Comments (2)
In modAL, this all depends on what the .fit()
method of the model does. For scikit-learn estimators, AFAIK this retrains the model from scratch. For Keras models, it just performs the backpropagation algorithm for the data using the old weights. Note that modAL itself does not initialize any new objects for models, it keeps the original.
Currently, there are two options in modAL for retraining your model.
learner.fit(X_new, y_new, only_new=False)
(this is default)
Following your example, this calls the model's.fit()
method passing all of the 110 examples. This should be used where the model is retrained from scratch. (Like for models in scikit-learn.)learner.fit(X_new, y_new, only_new=True)
This calls the.fit()
method of the model only withX_new
andy_new
. It should be used for active learning with neural networks, when you may not want to do backpropagation on all of the known training data, because it might cause the model to overfit.
So, it cannot be stated that one version is better than the other. Each has its own use cases, and they should be used accordingly.
from modal.
Got it. Thanks for the detailed explanation.
from modal.
Related Issues (20)
- Multivariate Active regression
- How to extract the image names and labels in the training set after completing the active learning loop and write them to a CSV file
- decision_function instead of predict_proba HOT 5
- AttributeError: bootstrap_init HOT 3
- TypeError: cannot concatenate object of type '<class 'numpy.ndarray'>'; only Series and DataFrame objs are valid
- Can I use modAL with estimators from other libraries than scikit-learn like xgboost? HOT 1
- Which sampling method is best for very unbalanced data? HOT 1
- Encountering error with number of batches per epoch
- mmdetection integration with modAL
- Adding active learning regression implementations based on greedy sampling HOT 2
- modAL not installable via pypi anymore HOT 3
- the modAL package has been changed into modal in the pip repository HOT 7
- Data augmentation with `skorch`
- QBC approach for multi-class classification
- Suggestion on how to improve acquisition.UCB for active GP example HOT 1
- QBC stratified bootstrapping HOT 1
- Use modAL on BERT models HOT 1
- Spacy NER HOT 1
- raise ImportError( ImportError: C extension: None not built. If you want to import pandas from the source directory, you may need to run 'python setup.py build_ext' to build the C extensions first.
- uncertainty query for 2d classifier output
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from modal.