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 297

Machine Learning Metrics Data Science Publishing 297

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 345

Metrics Measurement Modeling Data Science 345

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 190

Metrics Machine Learning Data Science Optimization 190

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 292

Machine Learning Metrics Data Science Publishing 292

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 244

Metrics Machine Learning Data Science Publishing 244

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.

Metrics 325

Metrics KPI Measurement Reporting 325

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 254

Machine Learning Modeling Metrics IT 254

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!

Machine Learning 387

Machine Learning Modeling Analytics Metrics 387

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 241

Metrics Testing Machine Learning Analytics 241

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 250

Metrics Modeling Analytics Statistics 250

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

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 292

Machine Learning Analytics Metrics Structured Data 292

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 101

Data Processing Machine Learning Measurement Optimization 101

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.

Machine Learning 220

Machine Learning Risk Management Statistics 220

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 115

Metrics Machine Learning Data Science 115

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.

Metrics 141

Metrics Modeling Machine Learning Data Science 141

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 310

Metrics Modeling Data Science Publishing 310

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 97

Machine Learning Forecasting Recreation/Entertainment Metrics 97

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.

Machine Learning 151

Machine Learning Metrics 151

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]

Machine Learning 191

Machine Learning Modeling Testing Risk Management 191

CIO Business Intelligence

JUNE 27, 2022

In the last two years, the OCC team and SAP have worked together to teach CEMEX’s SAP Solution Manager to run SAP operations intelligently, utilizing machine learning and artificial intelligence, supported by best practices. The Machine Learning Extension for ELP SAP Solution Manager 7.2 The result?

Machine Learning 96

Machine Learning Manufacturing Software Metrics 96

KDnuggets

APRIL 13, 2023

Improves your data science skill arsenals with these metrics.

Metrics 149

Metrics Data Science Machine Learning 149

Smart Data Collective

DECEMBER 9, 2020

When most people consider the merits of machine learning, they typically think about its applications from a capitalist standpoint. There are countless ways that business owners are using machine learning advances to pad their bottom lines. They learn to identify numerous risk factors and alert the driver.

Machine Learning 141

Machine Learning Risk Insurance Predictive Analytics 141

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 113

Machine Learning Cost-Benefit Forecasting Sales 113

IBM Big Data Hub

DECEMBER 19, 2023

Anomaly detection simply means defining “normal” patterns and metrics—based on business functions and goals—and identifying data points that fall outside of an operation’s normal behavior. A machine learning model trained with labeled data will be able to detect outliers based on the examples it is given.

Machine Learning 71

Machine Learning Optimization Unstructured Data Sales 71

KDnuggets

OCTOBER 11, 2019

In this upcoming webinar on Oct 23 @ 10 AM PT, learn why you should invest time in monitoring your machine learning models, the dangers of not paying attention to how a model’s performance can change over time, metrics you should be gathering for each model and what they tell you, and much more.

Machine Learning 57

Machine Learning Metrics Modeling 57

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 196

Machine Learning Metrics Analytics Data Science 196

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.

Metrics 160

Metrics Measurement Optimization Modeling 160

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 machine learning? This post will dive deeper into the nuances of each field.

Machine Learning 59

Machine Learning Data Science Statistics Deep Learning 59

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 272

Machine Learning Metrics Data Science Modeling 272

IBM Big Data Hub

OCTOBER 16, 2023

Machine learning (ML)—the artificial intelligence (AI) subfield in which machines learn from datasets and past experiences by recognizing patterns and generating predictions—is a $21 billion global industry projected to become a $209 billion industry by 2029.

Machine Learning 51

Machine Learning Marketing Forecasting Modeling 51

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.

Machine Learning 160

Machine Learning Modeling Metrics 160

Cloudera

FEBRUARY 16, 2022

In this example, the Machine Learning (ML) model struggles to differentiate between a chihuahua and a muffin. We will learn what it is, why it is important and how Cloudera Machine Learning (CML) is helping organisations tackle this challenge as part of the broader objective of achieving Ethical AI.

Machine Learning 81

Machine Learning Modeling Metadata Recreation/Entertainment 81

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 306

Data Science Metrics Machine Learning Publishing 306

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 […].

Modeling 274

Modeling Machine Learning Metrics Testing 274

Cloudera

APRIL 9, 2021

Specifically, we’ll focus on training Machine Learning (ML) models to forecast ECC part production demand across all of its factories. Predictive Analytics – AI & machine learning. So let’s introduce Cloudera Machine Learning (CML) and discuss how it addresses the aforementioned silo issues.

Machine Learning 98

Machine Learning Forecasting Manufacturing Predictive Analytics 98

KDnuggets

OCTOBER 13, 2022

In this article, I will be going through 4 common classification metrics: Accuracy, Precision, Recall, and ROC in relation to Logistic Regression.

Metrics 123

Metrics Machine Learning 123