[B! scikit_learn] moozã®ãƒ–ãƒƒã‚¯ãƒžãƒ¼ã‚¯

mooz id:mooz

scikit_learnã«é–¢ã™ã‚‹moozã®ãƒ–ãƒƒã‚¯ãƒžãƒ¼ã‚¯ (6)

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Scikit-learnã®pipeleine.PipelineãŒä¾¿åˆ© - KAZ log TechMemo
åˆ†æžã™ã‚‹éš›ã«ã€æ¬¡å…ƒåœ§ç¸®â†’åˆ†é¡žã®ã‚ˆã†ãªæµã‚Œã§è¡Œã†å ´åˆã«ã¯ã€scikit-learnã®PipelineãŒä¾¿åˆ©ã€‚ç‰¹ã«ãƒã‚¤ãƒ‘ãƒ¼ãƒ‘ãƒ©ãƒ¡ãƒ¼ã‚¿ãƒ¼ã‚’æŽ¢ã™ã¨ãã«ã¯æ‰‹ç¶šãŒç…©é›‘ã«ãªã‚‹ã“ã¨ã‚‚ã‚ã‚Šã¾ã™ãŒã€ã¾ã¨ã‚ã¦åˆ†é¡žå™¨ã¨ã—ã¦ã¦GridSearchCVã«çªã£è¾¼ã‚€ã ã‘ã§è‰¯ã„ã®ã§ã‚ã‚“ã©ãã•ã„ã“ã¨è€ƒãˆãšã«æ¸ˆã¿ã¾ã™ãã€‚ ä»Šå›žã¯Scikit-learnã®ã‚µãƒ³ãƒ—ãƒ«ãƒ‡ãƒ¼ã‚¿ã®ä¸ã‹ã‚‰ã€the digits datasetã®ãƒ‡ãƒ¼ã‚¿ã‚’ãƒãƒ¼ãƒ‰ã—ã€PCAã§ã®æ¬¡å…ƒåœ§ç¸®ã‹ã‚‰SVMã§ã®åˆ†é¡žã‚’Pipelineã§ã¾ã¨ã‚ã¦å®Ÿè¡Œã™ã‚‹æ‰‹é †ã‚’ç¢ºèªã—ã¦ã¿ã¾ã—ãŸã€‚ ã‚³ãƒ¼ãƒ‰ ã¾ãšã¯ãƒ‡ãƒ¼ã‚¿ã‚’èªã¿è¾¼ã¿ã€è¨“ç·´ãƒ‡ãƒ¼ã‚¿ã¨ãƒ†ã‚¹ãƒˆãƒ‡ãƒ¼ã‚¿ã«åˆ†å‰²ã€‚ ã¾ãŸæ‰‹æ›¸ãæ–‡å—ã®ãƒ‡ãƒ¼ã‚¿ãŒã©ã‚“ãªã‚‚ã®ã‹ã‚’æãå‡ºã—ã¾ã™ã€‚ import numpy as np from sklearn import svm from sklearn.decomposition import PCA from sklear
mooz 2017/12/03
scikit_learn
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Model selection: choosing estimators and their parameters
mooz 2016/09/10
scikit_learn
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https://scikit-learn.org/stable/modules/learning_curve.html
mooz 2016/05/29
scikit-learn ã§ validation curve

scikit_learn

machine_learning
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OneHotEncoder
mooz 2016/03/24
1-of-K è¡¨è¨˜ã«å¤‰ãˆã‚‹

python

scikit_learn
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Plot multi-class SGD on the iris dataset
Note Go to the end to download the full example code. or to run this example in your browser via JupyterLite or Binder Plot multi-class SGD on the iris dataset# Plot decision surface of multi-class SGD on iris dataset. The hyperplanes corresponding to the three one-versus-all (OVA) classifiers are represented by the dashed lines. # Authors: The scikit-learn developers # SPDX-License-Identifier: BS
mooz 2015/10/31
Drawing hyperplane and contour of multi-class classifier in scikit-learn

python

scikit_learn
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3.2. Tuning the hyper-parameters of an estimator
mooz 2013/09/09
ãƒ‘ãƒ©ãƒ¡ã‚¿ç©ºé–“ã®ã‚°ãƒªãƒƒãƒ‰ã‚µãƒ¼ãƒ

machine_learning

python

scikit_learn
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1