7 Important Model Evaluation Error Metrics Everyone should know

 

Finding the right model for your prediction is important but highly dependent on the metric you use to assess the quality of the predictions and the predictive power of the model. In this article, Tavish Srivastava, decrypts 7 evaluation metrics: Definition, usage and their influence on the model selection. 

The metrics include the classics: RMSE, ROC-AUC and the confusion matrix, alternatives metrics such as the Gini Coefficient and Gain Chart which are frequently used in Kaggle competitions and less frequent ones such as the Kolmogorov Smirnov Chart or the Concordant – Discordant Ratio.

A very well illustrated article which offers a good introduction on the importance of the metric for model selection.

Classifying Bees With Google TensorFlow

 The Bees Classifier Metis Challenge on DataDriven.org consisted in predicting the type of bees appearing in a 4000 images. Given the set of images it was up to the participants to build their own set of features using image processing techniques.

In this article, Philippe Dagher Data Scientist and Kaggler, builds a basic Google Tensorflow algorithm to determine the genus—Apis (honey bee) or Bombus (bumble bee)—based on photographs of the insects. A good coding example on how to apply Google TensorFlow on a real life dataset.

Find the blog useful?

Help others see it by commenting and sharing.

Learn more about this topic and others like it at our Data Visualization Conference, trainings and workshops.

References:  

http://nasdag.github.io/blog/2016/01/19/classifying-bees-with-google-tensorflow
http://www.analyticsvidhya.com/blog/2016/02/7-important-model-evaluation-error-metrics