sklearn_src
sklearn,
software that uses scikit-learn, which
is a Python-based library for machine learning computations.
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blob_classify_kernelized_svm,
a scikit-learn code which
uses a kernelized support vector machine to classify an
artificial dataset of groups of "blobs".
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blob_classify_logistic_multi,
a scikit-learn code which
uses multiple applications of logistic regression to classify an
artificial dataset of three groups of "blobs".
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blob_cluster_kmeans,
a scikit-learn code which
uses the k-means algorithm to cluster blob data.
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cancer_classify_decision,
a scikit-learn code which
uses a decision tree algorithm to classify the breast cancer dataset,
comparing the training and testing accuracy as the depth of the
tree is varied.
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cancer_classify_forest,
a scikit-learn code which
uses the random forest algorithm to classify
the breast cancer dataset.
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cancer_classify_gradboost,
a scikit-learn code which
uses the gradient boosting algorithm to classify
the breast cancer dataset.
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cancer_classify_knn,
a scikit-learn code which
uses the k-nearest neighbor algorithm to classify
the breast cancer dataset,
comparing the training and testing accuracy as the number of
neighbors is increased.
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cancer_classify_logistic,
a scikit-learn code which
uses logistic regression to classify the breast cancer dataset,
investigating the influence of the C parameter.
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cancer_classify_mlp,
a scikit-learn code which
uses a multilayer perceptron to classify the breast cancer dataset.
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cancer_classify_svm_rbf,
a scikit-learn code which
uses the support vector algorithm with RBF kernel on
the cancer dataset, showing that the data should be rescaled
to avoid overfitting.
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cancer_scale_minmax,
a scikit-learn code which
uses the min-max scaling to preprocess the cancer dataset.
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cancer_visualize_histogram,
a scikit-learn code which
displays all 30 features of the cancer dataset as histograms
of feature frequence for malignant versus benign cases.
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cancer_visualize_pca,
a scikit-learn code which
uses principal component analysis (PCA) of the cancer dataset
to visualize the difference between malignant and benign cases.
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circle_classify_gradboost,
a scikit-learn code which
uses the gradient boost algorithm to classify the artificial
circle dataset, and then determines the prediction uncertainties.
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digits_visualize_pca,
a scikit-learn code which
uses principal component analysis (PCA) of the digits dataset
to visualize the grouping of data.
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digits_visualize_tsne,
a scikit-learn code which
uses t-distributed stochastic neighbor embedding (tsne) of the digits
dataset to visualize the grouping of data.
-
faces_classify_knn,
a scikit-learn code which
uses the k-nearest neighbor algorithm to match new faces
with images in the faces dataset.
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faces_classify_nmf,
a scikit-learn code which
uses the nonnegative matrix factorizatoin algorithm to match new faces
with images in the faces dataset.
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faces_classify_pca,
a scikit-learn code which
uses principal component analysis (PCA) to match new faces
with images in the faces dataset.
-
handcrafted_classify_svm_rbf,
a scikit-learn code which
uses the support vector algorithm with RBF kernel on
the handcrafted dataset.
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forge_classify_knn,
a scikit-learn code which
uses the k-nearest neighbor algorithm to choose one of two
classes for each of 26 items in the forge dataset,
involving two features.
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forge_classify_svm,
a scikit-learn code which
uses the support vector machine (SCM) classifier to choose one of two
classes for each of 26 items in the forge dataset,
involving two features.
-
handcrafted_classify_svm_rbf,
a scikit-learn code which
uses the support vector algorithm with RBF kernel on
the handcrafted dataset.
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housing_data_fetch,
a scikit-learn code which
fetches a housing dataset from GitHub and stores it locally.
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iris_classify_gradboost,
a scikit-learn code which
uses the gradient boost algorithm to classify the iris
dataset, and then determines the prediction uncertainties.
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iris_classify_knn,
a scikit-learn code which
uses the k-nearest neighbor algorithm to classify the species of
iris specimens based on a set of 150 sets of four measurements
sepal and petal width and length.
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logistic_regression,
a scikit-learn code which
use logistic regression to classify data.
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moon_classify_forest,
a scikit-learn code which
uses the random forest algorithm to classify samples of the
artificial moon dataset.
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moon_classify_mlp,
a scikit-learn code which
uses a multilayer perceptron method to classify samples of the
artificial moon dataset.
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ram_regression_decision,
a scikit-learn code which
uses a decision tree algorithm to perform regression
on the RAM price dataset.
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ram_regression_linear,
a scikit-learn code which
uses linear regresssion to perform regression
on the RAM price dataset.
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signal_classify_nmf,
a scikit-learn code which
uses non-negative matrix factorization (nmf) to match new signals
to items in the signal dataset.
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study_classify_logistic,
a scikit-learn code which
uses the logistic regression algorithm to classify the outcome
of students based on study time.
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tester,
a BASH script which runs the tests.
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wave_regression_knn,
a scikit-learn code which
uses the k-nearest neighbor algorithm to form a regression predictor
for the wave dataset.
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wave_regression_ols,
a scikit-learn code which
uses the ordinary least squares algorithm to form a regression
predictor for the wave dataset.
Last revised on 28 March 2024.