简介： ...................................................................... 2
主要特点：................................................................... 2
scikit-learn安装：（ubuntu版本14.04.1） ......................................... 2
Classification .............................................................. 2
1.监督学习 ........................................................... 2
1.1广义线性模型： ................................................ 2
1.2 支持向量机 ................................................... 9
1.3 随机梯度下降 ................................................ 10
1.4 最近邻 ...................................................... 10
1.5 Gaussian Processes ............................................. 15
1.6 Cross decomposition ............................................ 16
1.7 Naive Bayes .................................................. 16
1.8 Decision Trees ................................................. 17
1.9 Ensemble methods ............................................. 20
1.10 Multiclass and multilabel algorithms .............................. 25
1.11 Feature selection .............................................. 26
1.14 Isotonic regression ............................................ 29
2. .................................................................. 29
2.3 Clustering .................................................... 29
2.5 Decomposing signals in components (matrix factorization problems) ...... 32
3.Model selection and evaluation ......................................... 32
3.1 Cross-validation: evaluating estimator performance .................... 32
3.2 Grid Search: Searching for estimator parameters ...................... 35
3.3 Pipeline: chaining estimators ..................................... 37
3.4 FeatureUnion: Combining feature extractors ......................... 38
3.5. Model evaluation: quantifying the quality of predictions ............... 38
3.6. Model persistence .............................................. 42
3.7. Validation curves: plotting scores to evaluate models .................. 43
4................................................................... 44
4.2 Preprocessing data .............................................. 44
4.4 Random Projection ............................................. 49
