iteratively removes less important features, creating a subset that maximizes predictive accuracy.

Machine Learning 266

Machine Learning Metrics Analytics Data Science 266

KDnuggets

APRIL 13, 2023

Improves your data science skill arsenals with these metrics.

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Metrics Data Science Machine Learning 160

DataKitchen

NOVEMBER 5, 2024

Data is typically organized into project-specific schemas optimized for business intelligence (BI) applications, advanced analytics, and machine learning. Similarly, downstream business metrics in the Gold layer may appear skewed due to missing segments, which can impact high-stakes decisions.

Data Quality 195

Data Quality Testing Metrics Reporting 195

Occam's Razor

AUGUST 9, 2021

Almost all metrics you currently use have one common thread: They are almost all backward-looking. If you want to deepen the influence of data in your organization – and your personal influence – 30% of your analytics efforts should be centered around the use of forward-looking metrics. Predictive metrics!

Metrics 143

Metrics Analytics Key Performance Indicator Predictive Modeling 143

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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Marketing Experimentation Metrics Testing 364

KDnuggets

NOVEMBER 14, 2022

In this post, you will learn to clarify business problems & constraints, understand problem statements, select evaluation metrics, overcome technical challenges, and design high-level systems.

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Machine Learning Metrics 157

KDnuggets

JANUARY 12, 2022

Using one or two performance metrics seems sufficient to claim that your ML model is good — chances are that it’s not.

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Machine Learning Modeling Metrics 160

Smart Data Collective

FEBRUARY 14, 2023

We have talked about the impact that machine learning has had on website and app development. However, machine learning technology can also help solve Internet problems on a more granular level. Fortunately, machine learning technology shows some promise in addressing them.

Machine Learning 124

Machine Learning Internet Publishing and Broadcasting Broadcasting Cost-Benefit 124

O'Reilly on Data

MAY 14, 2020

This role includes everything a traditional PM does, but also requires an operational understanding of machine learning software development, along with a realistic view of its capabilities and limitations. data platform, metrics, ML/AI research, and applied ML). This is both an advantage and a disadvantage!

Management 364

Management Experimentation B2B Machine Learning 364

KDnuggets

MARCH 31, 2023

Looking to understand the most commonly used distance metrics in machine learning? This guide will help you learn all about Euclidean, Manhattan, and Minkowski distances, and how to compute them in Python.

Metrics 122

Metrics Machine Learning Data Science 122

Smart Data Collective

NOVEMBER 7, 2023

If you’re eager to monetize the web hosting services you offer to third party site owners, or you have a selection of self-hosted sites which you are eager to wring more cash out of, then machine learning could be the answer. This is where machine learning from top developers comes into play.

Data Processing 124

Data Processing Machine Learning Measurement Optimization 124

O'Reilly on Data

JUNE 19, 2019

A look at the landscape of tools for building and deploying robust, production-ready machine learning models. Our surveys over the past couple of years have shown growing interest in machine learning (ML) among organizations from diverse industries. Why aren’t traditional software tools sufficient?

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Modeling Machine Learning Testing Metrics 254

Smart Data Collective

AUGUST 15, 2022

By using artificial intelligence and machine learning, industries can better cope with their consumers’ demands. Today, companies use machine learning, in particular, to ensure that they achieve the appropriate productivity output for the amount of money they spend on their business operations.

Machine Learning 128

Machine Learning Cost-Benefit Forecasting Sales 128

KDnuggets

SEPTEMBER 20, 2022

However, this metric alone is never the entire story, as it can still offer misleading results. When building and optimizing your classification model, measuring how accurately it predicts your expected outcome is crucial. That's where these additional performance evaluations come into play to help tease out more meaning from your model.

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Metrics Measurement Optimization Modeling 160

CIO Business Intelligence

NOVEMBER 29, 2022

In a bid to help enterprises offer better customer service and experience , Amazon Web Services (AWS) on Tuesday, at its annual re:Invent conference, said that it was adding new machine learning capabilities to its cloud-based contact center service, Amazon Connect. c (Sydney), and Europe (London).

Machine Learning 111

Machine Learning Forecasting Recreation/Entertainment Metrics 111

O'Reilly on Data

OCTOBER 13, 2020

Similarly, in “ Building Machine Learning Powered Applications: Going from Idea to Product ,” Emmanuel Ameisen states: “Indeed, exposing a model to users in production comes with a set of challenges that mirrors the ones that come with debugging a model.”. While useful, these constructs are not beyond criticism. Monitoring.

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