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A malicious npm package targets Solana developers, rerouting funds in 2% of transactions to a hardcoded address.
an AutoML library that builds, optimizes, and evaluates machine learning pipelines using domain-specific objective functions
EvalML is an AutoML library which builds, optimizes, and evaluates machine learning pipelines using domain-specific objective functions.
Key Functionality
Install from PyPI:
pip install evalml
or from the conda-forge channel on conda:
conda install -c conda-forge evalml
Update checker - Receive automatic notifications of new Woodwork releases
PyPI:
pip install "evalml[updater]"
Conda:
conda install -c conda-forge alteryx-open-src-update-checker
import evalml
X, y = evalml.demos.load_breast_cancer()
X_train, X_test, y_train, y_test = evalml.preprocessing.split_data(X, y, problem_type='binary')
from evalml.automl import AutoMLSearch
automl = AutoMLSearch(X_train=X_train, y_train=y_train, problem_type='binary')
automl.search()
automl.rankings
pipeline = automl.best_pipeline
pipeline.predict(X_test)
Read more about EvalML on our documentation page:
The EvalML community is happy to provide support to users of EvalML. Project support can be found in four places depending on the type of question:
evalml
tag.EvalML is an open source project built by Alteryx. To see the other open source projects we’re working on visit Alteryx Open Source. If building impactful data science pipelines is important to you or your business, please get in touch.
FAQs
an AutoML library that builds, optimizes, and evaluates machine learning pipelines using domain-specific objective functions
We found that evalml demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 8 open source maintainers collaborating on the project.
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