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[#]: subject: "Machine Learning: Classification Using Python"
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[#]: via: "https://www.opensourceforu.com/2022/05/machine-learning-classification-using-python/"
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[#]: author: "Gayatri Venugopal https://www.opensourceforu.com/author/gayatri-venugopal/"
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[#]: collector: "lkxed"
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[#]: translator: "geekpi"
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[#]: reviewer: " "
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[#]: publisher: " "
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[#]: url: " "
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Machine Learning: Classification Using Python
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======
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In machine learning (ML), a set of data is analysed to predict a result. Python is considered one of the best programming language choices for ML. In this article, we will discuss machine learning with respect to classification using Python.
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![machine-learning-classification][1]
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Let’s say you want to teach a child to differentiate between apples and oranges. There are various ways to do this. You could ask the child to touch both kinds of fruits so that they get familiar with the shape and the softness. You could also show her multiple examples of apples and oranges, so that they can visually spot the differences. The technological equivalent of this process is known as machine learning.
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Machine learning teaches computers to solve a particular problem, and to get better at it through experience. The example discussed here is a classification problem, where the machine is given various labelled examples, and is expected to label an unlabelled sample using the knowledge it acquired from the labelled samples. A machine learning problem can also take the form of regression, where it is expected to predict a real-valued solution to a given problem based on known samples and their solutions. Classification and regression are broadly termed as supervised learning. Machine learning can also be unsupervised, where the machine identifies patterns in unlabelled data, and forms clusters of samples with similar patterns. Another form of machine learning is reinforcement learning, where the machine learns from its environment by making mistakes.
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### Classification
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Classification is the process of predicting the label of a given set of points based on the information obtained from known points. The class, or label, associated with a data set could be binary or multiple in nature. As an example, if we have to label the sentiment associated with a sentence, we could label it as positive, negative or neutral. On the other hand, problems where we have to predict whether a fruit is an apple or an orange will have binary labels. Table 1 gives a sample data set for a classification problem.
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In this table, the value of the last column, i.e., loan approved, is expected to be predicted based on the other variables. In the subsequent sections, we will learn how to train and evaluate a classifier using Python.
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| - | - | - | - | - |
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| :- | :- | :- | :- | :- |
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| Age | Credit rating | Job | Property owned | Load approval |
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| 35 | good | yes | yes | yes |
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| 32 | poor | yes | no | no |
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| 22 | fair | no | no | no |
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| 42 | good | yes | no | yes |
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Table 1
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### Training and evaluating a classifier
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In order to train a classifier, we need to have a data set containing labelled examples. Though the process of cleaning the data is not covered in this section, it is recommended that you read about various data preprocessing and cleaning techniques before feeding your data set to a classifier. In order to process the data set in Python, we will import the pandas package and the data frame structure. You may then choose from a variety of classification algorithms such as decision tree, support vector classifier, random forest, XG boost, ADA boost, etc. We will look at the random forest classifier, which is an ensemble classifier formed using multiple decision trees.
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```
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from sklearn.ensemble import RandomForestClassifier
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from sklearn import metrics
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classifier = RandomForestClassifier()
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#creating a train-test split with a proportion of 70:30
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)
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classifier.fit(X_train, y_train) #train the classifier on the training set
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y_pred = classifier.predict(X_test) #evaluate the classifier on unknown data
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print(“Accuracy: “, metrics.accuracy_score(y_test, y_pred)) #compare the predictions with the actual values in the test set
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```
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Although this program uses accuracy as the performance metric, a combination of metrics should be used, as accuracy tends to generate non-representative results when the test set is imbalanced. For instance, we will get a high accuracy if the model gives the same prediction for every record and the data set that is used to test the model is imbalanced, i.e., most of the records in the data set have the same class that the model predicted.
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### Tuning a classifier
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Tuning refers to the process of modifying the values of the hyperparameters of a model in order to improve its performance. A hyperparameter is a parameter whose value can be changed to improve the learning process of the algorithm.
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The following code depicts random search hyperparameter tuning. In this, we define a search space from which the algorithm will pick different values, and choose the one that produces the best results:
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```
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from sklearn.model_selection import RandomizedSearchCV
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#define the search space
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min_samples_split = [2, 5, 10]
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min_samples_leaf = [1, 2, 4]
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grid = {‘min_samples_split’ : min_samples_split, ‘min_samples_leaf’ : min_samples_leaf}
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classifier = RandomizedSearchCV(classifier, grid, n_iter = 100)
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#n_iter represents the number of samples to extract from the search space
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#result.best_score and result.best_params_ can be used to obtain the best performance of the model, and the best values of the parameters
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classifier.fit(X_train, y_train)
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```
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### Voting classifier
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You can also use multiple classifiers and their predictions to create a model that will give a single prediction based on the individual predictions. This process (in which only the number of classifiers that voted for each prediction is considered) is called hard voting. Soft voting is a process in which each classifier generates a probability of a given record belonging to a particular class, and the voting classifier generates as its prediction, the class that obtained the maximum probability.
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A code snippet for creating a soft voting classifier is given below:
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```
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soft_voting_clf = VotingClassifier(
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estimators=[(‘rf’, rf_clf), (‘ada’, ada_clf), (‘xgb’, xgb_clf), (‘et’, et_clf), (‘gb’, gb_clf)],
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voting=’soft’)
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soft_voting_clf.fit(X_train, y_train)
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```
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This article has summarised the use of classifiers, tuning a classifier and the process of combining the results of multiple classifiers. Do use this as a reference point and explore each area in detail.
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--------------------------------------------------------------------------------
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via: https://www.opensourceforu.com/2022/05/machine-learning-classification-using-python/
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作者：[Gayatri Venugopal][a]
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选题：[lkxed][b]
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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://www.opensourceforu.com/author/gayatri-venugopal/
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[b]: https://github.com/lkxed
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[1]: https://www.opensourceforu.com/wp-content/uploads/2022/04/machine-learning-classification.jpg
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@ -0,0 +1,108 @@
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[#]: subject: "Machine Learning: Classification Using Python"
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[#]: via: "https://www.opensourceforu.com/2022/05/machine-learning-classification-using-python/"
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[#]: author: "Gayatri Venugopal https://www.opensourceforu.com/author/gayatri-venugopal/"
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[#]: collector: "lkxed"
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[#]: translator: "geekpi"
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[#]: reviewer: " "
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[#]: publisher: " "
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[#]: url: " "
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机器学习：使用 Python 进行分类
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======
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在机器学习 (ML) 中，分析一组数据以预测结果。 Python 被认为是 ML 的最佳编程语言选择之一。在本文中，我们将讨论使用 Python 进行分类的机器学习。
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![machine-learning-classification][1]
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假设你想教孩子区分苹果和橙子。有多种方法可以做到这一点。你可以让孩子触摸这两种水果，让他们熟悉形状和柔软度。你还可以向她展示苹果和橙子的多个例子，以便他们可以直观地发现差异。这个过程的技术等价物被称为机器学习。
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机器学习教计算机解决特定问题，并通过经验变得更好。这里讨论的示例是一个分类问题，其中机器被赋予各种标记示例，并期望使用它从标记样本中获得的知识来标记未标记样本。机器学习问题也可以采用回归的形式，其中期望根据已知样本及其解决方案来预测给定问题的实值解决方案。分类和回归被广泛称为监督学习。机器学习也可以是无监督的，机器识别未标记数据中的模式，并形成具有相似模式的样本集群。机器学习的另一种形式是强化学习，机器通过犯错从环境中学习。
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### 分类
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分类是根据从已知点获得的信息来预测一组给定点的标签的过程。与一个数据集相关的类别或标签可以是二元的，也可以是多元的。举例来说，如果我们必须给与一个句子相关的情绪打上标签，我们可以把它标记为正面、负面或中性。另一方面，我们必须预测一个水果是苹果还是橘子的问题将有二元标签。表 1 给出了一个分类问题的样本数据集。
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在该表中，最后一列的值，即贷款批准，预计将基于其他变量进行预测。在接下来的部分中，我们将学习如何使用 Python 训练和评估分类器。
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| - | - | - | - | - |
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| :- | :- | :- | :- | :- |
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| 年龄 | 信用等级 | 工作 | 拥有房产 | 贷款批准 |
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| 35 | 好 | 是 | 是 | 是 |
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| 32 | 差 | 是 | 不 | 不 |
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| 22 | 一般 | 不 | 不 | 不 |
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| 42 | 好 | 是 | 不 | 是 |
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表格 1
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### 训练和评估分类器
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为了训练分类器，我们需要一个包含标记示例的数据集。尽管本节不涉及清理数据的过程，但建议你在将数据集输入分类器之前阅读各种数据预处理和清理技术。为了在 Python 中处理数据集，我们将导入 pandas 包和 data frame 结构。然后，你可以从多种分类算法中进行选择，例如决策树、支持向量分类器、随机森林、XG boost、ADA boost 等。我们将看看随机森林分类器，它是使用多个决策树形成的集成分类器。
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```
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from sklearn.ensemble import RandomForestClassifier
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from sklearn import metrics
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classifier = RandomForestClassifier()
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#creating a train-test split with a proportion of 70:30
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)
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classifier.fit(X_train, y_train) #train the classifier on the training set
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y_pred = classifier.predict(X_test) #evaluate the classifier on unknown data
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print(“Accuracy: “, metrics.accuracy_score(y_test, y_pred)) #compare the predictions with the actual values in the test set
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```
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虽然这个程序使用准确性作为性能指标，但应该使用多种指标的组合，因为当测试集不平衡时，准确性往往会产生非代表性的结果。例如，如果模型对每条记录都给出了相同的预测，而用于测试模型的数据集是不平衡的，即数据集中的大多数记录与模型预测的类别相同，我们就会得到很高的准确率。
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### 调整分类器
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调优是指修改模型的超参数值以提高其性能的过程。超参数是可以改变其值以改进算法的学习过程的参数。
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以下代码描述了随机搜索超参数调整。在此，我们定义了一个搜索空间，算法将从该搜索空间中选择不同的值，并选择产生最佳结果的那个：
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```
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from sklearn.model_selection import RandomizedSearchCV
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#define the search space
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min_samples_split = [2, 5, 10]
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min_samples_leaf = [1, 2, 4]
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grid = {‘min_samples_split’ : min_samples_split, ‘min_samples_leaf’ : min_samples_leaf}
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classifier = RandomizedSearchCV(classifier, grid, n_iter = 100)
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#n_iter represents the number of samples to extract from the search space
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#result.best_score and result.best_params_ can be used to obtain the best performance of the model, and the best values of the parameters
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classifier.fit(X_train, y_train)
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```
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### 投票分类器
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你也可以使用多个分类器和它们的预测来创建一个模型，根据各个预测给出一个预测。这个过程（只考虑为每个预测投票的分类器的数量）被称为硬投票。软投票是一个过程，其中每个分类器产生一个给定记录属于特定类别的概率，而投票分类器产生的预测是获得最大概率的类别。
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A code snippet for creating a soft voting classifier is given below:
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```
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soft_voting_clf = VotingClassifier(
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estimators=[(‘rf’, rf_clf), (‘ada’, ada_clf), (‘xgb’, xgb_clf), (‘et’, et_clf), (‘gb’, gb_clf)],
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voting=’soft’)
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soft_voting_clf.fit(X_train, y_train)
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```
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这篇文章总结了分类器的使用，调整分类器和结合多个分类器的结果的过程。请将此作为一个参考点，详细探讨每个领域。
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--------------------------------------------------------------------------------
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via: https://www.opensourceforu.com/2022/05/machine-learning-classification-using-python/
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作者：[Gayatri Venugopal][a]
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选题：[lkxed][b]
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译者：[geekpi](https://github.com/geekpi)
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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://www.opensourceforu.com/author/gayatri-venugopal/
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[b]: https://github.com/lkxed
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[1]: https://www.opensourceforu.com/wp-content/uploads/2022/04/machine-learning-classification.jpg
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