Model Drift in Machine Learning — Data Science

Source — https://evidentlyai.com/blog/machine-learning-monitoring-data-and-concept-drift

Data Drift:

Target Drift:

Concept Drift:

- We might use both the old data as well as the new data to retrain our model. While retraining we can assign a higher weight to new data so that our model assigns priority to the latest patterns

- If we have enough new data available, we can do away with the past data.

If you like my content on Medium or Quantifiers and find it resourceful, you can show your support by hitting the clap button.

To connect with me reach out on Linkedin

For PM Interviews, you can refer to amazing articles at Technomanagers

--

--

--

IIM Bangalore | Writing about Machine Learning & Quantitative finance.

Love podcasts or audiobooks? Learn on the go with our new app.

Leveraging AI and Deep Learning for Video Summarization

Machine Learning: A Deja Vu?

Semantic DNA to Analyze Messaging Effectiveness: an Application of Explainable NLP

Recurrent Neural Networks: Part 5

Creating a Product Recommendation Neural Network in the Web Browser

The brain: the most sophisticated machine learning on earth

Experiments on different loss configurations for style transfer

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store
Priyanka Dalmia

Priyanka Dalmia

IIM Bangalore | Writing about Machine Learning & Quantitative finance.

More from Medium

Reproducible Data Science and why it matters

Deploy new ML model only if better than currently deployed model

AutoGluon: easy-to-use and high-performing AutoML

Real cases of Machine Learning at a Big Scale