- 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
- Building an Image Classifier Running on Raspberry Pi - Oct 9, 2018.
The tutorial starts by building the Physical network connecting Raspberry Pi to the PC via a router. After preparing their IPv4 addresses, SSH session is created for remotely accessing of the Raspberry Pi. After uploading the classification project using FTP, clients can access it using web browsers for classifying images.
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Classifier, Image Recognition, Raspberry Pi
- Must-Know: How to evaluate a binary classifier - Apr 11, 2017.
Binary classification is a basic concept which involves classifying the data into two groups. Read on for some additional insight and approaches.
Classifier, Interview Questions, Machine Learning
- Introduction to Local Interpretable Model-Agnostic Explanations (LIME) - Aug 25, 2016.
Learn about LIME, a technique to explain the predictions of any machine learning classifier.
Algorithms, Classifier, Explanation, Interpretability, LIME, Machine Learning, Prediction
- KDnuggets™ News 16:n12, Apr 6: Top 10 Essential Books; Perfect Data Science Interview - Apr 6, 2016.
Top 10 Essential Books for the Data Enthusiast; How to Compute the Statistical Significance of Two Classifiers Performance Difference; The Secret to a Perfect Data Science Interview; If Hollywood Made Movies About Machine Learning Algorithms.
Books, Classifier, Complexity, Machine Learning
- How to Compute the Statistical Significance of Two Classifiers Performance Difference - Mar 30, 2016.
To determine whether a result is statistically significant, a researcher would have to calculate a p-value, which is the probability of observing an effect given that the null hypothesis is true. Here we are demonstrating how you can compute difference between two models using it.
Classifier, Cross-validation, Model Performance, Statistical Significance
