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[translated by Flowsnow] How to use the Scikit-learn Python library for data science projects
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How to use the Scikit-learn Python library for data science projects
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======
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The Scikit-learn Python library, initially released in 2007, is commonly used in solving machine learning and data science problems—from the beginning to the end. The versatile library offers an uncluttered, consistent, and efficient API and thorough online documentation.
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### What is Scikit-learn?
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[Scikit-learn][1] is an open source Python library that has powerful tools for data analysis and data mining. It's available under the BSD license and is built on the following machine learning libraries:
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* **NumPy** , a library for manipulating multi-dimensional arrays and matrices. It also has an extensive compilation of mathematical functions for performing various calculations.
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* **SciPy** , an ecosystem consisting of various libraries for completing technical computing tasks.
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* **Matplotlib** , a library for plotting various charts and graphs.
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Scikit-learn offers an extensive range of built-in algorithms that make the most of data science projects.
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Here are the main ways the Scikit-learn library is used.
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#### 1. Classification
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The [classification][2] tools identify the category associated with provided data. For example, they can be used to categorize email messages as either spam or not.
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* Support vector machines (SVMs)
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* Nearest neighbors
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* Random forest
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#### 2. Regression
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Classification algorithms in Scikit-learn include:
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Regression involves creating a model that tries to comprehend the relationship between input and output data. For example, regression tools can be used to understand the behavior of stock prices.
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Regression algorithms include:
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* SVMs
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* Ridge regression
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* Lasso
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#### 3. Clustering
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The Scikit-learn clustering tools are used to automatically group data with the same characteristics into sets. For example, customer data can be segmented based on their localities.
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Clustering algorithms include:
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* K-means
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* Spectral clustering
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* Mean-shift
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#### 4. Dimensionality reduction
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Dimensionality reduction lowers the number of random variables for analysis. For example, to increase the efficiency of visualizations, outlying data may not be considered.
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Dimensionality reduction algorithms include:
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* Principal component analysis (PCA)
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* Feature selection
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* Non-negative matrix factorization
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#### 5. Model selection
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Model selection algorithms offer tools to compare, validate, and select the best parameters and models to use in your data science projects.
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Model selection modules that can deliver enhanced accuracy through parameter tuning include:
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* Grid search
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* Cross-validation
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* Metrics
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#### 6. Preprocessing
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The Scikit-learn preprocessing tools are important in feature extraction and normalization during data analysis. For example, you can use these tools to transform input data—such as text—and apply their features in your analysis.
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Preprocessing modules include:
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* Preprocessing
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* Feature extraction
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### A Scikit-learn library example
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Let's use a simple example to illustrate how you can use the Scikit-learn library in your data science projects.
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We'll use the [Iris flower dataset][3], which is incorporated in the Scikit-learn library. The Iris flower dataset contains 150 details about three flower species:
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* Setosa—labeled 0
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* Versicolor—labeled 1
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* Virginica—labeled 2
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The dataset includes the following characteristics of each flower species (in centimeters):
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* Sepal length
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* Sepal width
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* Petal length
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* Petal width
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#### Step 1: Importing the library
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Since the Iris dataset is included in the Scikit-learn data science library, we can load it into our workspace as follows:
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```
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from sklearn import datasets
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iris = datasets.load_iris()
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```
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These commands import the **datasets** module from **sklearn** , then use the **load_digits()** method from **datasets** to include the data in the workspace.
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#### Step 2: Getting dataset characteristics
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The **datasets** module contains several methods that make it easier to get acquainted with handling data.
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In Scikit-learn, a dataset refers to a dictionary-like object that has all the details about the data. The data is stored using the **.data** key, which is an array list.
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For instance, we can utilize **iris.data** to output information about the Iris flower dataset.
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```
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print(iris.data)
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```
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Here is the output (the results have been truncated):
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```
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[[5.1 3.5 1.4 0.2]
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[4.9 3. 1.4 0.2]
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[4.7 3.2 1.3 0.2]
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[4.6 3.1 1.5 0.2]
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[5. 3.6 1.4 0.2]
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[5.4 3.9 1.7 0.4]
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[4.6 3.4 1.4 0.3]
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[5. 3.4 1.5 0.2]
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[4.4 2.9 1.4 0.2]
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[4.9 3.1 1.5 0.1]
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[5.4 3.7 1.5 0.2]
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[4.8 3.4 1.6 0.2]
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[4.8 3. 1.4 0.1]
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[4.3 3. 1.1 0.1]
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[5.8 4. 1.2 0.2]
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[5.7 4.4 1.5 0.4]
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[5.4 3.9 1.3 0.4]
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[5.1 3.5 1.4 0.3]
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```
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Let's also use **iris.target** to give us information about the different labels of the flowers.
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```
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print(iris.target)
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```
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Here is the output:
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```
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[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
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0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
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1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2
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2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
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2 2]
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```
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If we use **iris.target_names** , we'll output an array of the names of the labels found in the dataset.
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```
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print(iris.target_names)
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```
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Here is the result after running the Python code:
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```
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['setosa' 'versicolor' 'virginica']
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```
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#### Step 3: Visualizing the dataset
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We can use the [box plot][4] to produce a visual depiction of the Iris flower dataset. The box plot illustrates how the data is distributed over the plane through their quartiles.
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Here's how to achieve this:
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```
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import seaborn as sns
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box_data = iris.data #variable representing the data array
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box_target = iris.target #variable representing the labels array
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sns.boxplot(data = box_data,width=0.5,fliersize=5)
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sns.set(rc={'figure.figsize':(2,15)})
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```
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Let's see the result:
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On the horizontal axis:
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* 0 is sepal length
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* 1 is sepal width
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* 2 is petal length
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* 3 is petal width
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The vertical axis is dimensions in centimeters.
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### Wrapping up
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Here is the entire code for this simple Scikit-learn data science tutorial.
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```
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from sklearn import datasets
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iris = datasets.load_iris()
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print(iris.data)
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print(iris.target)
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print(iris.target_names)
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import seaborn as sns
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box_data = iris.data #variable representing the data array
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box_target = iris.target #variable representing the labels array
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sns.boxplot(data = box_data,width=0.5,fliersize=5)
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sns.set(rc={'figure.figsize':(2,15)})
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```
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Scikit-learn is a versatile Python library you can use to efficiently complete data science projects.
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If you want to learn more, check out the tutorials on [LiveEdu][5], such as Andrey Bulezyuk's video on using the Scikit-learn library to create a [machine learning application][6].
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Do you have any questions or comments? Feel free to share them below.
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--------------------------------------------------------------------------------
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via: https://opensource.com/article/18/9/how-use-scikit-learn-data-science-projects
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作者：[Dr.Michael J.Garbade][a]
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选题：[lujun9972](https://github.com/lujun9972)
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译者：[译者ID](https://github.com/译者ID)
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校对：[校对者ID](https://github.com/校对者ID)
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本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
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[a]: https://opensource.com/users/drmjg
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[1]: http://scikit-learn.org/stable/index.html
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[2]: https://blog.liveedu.tv/regression-versus-classification-machine-learning-whats-the-difference/
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[3]: https://en.wikipedia.org/wiki/Iris_flower_data_set
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[4]: https://en.wikipedia.org/wiki/Box_plot
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[5]: https://www.liveedu.tv/guides/data-science/
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[6]: https://www.liveedu.tv/andreybu/REaxr-machine-learning-model-python-sklearn-kera/oPGdP-machine-learning-model-python-sklearn-kera/
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@ -0,0 +1,238 @@
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如何将Scikit-learn Python库用于数据科学项目
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======
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Scikit-learn Python库最初于2007年发布，从头到尾都通常用于解决机器学习和数据科学问题。 多功能库提供整洁，一致，高效的API和全面的在线文档。
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### 什么是Scikit-learn？
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[Scikit-learn][1]是一个开源Python库，拥有强大的数据分析和数据挖掘工具。 在BSD许可下可用，并建立在以下机器学习库上：
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- **NumPy**，一个用于操作多维数组和矩阵的库。 它还具有广泛的数学函数汇集，可用于执行各种计算。
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- **SciPy**，一个由各种库组成的生态系统，用于完成技术计算任务。
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- **Matplotlib**，一个用于绘制各种图表和图形的库。
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Scikit-learn提供了广泛的内置算法，可以充分用于数据科学项目。
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以下是使用Scikit-learn库的主要方法。
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#### 1. 分类
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[分类][2]工具识别与提供的数据相关联的类别。 例如，它们可用于将电子邮件分类为垃圾邮件或非垃圾邮件。
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Scikit-learn中的分类算法包括：
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- 支持向量机（SVM）
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- 最邻近
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- 随机森林
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#### 2. 回归
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回归涉及到创建一个模型去试图理解输入和输出数据之间的关系。 例如，回归工具可用于了解股票价格的行为。
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回归算法包括：
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- SVM
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- 岭回归Ridge regression
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- Lasso（LCTT译者注：Lasso 即 least absolute shrinkage and selection operator，又译最小绝对值收敛和选择算子、套索算法）
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#### 3. 聚类
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Scikit-learn聚类工具用于自动将具有相同特征的数据分组。 例如，可以根据客户数据的地点对客户数据进行细分。
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聚类算法包括：
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- K-means
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- 谱聚类Spectral clustering
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- Mean-shift
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#### 4. 降维
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降维降低了用于分析的随机变量的数量。 例如，为了提高可视化效率，可能不会考虑外围数据。
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降维算法包括：
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- 主成分分析Principal component analysis（PCA）
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- 功能选择Feature selection
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- 非负矩阵分解Non-negative matrix factorization
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#### 5. 模型选择
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模型选择算法提供了用于比较，验证和选择要在数据科学项目中使用的最佳参数和模型的工具。
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通过参数调整能够增强精度的模型选择模块包括：
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- 网格搜索Grid search
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- 交叉验证Cross-validation
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- 指标Metrics
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#### 6. 预处理
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Scikit-learn预处理工具在数据分析期间的特征提取和规范化中非常重要。 例如，您可以使用这些工具转换输入数据（如文本）并在分析中应用其特征。
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预处理模块包括：
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- 预处理
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- 特征提取
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### Scikit-learn库示例
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让我们用一个简单的例子来说明如何在数据科学项目中使用Scikit-learn库。
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我们将使用[鸢尾花花卉数据集][3]，该数据集包含在Scikit-learn库中。 鸢尾花数据集包含有关三种花种的150个细节，三种花种分别为：
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- Setosa-标记为0
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- Versicolor-标记为1
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- Virginica-标记为2
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数据集包括每种花种的以下特征（以厘米为单位）：
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- 萼片长度
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- 萼片宽度
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- 花瓣长度
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- 花瓣宽度
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#### 第1步：导入库
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由于Iris数据集包含在Scikit-learn数据科学库中，我们可以将其加载到我们的工作区中，如下所示：
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```
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from sklearn import datasets
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iris = datasets.load_iris()
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```
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这些命令从**sklearn**导入数据集**datasets**模块，然后使用**datasets**中的**load_iris()**方法将数据包含在工作空间中。
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#### 第2步：获取数据集特征
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数据集**datasets**模块包含几种方法，使您更容易熟悉处理数据。
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在Scikit-learn中，数据集指的是类似字典的对象，其中包含有关数据的所有详细信息。 使用**.data**键存储数据，该数据列是一个数组列表。
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例如，我们可以利用**iris.data**输出有关Iris花卉数据集的信息。
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```
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print(iris.data)
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```
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这是输出（结果已被截断）：
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```
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[[5.1 3.5 1.4 0.2]
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[4.9 3. 1.4 0.2]
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[4.7 3.2 1.3 0.2]
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[4.6 3.1 1.5 0.2]
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[5. 3.6 1.4 0.2]
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[5.4 3.9 1.7 0.4]
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[4.6 3.4 1.4 0.3]
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[5. 3.4 1.5 0.2]
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[4.4 2.9 1.4 0.2]
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[4.9 3.1 1.5 0.1]
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[5.4 3.7 1.5 0.2]
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[4.8 3.4 1.6 0.2]
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[4.8 3. 1.4 0.1]
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[4.3 3. 1.1 0.1]
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[5.8 4. 1.2 0.2]
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[5.7 4.4 1.5 0.4]
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[5.4 3.9 1.3 0.4]
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[5.1 3.5 1.4 0.3]
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```
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我们还使用**iris.target**向我们提供有关花朵不同标签的信息。
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```
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print(iris.target)
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```
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这是输出：
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```
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[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
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0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
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1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2
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2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
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2 2]
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```
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如果我们使用**iris.target_names**，我们将输出数据集中找到的标签名称的数组。
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```
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print(iris.target_names)
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```
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以下是运行Python代码后的结果：
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```
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['setosa' 'versicolor' 'virginica']
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```
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#### 第3步：可视化数据集
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||||
我们可以使用[箱形图][4]来生成鸢尾花数据集的视觉描绘。 箱形图说明了数据如何通过四分位数在平面上分布的。
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||||
以下是如何实现这一目标：
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```
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import seaborn as sns
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box_data = iris.data # 表示数据数组的变量
|
||||
box_target = iris.target # 表示标签数组的变量
|
||||
sns.boxplot(data = box_data,width=0.5,fliersize=5)
|
||||
sns.set(rc={'figure.figsize':(2,15)})
|
||||
```
|
||||
|
||||
让我们看看结果：
|
||||
|
||||
|
||||
|
||||
在横轴上：
|
||||
|
||||
* 0是萼片长度
|
||||
* 1是萼片宽度
|
||||
* 2是花瓣长度
|
||||
* 3是花瓣宽度
|
||||
|
||||
垂直轴的尺寸以厘米为单位。
|
||||
|
||||
### 总结
|
||||
|
||||
以下是这个简单的Scikit-learn数据科学教程的完整代码。
|
||||
|
||||
```
|
||||
from sklearn import datasets
|
||||
iris = datasets.load_iris()
|
||||
print(iris.data)
|
||||
print(iris.target)
|
||||
print(iris.target_names)
|
||||
import seaborn as sns
|
||||
box_data = iris.data # 表示数据数组的变量
|
||||
box_target = iris.target # 表示标签数组的变量
|
||||
sns.boxplot(data = box_data,width=0.5,fliersize=5)
|
||||
sns.set(rc={'figure.figsize':(2,15)})
|
||||
```
|
||||
|
||||
Scikit-learn是一个多功能的Python库，可用于高效完成数据科学项目。
|
||||
|
||||
如果您想了解更多信息，请查看[LiveEdu][5]上的教程，例如Andrey Bulezyuk关于使用Scikit-learn库创建[机器学习应用程序][6]的视频。
|
||||
|
||||
有什么评价或者疑问吗？ 欢迎在下面分享。
|
||||
|
||||
--------------------------------------------------------------------------------
|
||||
|
||||
via: https://opensource.com/article/18/9/how-use-scikit-learn-data-science-projects
|
||||
|
||||
作者：[Dr.Michael J.Garbade][a]
|
||||
选题：[lujun9972](https://github.com/lujun9972)
|
||||
译者：[Flowsnow](https://github.com/Flowsnow)
|
||||
校对：[校对者ID](https://github.com/校对者ID)
|
||||
|
||||
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
|
||||
|
||||
[a]: https://opensource.com/users/drmjg
|
||||
[1]: http://scikit-learn.org/stable/index.html
|
||||
[2]: https://blog.liveedu.tv/regression-versus-classification-machine-learning-whats-the-difference/
|
||||
[3]: https://en.wikipedia.org/wiki/Iris_flower_data_set
|
||||
[4]: https://en.wikipedia.org/wiki/Box_plot
|
||||
[5]: https://www.liveedu.tv/guides/data-science/
|
||||
[6]: https://www.liveedu.tv/andreybu/REaxr-machine-learning-model-python-sklearn-kera/oPGdP-machine-learning-model-python-sklearn-kera/
|
||||
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