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scikit-learn: machine learning in Python — scikit-learn 0.16.1 documentation

Scikit-learn 0.16 (Stable). Machine Learning in Python. Simple and efficient tools for data mining and data analysis. Accessible to everybody, and reusable in various contexts. Built on NumPy, SciPy, and matplotlib. Open source, commercially usable - BSD license. An introduction to machine learning with scikit-learn. Machine learning: the problem setting. Loading an example dataset. A tutorial on statistical-learning for scientific data processing. Nearest neighbor and the curse of dimensionality. Evalua...

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Scikit-learn 0.16 (Stable). Machine Learning in Python. Simple and efficient tools for data mining and data analysis. Accessible to everybody, and reusable in various contexts. Built on NumPy, SciPy, and matplotlib. Open source, commercially usable - BSD license. An introduction to machine learning with scikit-learn. Machine learning: the problem setting. Loading an example dataset. A tutorial on statistical-learning for scientific data processing. Nearest neighbor and the curse of dimensionality. Evalua...

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scikit-learn: machine learning in Python — scikit-learn 0.16.1 documentation | scikit-learn.org Reviews

https://scikit-learn.org

Scikit-learn 0.16 (Stable). Machine Learning in Python. Simple and efficient tools for data mining and data analysis. Accessible to everybody, and reusable in various contexts. Built on NumPy, SciPy, and matplotlib. Open source, commercially usable - BSD license. An introduction to machine learning with scikit-learn. Machine learning: the problem setting. Loading an example dataset. A tutorial on statistical-learning for scientific data processing. Nearest neighbor and the curse of dimensionality. Evalua...

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scikit-learn.org

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Choosing the right estimator — scikit-learn 0.17.1 documentation

http://scikit-learn.org/stable/tutorial/machine_learning_map/index.html

Scikit-learn 0.17 (stable). Scikit-learn 0.18 (development). Working With Text Data. This documentation is for scikit-learn version 0.17.1. Mdash; Other versions. If you use the software, please consider citing scikit-learn. Choosing the right estimator. Choosing the right estimator. Often the hardest part of solving a machine learning problem can be finding the right estimator for the job. Different estimators are better suited for different types of data and different problems.

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2.1. Gaussian mixture models — scikit-learn 0.17.1 documentation

http://scikit-learn.org/stable/modules/mixture.html

Scikit-learn 0.17 (stable). Scikit-learn 0.18 (development). This documentation is for scikit-learn version 0.17.1. Mdash; Other versions. If you use the software, please consider citing scikit-learn. 21 Gaussian mixture models. 2111 Pros and cons of class. 2112 Selecting the number of components in a classical GMM. 2113 Estimation algorithm Expectation-maximization. 212 VBGMM classifier: variational Gaussian mixtures. 2121 Pros and cons of class. 2122 Estimation algorithm: variational inference. Scikit-...

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Partial Dependence Plots — scikit-learn 0.17.1 documentation

http://scikit-learn.org/stable/auto_examples/ensemble/plot_partial_dependence.html

Scikit-learn 0.17 (stable). Scikit-learn 0.18 (development). Prediction Intervals for Gradient Boosting Regression. This documentation is for scikit-learn version 0.17.1. Mdash; Other versions. If you use the software, please consider citing scikit-learn. Partial dependence plots show the dependence between the target function [2]. This example shows how to obtain partial dependence plots from a. Trained on the California housing dataset. The example is taken from [1]. Avg occupants per household (.

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sklearn.tree.DecisionTreeClassifier — scikit-learn 0.17.1 documentation

http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html

Scikit-learn 0.17 (stable). Scikit-learn 0.18 (development). Sklearn.svm.libsvm.cross validation. This documentation is for scikit-learn version 0.17.1. Mdash; Other versions. If you use the software, please consider citing scikit-learn. Min weight fraction leaf=0.0. A decision tree classifier. Read more in the User Guide. String, optional (default=”gini”). String, optional (default=”best”). Int, float, string or None, optional (default=None). If int, then consider. Features at each split. If float, then.

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1.9. Naive Bayes — scikit-learn 0.17.1 documentation

http://scikit-learn.org/stable/modules/naive_bayes.html

Scikit-learn 0.17 (stable). Scikit-learn 0.18 (development). This documentation is for scikit-learn version 0.17.1. Mdash; Other versions. If you use the software, please consider citing scikit-learn. 191 Gaussian Naive Bayes. 192 Multinomial Naive Bayes. 193 Bernoulli Naive Bayes. 194 Out-of-core naive Bayes model fitting. And a dependent feature vector. Bayes’ theorem states the following relationship:. Using the naive independence assumption that. This relationship is simplified to. In the training set.

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mblondel.org

Mathieu Blondel // Publications

http://www.mblondel.org/publications

Optimization for machine learning. Akinori Fujino, Naonori Ueda, Masakazu Ishihata. In Proceedings of Neural Information Processing Systems ( NIPS. Polynomial Networks and Factorization Machines: New Insights and Efficient Training Algorithms. Masakazu Ishihata, Akinori Fujino, Naonori Ueda. In Proceedings of International Conference on Machine Learning ( ICML. Akinori Fujino, Naonori Ueda. Large-scale Multiclass Support Vector Machine Training via Euclidean Projection onto the Simplex. Journal of Machin...

adtech-blog.united.jp

8月, 2015 | UNITED アドテクブログ

http://adtech-blog.united.jp/archives/date/2015/08

ソフトウェアテスト software test は、コンピュータのプログラムを実行し、正しく動作するか、目標とした品質に到達しているか、意図しない動作をしないかどうかを確認する作業のことである。プログラムを書く - 動作確認する - 正しく動かなければ修正 - 再度動作確認する - …. 単体テストユニットテストと呼ばれることもありますは、プログラムを構成する比較的小さな単位ユニットが個々の機能を正しく果たしているかどうかを検証するテストです。 Def add five(x) return x 5. Assert add five(3) = 8. H = get nested array() assert isinstance(h, dict). あくまでユニットテストを書くのは手段にすぎず目的ではないということです。 Hadoop / Spark Conference 2016 参加報告. Proudly powered by WordPress.

abelardresearch.com

abelard | Abelard Lindsay's CILTEP, Nootropics and Brain Hacking Blog

http://www.abelardresearch.com/author/abelard

Abelard Lindsay's CILTEP, Nootropics and Brain Hacking Blog. About Me / Contact. All posts by abelard. Maganese and Alpha Synuclein Research. August 5, 2016. Peres, T. V., Parmalee, N. L., Martinez-Finley, E. J., and Aschner, M. (2016). Untangling the Manganese-α-Synuclein Web. Front. Neurosci. Frontiers in Neuroscience. 10 doi:10.3389/fnins.2016.00364. The metals that cause alpha-synuclein to tangle up and cause disease are aluminum, copper, cadmium, iron, manganese, and zinc ( Paik et al., 1999. Weight...

tutoriels-data-mining.blogspot.com

Tutoriels Tanagra pour le Data Mining et le Data Science: Python - La distribution Anaconda

http://tutoriels-data-mining.blogspot.com/2015/08/python-la-distribution-anaconda.html

Tutoriels Tanagra pour le Data Mining et le Data Science. Mise à niveau SISE. Vendredi 7 août 2015. Python - La distribution Anaconda. Arrivé à ce stade de mon cours (cf. les séances précédentes), je souhaitais l’orienter vers le calcul scientifique, en particulier la programmation statistique. J’avais identifié quelques packages comme les incontournables numpy. D’autres également avaient attiré mon attention, pandas. Et je l’ai trouvée en Anaconda. Là également avec des fonctionnalités supplémentaires.

sachithdhanushka.blogspot.com

Sachith's Matrix: Mining Twitter Data using Python: Getting Started

http://sachithdhanushka.blogspot.com/2014/02/mining-twitter-data-using-python.html

I can only show you the door. You're the one that has to walk through it. Saturday, February 15, 2014. Mining Twitter Data using Python: Getting Started. Data Mining is a hot topic these days, and Twitter is being used heavily as a data source in various Data Mining applications. In this post I will introduce you to start mining twitter data with Python using the Tweepy module. 1 Install python ( MacOS comes with python installed). 2 Get a Twitter API key. Go to https:/ dev.twitter.com/. Import tweepy au...

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scikit-bio.org

scikit-bio

Scikit-bio is an open-source, BSD-licensed, python package providing data structures, algorithms, and educational resources for bioinformatics. Scikit-bio is currently in beta. We are very actively developing it, and backward-incompatible interface changes can and will arise. For more details, including what we mean by. To install the latest release of scikit-bio:. Equivalently, you can use the. Package manager available in Anaconda. You can verify your installation by running the scikit-bio unit tests:.

scikit-criteria.org

Indices and tables — Scikit-Criteria 0.0.1 documentation

Scikit-criteria is a collection of Multiple-criteria decision analysis ( MCDA. Methods integrated into scientific python stack. Built on NumPy, SciPy, and matplotlib. Open source, commercially usable - BSD license. Provided by Read the Docs. On Read the Docs. Free document hosting provided by Read the Docs.

scikit-image.org

scikit-image: Image processing in Python — scikit-image

Image processing in Python. Is a collection of algorithms for image processing. It is available free of charge and free of restriction. We pride ourselves on high-quality, peer-reviewed code, written by an active community of volunteers. Filtering an image with. For more examples, please visit our gallery. Or any NumPy array! If you find this project useful, please cite:. PeerJ 2:e453 (2014) http:/ dx.doi.org/10.7717/peerj.453. Version 0.11.0 04/03/2015. Version 0.10.0 27/05/2014. Belgium, August 2012.

scikit-learn.org

scikit-learn: machine learning in Python — scikit-learn 0.16.1 documentation

Scikit-learn 0.16 (Stable). Machine Learning in Python. Simple and efficient tools for data mining and data analysis. Accessible to everybody, and reusable in various contexts. Built on NumPy, SciPy, and matplotlib. Open source, commercially usable - BSD license. An introduction to machine learning with scikit-learn. Machine learning: the problem setting. Loading an example dataset. A tutorial on statistical-learning for scientific data processing. Nearest neighbor and the curse of dimensionality. Evalua...

scikit-multilearn.github.io

scikit-multilearn | Multi-label classification package for python

Multi-label classification package for python. Download 0.0.1. A native Python implementation of a variety of multi-label classification algorithms. The list includes:. Label cooccurence-based partitioning clasifiers. Hierarchy of Multi-label Classifiers [in-progress]. Classifier chains and others [in-progress]. For reference purposes and integration needs a Meka wrapper class is implemented. Thus providing access to all methods available in meka, mulan and weka - the reference standard of the field.

scikit-nano.org

scikit-nano

The scikit-nano Project is an open-source Python toolkit for nanoscience. Current Version: 0.3.21. You can install the latest version of scikit-nano using pip. Or by downloading the source code and installing it manually - see the Source. Tab above for more details. You can install the latest version of scikit-nano using pip. Or by downloading the source code and installing it manually - see the Source. Tab above for more details. You can install the latest version of scikit-nano using pip. Check out the...

scikit-optimize.github.io

skopt API documentation

Store and load results. Is a simple and efficient library for sequential model-based optimization, accessible to everybody and reusable in various contexts. The library is built on top of NumPy, SciPy and Scikit-Learn. Find the minimum of the noisy function. For more read our introduction to bayesian optimization. And the other examples. The library is still experimental and under heavy development. The development version can be installed through:. Run the tests by executing. In the top level directory.

scikitechture.blogspot.com

Technorazzi: Updates on Science and Technology

Technorazzi: Updates on Science and Technology. Friday, March 19, 2010. IPad: Screenwriter's best friend? Maricris V. Faderugao. From Kindle to Nook and now, ipod to ipad. These days, you need not to buy a book, smell the ink and spines to read. The necessity for information is growing fast in today's fast-paced way of living and we cannot let mother nature to suffer. Still, we have to keep up with the demand. Scott Stein on CNET news wrote:. IPad: Screenwriter's best friend? I'm not the only one. On the...

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SCI Nilvia Sorocaba e Região. Sábado, 2 de julho de 2016. Nossa ATIVAÇÃO é apenas se alimentar com uma CESTA de CONSUMO ao mês, e ainda a CUSTO ZERO! SCI a maior e melhor Oportunidade que já aconteceu. SCI Nilvia Sorocaba e Região. Compartilhar com o Pinterest. Nossa ATIVAÇÃO é apenas se alimentar com uma CESTA de CONSUMO ao mês, e ainda a CUSTO ZERO! SCI a maior e melhor Oportunidade que já aconteceu. SCI Nilvia Sorocaba e Região. Compartilhar com o Pinterest. Quarta-feira, 27 de janeiro de 2016. Seja v...