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sklearn · PyPI
Jul 15, 2015 · sklearn 0.0 pip install sklearn Copy PIP instructions. Latest version. Released: Jul 15, 2015 A set of python modules for machine learning and data mining. Navigation. Project description Release history Download files Project links. Homepage Statistics. View statistics for ...
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scikit-learn: machine learning in Python — scikit-learn 1.1.2 …
Preprocessing. Feature extraction and normalization. Applications: Transforming input data such as text for use with machine learning algorithms. Algorithms: preprocessing, feature extraction, and …
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7. Dataset loading utilities — scikit-learn 1.1.2 documentation
7. Dataset loading utilities¶. The sklearn.datasets package embeds some small toy datasets as introduced in the Getting Started section.. This package also features helpers to fetch larger datasets commonly used by the machine learning community to benchmark algorithms on data that comes from the ‘real world’.
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sklearn-onnx: Convert your scikit-learn model into ONNX
Related converters. sklearn-onnx only converts models from scikit-learn.onnxmltools can be used to convert models for libsvm, lightgbm, xgboost.Other converters can be found on github/onnx, torch.onnx, ONNX-MXNet API, Microsoft.ML.Onnx…. Credits. The package was started by the following engineers and data scientists at Microsoft starting from winter 2017: Zeeshan …
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sklearn.cluster.KMeans — scikit-learn 1.1.2 documentation
sklearn.cluster.KMeans¶ class sklearn.cluster. KMeans (n_clusters = 8, *, init = 'k-means++', n_init = 10, max_iter = 300, tol = 0.0001, verbose = 0, random_state = None, copy_x = True, algorithm = 'lloyd') [source] ¶. K-Means clustering. Read more in the User Guide.. Parameters: n_clusters int, default=8. The number of clusters to form as well as the number of centroids to …
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GitHub - scikit-learn/scikit-learn: scikit-learn: machine learning in
Scikit-learn 0.20 was the last version to support Python 2.7 and Python 3.4. scikit-learn 1.0 and later require Python 3.7 or newer. scikit-learn 1.1 and later require Python 3.8 or newer. Scikit-learn plotting capabilities (i.e., functions start with plot_ and classes end with "Display") require Matplotlib (>= 3.1.2). For running the examples Matplotlib >= 3.1.2 is required.
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sklearn.metrics.auc — scikit-learn 1.1.2 documentation
sklearn.metrics.auc¶ sklearn.metrics. auc (x, y) [source] ¶ Compute Area Under the Curve (AUC) using the trapezoidal rule. This is a general function, given points on a curve. For computing the area under the ROC-curve, see roc_auc_score. For an alternative way to summarize a precision-recall curve, see average_precision_score. Parameters:
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automl/auto-sklearn: Automated Machine Learning with scikit-learn - GitHub
Jul 20, 2015 · auto-sklearn. auto-sklearn is an automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator. Find the documentation here. Quick links: Installation Guide; Releases; Manual; Examples; API; auto-sklearn …
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Classifier comparison — scikit-learn 1.1.2 documentation
# Code source: Gaël Varoquaux # Andreas Müller # Modified for documentation by Jaques Grobler # License: BSD 3 clause import numpy as np import matplotlib.pyplot as plt from matplotlib.colors import ListedColormap from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.datasets import ...
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