AI实践哪家强?来 AICon, 解锁技术前沿,探寻产业新机! 了解详情
写点什么

如何用 Python 构建机器学习模型?

  • 2021-05-20
  • 本文字数:3137 字

    阅读完需:约 10 分钟

如何用Python构建机器学习模型?

本文,我们将通过 Python 语言包,来构建一些机器学习模型。

构建机器学习模型的模板


该 Notebook 包含了用于创建主要机器学习算法所需的代码模板。在 scikit-learn 中,我们已经准备好了几个算法。只需调整参数,给它们输入数据,进行训练,生成模型,最后进行预测。

1.线性回归


对于线性回归,我们需要从 sklearn 库中导入 linear_model。我们准备好训练和测试数据,然后将预测模型实例化为一个名为线性回归 LinearRegression 算法的对象,它是 linear_model 包的一个类,从而创建预测模型。之后我们利用拟合函数对算法进行训练,并利用得分来评估模型。最后,我们将系数打印出来,用模型进行新的预测。


# Import modulesfrom sklearn import linear_model
# Create training and test subsetsx_train = train_dataset_predictor_variablesy_train = train_dataset_predicted_variable
x_test = test_dataset_precictor_variables
# Create linear regression objectlinear = linear_model.LinearRegression()
# Train the model with training data and check the scorelinear.fit(x_train, y_train)linear.score(x_train, y_train)
# Collect coefficientsprint('Coefficient: \n', linear.coef_)print('Intercept: \n', linear.intercept_)
# Make predictionspredicted_values = linear.predict(x_test)
复制代码

2.逻辑回归


在本例中,从线性回归到逻辑回归唯一改变的是我们要使用的算法。我们将 LinearRegression 改为 LogisticRegression。


# Import modulesfrom sklearn.linear_model import LogisticRegression
# Create training and test subsetsx_train = train_dataset_predictor_variablesy_train = train_dataset_predicted_variable
x_test = test_dataset_precictor_variables
# Create logistic regression objectmodel = LogisticRegression()
# Train the model with training data and checking the scoremodel.fit(x_train, y_train)model.score(x_train, y_train)
# Collect coefficientsprint('Coefficient: \n', model.coef_)print('Intercept: \n', model.intercept_)
# Make predictionspredicted_vaues = model.predict(x_teste)
复制代码


3.决策树


我们再次将算法更改为 DecisionTreeRegressor:


# Import modulesfrom sklearn import tree
# Create training and test subsetsx_train = train_dataset_predictor_variablesy_train = train_dataset_predicted_variable
x_test = test_dataset_precictor_variables
# Create Decision Tree Regressor Objectmodel = tree.DecisionTreeRegressor()
# Create Decision Tree Classifier Objectmodel = tree.DecisionTreeClassifier()
# Train the model with training data and checking the scoremodel.fit(x_train, y_train)model.score(x_train, y_train)
# Make predictionspredicted_values = model.predict(x_test)
复制代码


4.朴素贝叶斯


我们再次将算法更改为 DecisionTreeRegressor:


# Import modulesfrom sklearn.naive_bayes import GaussianNB
# Create training and test subsetsx_train = train_dataset_predictor_variablesy_train = train_dataset_predicted variable
x_test = test_dataset_precictor_variables
# Create GaussianNB objectmodel = GaussianNB()
# Train the model with training data model.fit(x_train, y_train)
# Make predictionspredicted_values = model.predict(x_test)
复制代码


5.支持向量机


在本例中,我们使用 SVM 库的 SVC 类。如果是 SVR,它就是一个回归函数:


# Import modulesfrom sklearn import svm
# Create training and test subsetsx_train = train_dataset_predictor_variablesy_train = train_dataset_predicted variable
x_test = test_dataset_precictor_variables
# Create SVM Classifier object model = svm.svc()
# Train the model with training data and checking the scoremodel.fit(x_train, y_train)model.score(x_train, y_train)
# Make predictionspredicted_values = model.predict(x_test)
复制代码


6.K- 最近邻


在 KneighborsClassifier 算法中,我们有一个超参数叫做 n_neighbors,就是我们对这个算法进行调整。


# Import modulesfrom sklearn.neighbors import KNeighborsClassifier
# Create training and test subsetsx_train = train_dataset_predictor_variablesy_train = train_dataset_predicted variable
x_test = test_dataset_precictor_variables
# Create KNeighbors Classifier Objects KNeighborsClassifier(n_neighbors = 6) # default value = 5
# Train the model with training datamodel.fit(x_train, y_train)
# Make predictionspredicted_values = model.predict(x_test)
复制代码


7.K- 均值


# Import modulesfrom sklearn.cluster import KMeans
# Create training and test subsetsx_train = train_dataset_predictor_variablesy_train = train_dataset_predicted variable
x_test = test_dataset_precictor_variables
# Create KMeans objects k_means = KMeans(n_clusters = 3, random_state = 0)
# Train the model with training datamodel.fit(x_train)
# Make predictionspredicted_values = model.predict(x_test)
复制代码


8.随机森林


# Import modulesfrom sklearn.ensemble import RandomForestClassifier
# Create training and test subsetsx_train = train_dataset_predictor_variablesy_train = train_dataset_predicted variable
x_test = test_dataset_precictor_variables
# Create Random Forest Classifier objects model = RandomForestClassifier()
# Train the model with training data model.fit(x_train, x_test)
# Make predictionspredicted_values = model.predict(x_test)
复制代码


9.降维


# Import modulesfrom sklearn import decomposition
# Create training and test subsetsx_train = train_dataset_predictor_variablesy_train = train_dataset_predicted variable
x_test = test_dataset_precictor_variables
# Creating PCA decomposition objectpca = decomposition.PCA(n_components = k)
# Creating Factor analysis decomposition objectfa = decomposition.FactorAnalysis()
# Reduc the size of the training set using PCAreduced_train = pca.fit_transform(train)
# Reduce the size of the training set using PCAreduced_test = pca.transform(test)
复制代码


10.梯度提升和 AdaBoost


# Import modulesfrom sklearn.ensemble import GradientBoostingClassifier
# Create training and test subsetsx_train = train_dataset_predictor_variablesy_train = train_dataset_predicted variable
x_test = test_dataset_precictor_variables
# Creating Gradient Boosting Classifier objectmodel = GradientBoostingClassifier(n_estimators = 100, learning_rate = 1.0, max_depth = 1, random_state = 0)
# Training the model with training data model.fit(x_train, x_test)
# Make predictionspredicted_values = model.predict(x_test)
复制代码


我们的工作将是把这些算法中的每一个块转化为一个项目。首先,定义一个业务问题,对数据进行预处理,训练算法,调整超参数,获得可验证的结果,在这个过程中不断迭代,直到我们达到满意的精度,做出理想的预测。


原文链接:


https://levelup.gitconnected.com/10-templates-for-building-machine-learning-models-with-notebook-282c4eb0987f

2021-05-20 16:012814

评论

发布
暂无评论
发现更多内容

记一次混合云API发布的反思

雪雷

iptables API api发布

API 中签名的使用

架构精进之路

接口安全

SonarQube集成gitlab/jenkins

雪雷

jenkins sonar gitlab ci 代码扫描

Serverless初探

雪雷

Serverless Lambda 无服务器云函数

业务容器化改造

雪雷

Docker 容器 微服务 服务化改造

API统一管理平台-YApi

雪雷

YAPI API接口管理

Flink高可用性设置-4

小知识点

scala 大数据 flink 流计算

Elasticsearch安装

北漂码农有话说

MySQL线程状态详解

Simon

MySQL 线程状态

Guacamole实战

雪雷

guacamole 远程登录 堡垒机

RabbitMQ实践

雪雷

RabbitMQ 消息队列

JVM-技术专题-GCViewer调优GC

码界西柚

JVM

探测mysqldump详细过程

Simon

MySQL

同态加密

soolaugust

学习 加密 同态加密

微服务API网关-Kong详解

雪雷

kong api 网关

Jenkins 详解

雪雷

jenkins

Apache常用配置指北

亻尔可真木奉

Apache 代理 跨域

Golang领域模型-开篇

奔奔奔跑

微服务 后端 领域驱动设计 架构设计 Go 语言

lower_case_table_names参数详解

Simon

MySQL

JVM-技术专题-管程技术分析

码界西柚

JVM 管程

性能优化-技术专题-并发编程

码界西柚

Java 多线程

在java中使用SPI创建可扩展的应用程序

程序那些事

Java spi 可扩展程序 可扩展应用

Gitlab Pipeline+Supervisor 实战Python项目CI/CD

雪雷

gitlab jenkins CI/CD Supervisor

记一次混合监控的反思

雪雷

监控 zabbix redis监控 监控宝

Linux自定义快捷工具

雪雷

Linux Shell tools scripts

Docker Web管理工具

雪雷

Docker shipyard dockerui

Python利用sphinx构建个人博客

雪雷

sphinx Blog

Linux系统检查脚本

雪雷

Shell 系统检测

Docker+Jenkins+Gitlab+Django应用部署实践

雪雷

DevOps jenkins CI/CD

Ceph集群部署

雪雷

分布式存储 Ceph rdb pvc

Jenkins部署Python项目实战

雪雷

Python jenkins CI/CD

如何用Python构建机器学习模型?_AI&大模型_Anello_InfoQ精选文章