scikit-learn是python进行数据挖掘与分析的重要而有效的工具，学习scikit-learn最重要的资料来源就是它的官网：

scikit-learn: machine learning in Python

一进官网就能看到它的6大功能：

classification

Regression

Clustering

Dimensionality reduction

Model selection

Preprocessing

它的API模块包括：

sklearn.base: Base classes and utility function

sklearn.cluster: Clustering

sklearn.cluster.bicluster: Biclustering

sklearn.covariance: Covariance Estimators

sklearn.model_selection: Model Selection

sklearn.datasets: Datasets

sklearn.decomposition: Matrix Decomposition

sklearn.dummy: Dummy estimators

sklearn.ensemble: Ensemble Methods

sklearn.exceptions: Exceptions and warnings

sklearn.feature_extraction: Feature Extraction

sklearn.feature_selection: Feature Selection

sklearn.gaussian_process: Gaussian Processes

sklearn.isotonic: Isotonic regression

sklearn.kernel_approximation: Kernel Approximation

sklearn.kernel_ridge: Kernel Ridge Regression

sklearn.discriminant_analysis: Discriminant Analysis

sklearn.linear_model: Generalized Linear Models

sklearn.manifold: Manifold Learning

sklearn.metrics: Metrics

sklearn.mixture: Gaussian Mixture Models

sklearn.multiclass: Multiclass and multilabel classification

sklearn.multioutput: Multioutput regression and classification

sklearn.naive_bayes: Naive Bayes

sklearn.neighbors: Nearest Neighbors

sklearn.neural_network: Neural network models

sklearn.calibration: Probability Calibration

sklearn.cross_decomposition: Cross decomposition

sklearn.pipeline: Pipeline

sklearn.preprocessing: Preprocessing and Normalization

sklearn.random_projection: Random projection

sklearn.semi_supervised: Semi-Supervised Learning

sklearn.svm: Support Vector Machines

sklearn.tree: Decision Tree

sklearn.utils: Utilities

经常玩数据比赛的人就会发现classification，Regression，Clustering，Dimensionality reduction，Model selection，Preprocessing这几样都会经常用到的。尤其是classification和Regression，不过如果你直接用他们进行分类或者回归，不对数据进行预处理和其他的操作的话，成绩通常不太会太好。

其实scikit-learn学习还真没有太多好说的，因为文档写的很好，而且例代码也很多，一般情况拿过例代码，调一调就好了。

比如你想试一下linear_model的Ridge，直接访问：

1.1. Linear Models

看一下Ridge的说明，如果想试一下，里面的例代码都写好了：

>>> from sklearn import linear_model

>>> reg = linear_model.Ridge (alpha = .5)

>>> reg.fit ([[0, 0], [0, 0], [1, 1]], [0, .1, 1])

Ridge(alpha=0.5, copy_X=True, fit_intercept=True, max_iter=None,

normalize=False, random_state=None, solver='auto', tol=0.001)

>>> reg.coef_

array([ 0.34545455, 0.34545455])

>>> reg.intercept_

0.13636...

用起来的顺序大概就是

1，拿过来，跑一下，理解一下。

2，改一改，代入自己的数据，跑一下，看看结果。

3，调一调参数，优化一下。

如果你在想了解函数的参数怎么回事，点击这个文档里这个函数的链接就行了啊。比如ridgeCV:

3.2.4.1.9. sklearn.linear_model.RidgeCV - scikit-learn 0.22 documentation

就酱，你已经成为一个合格的调包侠了。