Release Highlights for scikit-learn 1.0
Release Highlights for scikit-learn 1.0# We are very pleased to announce the release of scikit-learn 1.0! The library has been stable for quite some time, releasing version 1.0 is recognizing that and signalling it to our users. This release does not include any breaking changes apart from the usual two-release deprecation cycle. For the future, we do our best to keep this pattern. This release in
Comparing randomized search and grid search for hyperparameter estimation
Comparing randomized search and grid search for hyperparameter estimation# Compare randomized search and grid search for optimizing hyperparameters of a linear SVM with SGD training. All parameters that influence the learning are searched simultaneously (except for the number of estimators, which poses a time / quality tradeoff). The randomized search and the grid search explore exactly the same s
sklearn.ensemble.RandomForestClassifier
sklearn.ensemble.RandomForestClassifier¶ class sklearn.ensemble.RandomForestClassifier(n_estimators=100, *, criterion='gini', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features='sqrt', max_leaf_nodes=None, min_impurity_decrease=0.0, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, class_weight=None, ccp_a
scikit-learn Tutorials
Note Doctest Mode The code-examples in the above tutorials are written in a python-console format. If you wish to easily execute these examples in IPython, use: in the IPython-console. You can then simply copy and paste the examples directly into IPython without having to worry about removing the >>> manually.
Clustering text documents using k-means — scikit-learn 0.14 documentation
This documentation is for scikit-learn version 0.14 — Other versions If you use the software, please consider citing scikit-learn. Clustering text documents using k-means Clustering text documents using k-means¶ This is an example showing how the scikit-learn can be used to cluster documents by topics using a bag-of-words approach. This example uses a scipy.sparse matrix to store the features inst
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