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2.1. Gaussian mixture models — scikit-learn 1.7...
sklearn.mixture is a package which enables one to learn Gaussian Mixture Models (diagonal, spherical, tied and full covariance matrices supported), sample them, and estimate them from data. Facilit...scikit-learn.org/stable/modules/mixture.html -
Post pruning decision trees with cost complexit...
The DecisionTreeClassifier provides parameters such as min_samples_leaf and max_depth to prevent a tree from overfiting. Cost complexity pruning provides another option to control the size of a tre...scikit-learn.org/stable/auto_examples/tree/plot_cost_complexity_pruning.html -
Comparison of LDA and PCA 2D projection of Iris...
The Iris dataset represents 3 kind of Iris flowers (Setosa, Versicolour and Virginica) with 4 attributes: sepal length, sepal width, petal length and petal width. Principal Component Analysis (PCA)...scikit-learn.org/stable/auto_examples/decomposition/plot_pca_vs_lda.html -
scikit-learn: machine learning in Python — scik...
Skip to main content Back to top Ctrl + K scikit-learn Machine Learning in Python Getting Started Release Highlights ...scikit-learn.org/stable/index.html -
7.1. Pipelines and composite estimators — sciki...
To build a composite estimator, transformers are usually combined with other transformers or with predictors(such as classifiers or regressors). The most common tool used for composing estimators i...scikit-learn.org/stable/modules/compose.html -
Plot the decision surfaces of ensembles of tree...
Plot the decision surfaces of forests of randomized trees trained on pairs of features of the iris dataset. This plot compares the decision surfaces learned by a decision tree classifier (first col...scikit-learn.org/stable/auto_examples/ensemble/plot_forest_iris.html -
Factor Analysis (with rotation) to visualize pa...
Investigating the Iris dataset, we see that sepal length, petal length and petal width are highly correlated. Sepal width is less redundant. Matrix decomposition techniques can uncover these latent...scikit-learn.org/stable/auto_examples/decomposition/plot_varimax_fa.html -
Comparing Random Forests and Histogram Gradient...
In this example we compare the performance of Random Forest (RF) and Histogram Gradient Boosting (HGBT) models in terms of score and computation time for a regression dataset, though all the concep...scikit-learn.org/stable/auto_examples/ensemble/plot_forest_hist_grad_boosting_comparison.html -
An example of K-Means++ initialization — scikit...
An example to show the output of the sklearn.cluster.kmeans_plusplus function for generating initial seeds for clustering. K-Means++ is used as the default initialization for K-means. Total running...scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_plusplus.html -
14. External Resources, Videos and Talks — scik...
The scikit-learn MOOC: If you are new to scikit-learn, or looking to strengthen your understanding, we highly recommend the scikit-learn MOOC (Massive Open Online Course). The MOOC, created and mai...scikit-learn.org/stable/presentations.html