- Visual Scoring Techniques for Classification Models - Nov 3, 2021.
Read this article assessing a model performance in a broader context.
Classification, Knime, Low-Code, Machine Learning, Metrics, Visualization
- Advanced PyTorch Lightning with TorchMetrics and Lightning Flash - Nov 1, 2021.
In this tutorial we will be diving deeper into two additional tools you should be using: TorchMetrics and Lightning Flash. TorchMetrics unsurprisingly provides a modular approach to define and track useful metrics across batches and devices, while Lightning Flash offers a suite of functionality facilitating more efficient transfer learning and data handling, and a recipe book of state-of-the-art approaches to typical deep learning problems.
Metrics, Python, PyTorch, PyTorch Lightning, Transfer Learning
- How to calculate confidence intervals for performance metrics in Machine Learning using an automatic bootstrap method - Oct 15, 2021.
Are your model performance measurements very precise due to a “large” test set, or very uncertain due to a “small” or imbalanced test set?
Machine Learning, Metrics, Statistics
- How I Built A Perfect Model And Got Into Trouble - Oct 12, 2021.
Data-driven decisions, actionable insights, business impact—you've seen these buzzwords in data science jobs descriptions. But, just focusing on these terms doesn't automatically lead to the best results. Learn from this real-world scenario that followed data-driven indecisiveness, found misleading insights, and initially created a negative business impact.
Analytics, Business, Customer Analytics, Finance, KPI, Metrics
- 5 Things That Make My Job as a Data Scientist Easier - Aug 23, 2021.
After working as a Data Scientist for a year, I am here to share some things I learnt along the way that I feel are helpful and have increased my efficiency. Hopefully some of these tips can help you in your journey :)
Data Science, Data Scientist, Metrics, Pandas, Plotly, Python, Time Series, Visualization
- ROC Curve Explained - Jul 6, 2021.
Learn to visualise a ROC curve in Python.
Data Visualization, Metrics, Python, ROC-AUC
- Similarity Metrics in NLP - May 10, 2021.
This post covers the use of euclidean distance, dot product, and cosine similarity as NLP similarity metrics.
Metrics, NLP, Similarity
- Metric Matters, Part 1: Evaluating Classification Models - Mar 16, 2021.
You have many options when choosing metrics for evaluating your machine learning models. Select the right one for your situation with this guide that considers metrics for classification models.
Accuracy, Classification, Metrics, Precision, Recall, ROC-AUC
4 Machine Learning Concepts I Wish I Knew When I Built My First Model - Mar 9, 2021.
Diving into building your first machine learning model will be an adventure -- one in which you will learn many important lessons the hard way. However, by following these four tips, your first and subsequent models will be put on a path toward excellence.
Feature Selection, Gradio, Hyperparameter, Machine Learning, Metrics, Python
- Evaluating Object Detection Models Using Mean Average Precision - Mar 3, 2021.
In this article we will see see how precision and recall are used to calculate the Mean Average Precision (mAP).
Computer Vision, Metrics, Modeling, Object Detection
Evaluating Deep Learning Models: The Confusion Matrix, Accuracy, Precision, and Recall - Feb 19, 2021.
This tutorial discusses the confusion matrix, and how the precision, recall and accuracy are calculated, and how they relate to evaluating deep learning models.
Accuracy, Confusion Matrix, Deep Learning, Metrics, Precision, Recall
- How to Create Custom Real-time Plots in Deep Learning - Dec 14, 2020.
How to generate real-time visualizations of custom metrics while training a deep learning model using Keras callbacks.
Data Visualization, Deep Learning, Keras, Metrics, Neural Networks, Python
- Essential Math for Data Science: Integrals And Area Under The Curve - Nov 25, 2020.
In this article, you’ll learn about integrals and the area under the curve using the practical data science example of the area under the ROC curve used to compare the performances of two machine learning models.
Machine Learning, Mathematics, Metrics, numpy, Python, Unbalanced
- Simple Python Package for Comparing, Plotting & Evaluating Regression Models - Nov 25, 2020.
This package is aimed to help users plot the evaluation metric graph with single line code for different widely used regression model metrics comparing them at a glance. With this utility package, it also significantly lowers the barrier for the practitioners to evaluate the different machine learning algorithms in an amateur fashion by applying it to their everyday predictive regression problems.
Data Visualization, Metrics, Modeling, Python, Regression
- Most Popular Distance Metrics Used in KNN and When to Use Them - Nov 11, 2020.
For calculating distances KNN uses a distance metric from the list of available metrics. Read this article for an overview of these metrics, and when they should be considered for use.
K-nearest neighbors, Metrics, scikit-learn
Goodhart’s Law for Data Science and what happens when a measure becomes a target? - Oct 14, 2020.
When developing analytics and algorithms to better understand a business target, unintended biases can sneak in that ensure desired outcomes are obtained. Guiding your work with multiple metrics in mind can help avoid such consequences of Goodhart's Law.
Advice, Chris Wiggins, Data Science, Ethics, Goodhart’s Law, Metrics, Social Good
- Metrics to Use to Evaluate Deep Learning Object Detectors - Aug 6, 2020.
It's important to understand which metric should be used to evaluate trained object detectors and which one is more important. Is mAP alone enough to evaluate the objector models? Can the same metric be used to evaluate object detectors on validation set and test set?
Computer Vision, Deep Learning, Metrics, Object Detection
- PyTorch Multi-GPU Metrics Library and More in New PyTorch Lightning Release - Jul 2, 2020.
PyTorch Lightning, a very light-weight structure for PyTorch, recently released version 0.8.1, a major milestone. With incredible user adoption and growth, they are continuing to build tools to easily do AI research.
GPU, Metrics, Python, PyTorch, PyTorch Lightning
- 3 Key Data Science Questions to Ask Your Big Data - Jun 3, 2020.
The process of understanding your data begins by asking 3 questions at the highest level, and then iteratively asking hundreds of cascading questions to get deeper insights.
Big Data, Business, Customer Analytics, Data Science, Metrics
Model Evaluation Metrics in Machine Learning - May 28, 2020.
A detailed explanation of model evaluation metrics to evaluate a classification machine learning model.
Classification, Confusion Matrix, Machine Learning, Metrics, Python, Regression
- Why you should NOT use MS MARCO to evaluate semantic search - Apr 2, 2020.
If we want to investigate the power and limitations of semantic vectors (pre-trained or not), we should ideally prioritize datasets that are less biased towards term-matching signals. This piece shows that the MS MARCO dataset is more biased towards those signals than we expected and that the same issues are likely present in many other datasets due to similar data collection designs.
Data Science, Metrics, NLP, Text Analytics
- A simple and interpretable performance measure for a binary classifier - Mar 4, 2020.
Binary classification tasks are the bread and butter of machine learning. However, the standard statistic for its performance is a mathematical tool that is difficult to interpret -- the ROC-AUC. Here, a performance measure is introduced that simply considers the probability of making a correct binary classification.
Classification, Classifier, Interpretability, Machine Learning, Metrics, ROC-AUC
- Recommender System Metrics: Comparing Apples, Oranges and Bananas - Feb 11, 2020.
This article will discuss a sometimes-overlooked aspect of what distinguishes recommender systems from other machine learning tasks: added uncertainties of measuring them.
Metrics, Recommendation Engine, Recommender Systems
- The 5 Most Useful Techniques to Handle Imbalanced Datasets - Jan 22, 2020.
This post is about explaining the various techniques you can use to handle imbalanced datasets.
Balancing Classes, Datasets, Metrics, Python, Sampling, Unbalanced
- Beginner’s Guide to K-Nearest Neighbors in R: from Zero to Hero - Jan 3, 2020.
This post presents a pipeline of building a KNN model in R with various measurement metrics.
Beginners, K-nearest neighbors, Metrics, R
- The 5 Classification Evaluation Metrics Every Data Scientist Must Know - Oct 16, 2019.
This post is about various evaluation metrics and how and when to use them.
Data Scientist, Machine Learning, Metrics, Python
- Clustering Metrics Better Than the Elbow Method - Oct 1, 2019.
We show what metric to use for visualizing and determining an optimal number of clusters much better than the usual practice — elbow method.
Clustering, Metrics
6 bits of advice for Data Scientists - Sep 25, 2019.
As a data scientist, you can get lost in your daily dives into the data. Consider this advice to be certain to follow in your work for being diligent and more impactful for your organization.
Advice, Data Cleaning, Data Scientist, Metrics, Overfitting, Statistics
6 Key Concepts in Andrew Ng’s “Machine Learning Yearning” - Aug 12, 2019.
If you are diving into AI and machine learning, Andrew Ng's book is a great place to start. Learn about six important concepts covered to better understand how to use these tools from one of the field's best practitioners and teachers.
AI, Andrew Ng, Best Practices, Deployment, Machine Learning, Metrics, Training Data
- Comparison of the Text Distance Metrics - Jan 7, 2019.
There are many different approaches of how to compare two texts (strings of characters). Each has its own advantages and disadvantages and is good only for a range of specific use cases.
Metrics, NLP, Text Analytics
- Using Confusion Matrices to Quantify the Cost of Being Wrong - Oct 11, 2018.
The terms ‘true condition’ (‘positive outcome’) and ‘predicted condition’ (‘negative outcome’) are used when discussing Confusion Matrices. This means that you need to understand the differences (and eventually the costs associated) with Type I and Type II Errors.
Confusion Matrix, Data Science, Machine Learning, Metrics, Predictive Modeling
- Receiver Operating Characteristic Curves Demystified (in Python) - Jul 20, 2018.
In this blog, I will reveal, step by step, how to plot an ROC curve using Python. After that, I will explain the characteristics of a basic ROC curve.
Machine Learning, Metrics, Python, ROC-AUC
- 7 Useful Suggestions from Andrew Ng “Machine Learning Yearning” - May 8, 2018.
Machine Learning Yearning is a book by AI and Deep Learning guru Andrew Ng, focusing on how to make machine learning algorithms work and how to structure machine learning projects. Here we present 7 very useful suggestions from the book.
Andrew Ng, Book, Data Cleaning, Data Preparation, Free ebook, Machine Learning, Metrics
- KDnuggets™ News 18:n18, May 2: Blockchain Explained in 7 Python Functions; Data Science Dirty Secret; Choosing the Right Evaluation Metric - May 2, 2018.
Also: Building Convolutional Neural Network using NumPy from Scratch; Data Science Interview Guide; Implementing Deep Learning Methods and Feature Engineering for Text Data: The GloVe Model; Jupyter Notebook for Beginners: A Tutorial
Blockchain, Convolutional Neural Networks, Data Science, Machine Learning, Metrics, numpy, Python
- Operational Machine Learning: Seven Considerations for Successful MLOps - Apr 30, 2018.
In this article, we describe seven key areas to take into account for successful operationalization and lifecycle management (MLOps) of your ML initiatives
DevOps, Machine Learning, Metrics, MLOps
- Learning Curves for Machine Learning - Jan 17, 2018.
But how do we diagnose bias and variance in the first place? And what actions should we take once we've detected something? In this post, we'll learn how to answer both these questions using learning curves.
Pages: 1 2
Bias, Machine Learning, Metrics, Training Data, Variance
- The danger in comparing your campaign performance against an average - Oct 26, 2017.
Performance measurement is only meaningful when compared against a benchmark. While “average” is a good, and easy to understand metric, it could be very deceptive.
CleverTap, Customer Analytics, Metrics
- Analytics 101: Comparing KPIs - Mar 20, 2017.
Different business units in the organisation have different behaviours (e.g. turnover rate) and they can’t be compared with each other. So, how can we tell whether the changes in their behaviour are reasons for concern?
KPI, Metrics, Statistics
- The Top 5 KPIs to Consider When Measuring Your Campaign - Feb 28, 2017.
When it comes to measuring marketing campaign performance or analysing customers in any business, below top 5 Key Performance Indicators (KPIs) needs to be used to strategically drive the business.
Churn, Customer Analytics, KPI, Metrics, ROI, Social Media
- The Best Metric to Measure Accuracy of Classification Models - Dec 7, 2016.
Measuring accuracy of model for a classification problem (categorical output) is complex and time consuming compared to regression problems (continuous output). Let’s understand key testing metrics with example, for a classification problem.
Pages: 1 2
Accuracy, Classification, CleverTap, Measurement, Metrics, Precision, Unbalanced
- Lift Analysis – A Data Scientist’s Secret Weapon - Mar 22, 2016.
Gain insight into using lift analysis as a metric for doing data science. Understand how to use it for evaluating the performance and quality of a machine learning model.
Data Science, Lift charts, Metrics
