Analytics Vidhya

AUGUST 29, 2023

From foundational concepts to advanced techniques, this article is your comprehensive guide. Whether you’re delving into descriptive statistics, probability distributions, or sophisticated regression models, R’s versatility and extensive packages facilitate seamless statistical exploration.

Statistics 270

Statistics Visualization Modeling Analytics 270

Analytics Vidhya

NOVEMBER 25, 2024

The normal distribution, also known as the Gaussian distribution, is one of the most widely used probability distributions in statistics and machine learning. Understanding its core properties, mean and variance, is important for interpreting data and modelling real-world phenomena.

Statistics 204

Statistics Machine Learning Modeling Analytics 204

Analytics Vidhya

APRIL 4, 2022

This article was published as a part of the Data Science Blogathon. Introduction One of the key challenges in Machine Learning Model is the explainability of the ML Model that we are building. In general, ML Model is a Black Box.

Modeling 290

Modeling Machine Learning Statistics Data Science 290

Analytics Vidhya

JUNE 1, 2023

With franchise leagues like IPL and BBL, teams rely on statistical models and tools for competitive edge. This article explores how data analytics optimizes strategies by leveraging player performances and opposition weaknesses. Introduction Cricket embraces data analytics for strategic advantage.

Predictive Modeling 291

Predictive Modeling Modeling Statistics Optimization 291

Analytics Vidhya

FEBRUARY 20, 2023

Introduction In this article, our focus will be on learning how to solve a time series problem. Time series analysis is a statistical technique used to analyze data […] The post How to Build Your Time Series Model? Before we take up a time series problem, we must familiarise ourselves with the concept of forecasting.

Modeling 291

Modeling Statistics Forecasting Analytics 291

Analytics Vidhya

DECEMBER 23, 2021

This article was published as a part of the Data Science Blogathon Optimization Optimization provides a way to minimize the loss function. Optimization aims to reduce training errors, and Deep Learning Optimization is concerned with finding a suitable model. In this article, we will […].

Optimization 348

Optimization Modeling Deep Learning Data Science 348

Analytics Vidhya

MAY 4, 2022

This article was published as a part of the Data Science Blogathon. Introduction to Random Forest Missing values have always been a concern for any statistical analysis. They significantly reduce the study’s statistical powers, which may lead to faulty conclusions.

Statistics 397

Statistics Data Science Publishing Modeling 397

Analytics Vidhya

JANUARY 20, 2022

This article was published as a part of the Data Science Blogathon. The post A brief introduction to Multilevel Modelling appeared first on Analytics Vidhya.

Modeling 343

Modeling Testing Data Science Publishing 343

Analytics Vidhya

SEPTEMBER 29, 2020

This article was published as a part of the Data Science Blogathon. The post How Machine Learning Models Fail to Deliver in Real-World Scenarios appeared first on Analytics Vidhya. Introduction Yesterday, my brother broke an antique at home. I began to.

Machine Learning 374

Machine Learning Modeling Data Science Publishing 374

Analytics Vidhya

MARCH 1, 2022

This article was published as a part of the Data Science Blogathon. Credit evaluations have progressed from being subjective decisions by the bank’s credit experts to a more statistically advanced evaluation. The post Gaussian Naive Bayes Algorithm for Credit Risk Modelling appeared first on Analytics Vidhya.

Risk 271

Risk Modeling Statistics Data Science 271

Analytics Vidhya

SEPTEMBER 17, 2022

This article was published as a part of the Data Science Blogathon. Introduction Conventionally, an automatic speech recognition (ASR) system leverages a single statistical language model to rectify ambiguities, regardless of context. However, we can improve the system’s accuracy by leveraging contextual information.

Metadata 400

Metadata Statistics Data Science Publishing 400

O'Reilly on Data

NOVEMBER 19, 2024

Reasons for using RAG are clear: large language models (LLMs), which are effectively syntax engines, tend to “hallucinate” by inventing answers from pieces of their training data. See the primary sources “ REALM: Retrieval-Augmented Language Model Pre-Training ” by Kelvin Guu, et al., at Facebook—both from 2020. What is GraphRAG?

Unstructured Data 318

Unstructured Data Structured Data Modeling Statistics 318

Analytics Vidhya

SEPTEMBER 6, 2021

This article was published as a part of the Data Science Blogathon Introduction Q-Q plots are also known as Quantile-Quantile plots. The post Q-Q plot – Ensure Your ML Model is Based on the Right Distribution appeared first on Analytics Vidhya.

Modeling 388

Modeling Data Science Publishing Analytics 388

Analytics Vidhya

OCTOBER 11, 2020

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

Metrics 329

Metrics Modeling Data Science Publishing 329

Analytics Vidhya

JULY 5, 2022

This article was published as a part of the Data Science Blogathon. Introduction to Imbalanced Datasets The accuracy achieved by many of the machine learning models using traditional statistical algorithms increases by just around 2% or so when the size of the training dataset is increased from 20% to 80%.

Statistics 353

Statistics Machine Learning Data Science Publishing 353

Analytics Vidhya

JUNE 17, 2024

Introduction The log-normal distribution is a fascinating statistical concept commonly used to model data that exhibit right-skewed behavior. This distribution has wide-ranging applications in various fields, such as biology, finance, and engineering.

Statistics 319

Statistics Finance Modeling Analytics 319

Analytics Vidhya

OCTOBER 9, 2020

This article was published as a part of the Data Science Blogathon. Introduction: Probabilistic Graphical Models (PGM) capture the complex relationships between random variables. The post Complete R Tutorial To Build Probabilistic Graphical Models! appeared first on Analytics Vidhya.

Modeling 319

Modeling Data Science Publishing Analytics 319

Analytics Vidhya

JULY 23, 2021

ArticleVideo Book This article was published as a part of the Data Science Blogathon Introduction Logistic Regression is another statistical model which is used for. The post Geometrical Approach To Understand Logistic Regression appeared first on Analytics Vidhya.

Statistics 353

Statistics Data Science Publishing Modeling 353

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.

Measurement 306

Measurement Modeling Data Science Publishing 306

Analytics Vidhya

OCTOBER 28, 2020

This article was published as a part of the Data Science Blogathon. Introduction A popular and widely used statistical method for time series forecasting. The post How to Create an ARIMA Model for Time Series Forecasting in Python appeared first on Analytics Vidhya.

Forecasting 234

Forecasting Modeling Statistics Data Science 234

Analytics Vidhya

MAY 28, 2021

ArticleVideo Book This article was published as a part of the Data Science Blogathon Introduction Logistic Regression, a statistical model is a very popular and. The post 20+ Questions to Test your Skills on Logistic Regression appeared first on Analytics Vidhya.

Testing 329

Testing Statistics Data Science Publishing 329

Analytics Vidhya

NOVEMBER 25, 2020

This article was published as a part of the Data Science Blogathon. The post Creating Linear Model, It’s Equation and Visualization for Analysis appeared first on Analytics Vidhya. Introduction Have you ever been tasked with visualizing the relationship between each.

Visualization 291

Visualization Modeling Data Science Publishing 291

Analytics Vidhya

SEPTEMBER 28, 2021

This article was published as a part of the Data Science Blogathon In this tutorial, we will learn: Introduction to Natural Language Processing Phases of Natural Language Processing Introduction to spaCy Installation and local setup of spaCy Statistical models available in spaCy Reading and processing text Spans Tokenization Sentence Detection Stopwords (..)

Statistics 315

Statistics Data Science Publishing Modeling 315

Analytics Vidhya

JULY 20, 2021

ArticleVideo Book This article was published as a part of the Data Science Blogathon Evaluation Metrics for Classification Problem Image source ?[link] The post Metrics to Evaluate your Classification Model to take the right decisions appeared first on Analytics Vidhya. link] Abstract The most.

Metrics 271

Metrics Modeling Data Science Publishing 271

O'Reilly on Data

MARCH 20, 2019

Apply fair and private models, white-hat and forensic model debugging, and common sense to protect machine learning models from malicious actors. Like many others, I’ve known for some time that machine learning models themselves could pose security risks. This is like a denial-of-service (DOS) attack on your model itself.

Modeling 278

Modeling Machine Learning Predictive Modeling Consulting 278

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]

Machine Learning 281

Machine Learning Modeling Testing Risk Management 281

Analytics Vidhya

MARCH 29, 2022

This article was published as a part of the Data Science Blogathon. Introduction to Linear Regression Image 1: Sales vs Budget data with a linear model representation Linear regression is a statistical method that presumes a linear relationship between the input and the output variables.

Statistics 271

Statistics Sales Data Science Publishing 271

Analytics Vidhya

JULY 25, 2022

This article was published as a part of the Data Science Blogathon. Machine Learning is the method of teaching computer programs to do a specific task accurately (essentially a prediction) by training a predictive model using various statistical algorithms leveraging data. Source: [link] For […].

Machine Learning 271

Machine Learning Predictive Modeling Statistics Data Science 271

Analytics Vidhya

MAY 21, 2021

ArticleVideo Book This article was published as a part of the Data Science Blogathon Introduction Whenever we build any machine learning model, we feed it. The post 4 Ways to Evaluate your Machine Learning Model: Cross-Validation Techniques (with Python code) appeared first on Analytics Vidhya.

Machine Learning 257

Machine Learning Modeling Data Science Publishing 257

Analytics Vidhya

JULY 9, 2022

This article was published as a part of the Data Science Blogathon. Introduction In order to build machine learning models that are highly generalizable to a wide range of test conditions, training models with high-quality data is essential.

Machine Learning 391

Machine Learning Data Science Data Collection Testing 391

O'Reilly on Data

JUNE 18, 2019

There has been a significant increase in our ability to build complex AI models for predictions, classifications, and various analytics tasks, and there’s an abundance of (fairly easy-to-use) tools that allow data scientists and analysts to provision complex models within days. Data integration and cleaning.

Machine Learning 279

Machine Learning Data Quality Statistics Modeling 279

Analytics Vidhya

DECEMBER 4, 2020

This article was published as a part of the Data Science Blogathon. Introduction Random Forests are always referred to as black-box models. Let’s try. The post Lets Open the Black Box of Random Forests appeared first on Analytics Vidhya.

Data Science 399

Data Science Publishing Modeling Analytics 399

Analytics Vidhya

JULY 9, 2024

This powerful metric, called relative entropy or information gain, has become indispensable in various fields, from statistical inference to deep learning. Introduction Few concepts in mathematics and information theory have profoundly impacted modern machine learning and artificial intelligence, such as the Kullback-Leibler (KL) divergence.

Machine Learning 255

Machine Learning Metrics Deep Learning Statistics 255

CIO Business Intelligence

FEBRUARY 6, 2025

This article reflects some of what Ive learned. The hype around large language models (LLMs) is undeniable. Think about it: LLMs like GPT-3 are incredibly complex deep learning models trained on massive datasets. Even basic predictive modeling can be done with lightweight machine learning in Python or R.

Unstructured Data 115

Unstructured Data Manufacturing Data Governance Sales 115

O'Reilly on Data

FEBRUARY 16, 2023

Large language models like ChatGPT and Google’s LaMDA aren’t designed to give correct results. Remember that these tools aren’t doing math, they’re just doing statistics on a huge body of text. You can train models that are optimized to be correct—but that’s a different kind of model.

Testing 302

Testing Statistics Modeling Optimization 302

O'Reilly on Data

NOVEMBER 13, 2018

Considerations for a world where ML models are becoming mission critical. As the data community begins to deploy more machine learning (ML) models, I wanted to review some important considerations. Before I continue, it’s important to emphasize that machine learning is much more than building models. Model lifecycle management.

Machine Learning 277

Machine Learning Risk Management Statistics 277

Analytics Vidhya

MARCH 22, 2022

This article was published as a part of the Data Science Blogathon. Introduction Feature analysis is an important step in building any predictive model. In this article, we will look into a very simple feature analysis technique that can be used in cases such as […].

Predictive Modeling 332

Predictive Modeling Data Science Publishing Modeling 332