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      How To Provision and Manage Remote Docker Hosts with Docker Machine on Ubuntu 18.04


      Introduction

      [Docker Machine](/) is a tool that makes it easy to provision and manage multiple Docker hosts remotely from your personal computer. Such servers are commonly referred to as Dockerized hosts and are used to run Docker containers.

      While Docker Machine can be installed on a local or a remote system, the most common approach is to install it on your local computer (native installation or virtual machine) and use it to provision Dockerized remote servers.

      Though Docker Machine can be installed on most Linux distributions as well as macOS and Windows, in this tutorial, you’ll install it on your local machine running Ubuntu 18.04 and use it to provision Dockerized DigitalOcean Droplets. If you don’t have a local Ubuntu 18.04 machine, you can follow these instructions on any Ubuntu 18.04 server.

      Prerequisites

      To follow this tutorial, you will need the following:

      • A local machine or server running Ubuntu 18.04 with Docker installed. See How To Install and Use Docker on Ubuntu 18.04 for instructions.
      • A DigitalOcean API token. If you don’t have one, generate it using this guide. When you generate a token, be sure that it has read-write scope. That is the default, so if you do not change any options while generating it, it will have read-write capabilities.

      Step 1 — Installing Docker Machine

      In order to use Docker Machine, you must first install it locally. On Ubuntu, this means downloading a handful of scripts from the official Docker repository on GitHub.

      To download and install the Docker Machine binary, type:

      • wget https://github.com/docker/machine/releases/download/v0.15.0/docker-machine-$(uname -s)-$(uname -m)

      The name of the file should be docker-machine-Linux-x86_64. Rename it to docker-machine to make it easier to work with:

      • mv docker-machine-Linux-x86_64 docker-machine

      Make it executable:

      Move or copy it to the /usr/local/bin directory so that it will be available as a system command:

      • sudo mv docker-machine /usr/local/bin

      Check the version, which will indicate that it's properly installed:

      You'll see output similar to this, displaying the version number and build:

      Output

      docker-machine version 0.15.0, build b48dc28d

      Docker Machine is installed. Let's install some additional helper tools to make Docker Machine easier to work with.

      Step 2 — Installing Additional Docker Machine Scripts

      There are three Bash scripts in the Docker Machine GitHub repository you can install to make working with the docker and docker-machine commands easier. When installed, these scripts provide command completion and prompt customization.

      In this step, you'll install these three scripts into the /etc/bash_completion.d directory on your local machine by downloading them directly from the Docker Machine GitHub repository.

      Note: Before downloading and installing a script from the internet in a system-wide location, you should inspect the script's contents first by viewing the source URL in your browser.

      The first script allows you to see the active machine in your prompt. This comes in handy when you are working with and switching between multiple Dockerized machines. The script is called docker-machine-prompt.bash. Download it

      • sudo wget https://raw.githubusercontent.com/docker/machine/master/contrib/completion/bash/docker-machine-prompt.bash -O /etc/bash_completion.d/docker-machine-prompt.bash

      To complete the installation of this file, you'll have to modify the value for the PS1 variable in your .bashrc file. The PS1 variable is a special shell variable used to modify the Bash command prompt. Open ~/.bashrc in your editor:

      Within that file, there are three lines that begin with PS1. They should look just like these:

      ~/.bashrc

      PS1='${debian_chroot:+($debian_chroot)}[33[01;32m]u@h[33[00m]:[33[01;34m]w[33[00m]$ '
      
      ...
      
      PS1='${debian_chroot:+($debian_chroot)}u@h:w$ '
      
      ...
      
      PS1="[e]0;${debian_chroot:+($debian_chroot)}u@h: wa]$PS1"
      

      For each line, insert $(__docker_machine_ps1 " [%s]") near the end, as shown in the following example:

      ~/.bashrc

      PS1='${debian_chroot:+($debian_chroot)}[33[01;32m]u@h[33[00m]:[33[01;34m]w[33[00m]$(__docker_machine_ps1 " [%s]")$ '
      
      ...
      
      PS1='${debian_chroot:+($debian_chroot)}u@h:w$(__docker_machine_ps1 " [%s]")$ '
      
      ...
      
      PS1="[e]0;${debian_chroot:+($debian_chroot)}u@h: wa]$(__docker_machine_ps1 " [%s]")$PS1"
      

      Save and close the file.

      The second script is called docker-machine-wrapper.bash. It adds a use subcommand to the docker-machine command, making it significantly easier to switch between Docker hosts. To download it, type:

      • sudo wget https://raw.githubusercontent.com/docker/machine/master/contrib/completion/bash/docker-machine-wrapper.bash -O /etc/bash_completion.d/docker-machine-wrapper.bash

      The third script is called docker-machine.bash. It adds bash completion for docker-machine commands. Download it using:

      • sudo wget https://raw.githubusercontent.com/docker/machine/master/contrib/completion/bash/docker-machine.bash -O /etc/bash_completion.d/docker-machine.bash

      To apply the changes you've made so far, close, then reopen your terminal. If you're logged into the machine via SSH, exit the session and log in again, and you'll have command completion for the docker and docker-machine commands.

      Let's test things out by creating a new Docker host with Docker Machine.

      Step 3 — Provisioning a Dockerized Host Using Docker Machine

      Now that you have Docker and Docker Machine running on your local machine, you can provision a Dockerized Droplet on your DigitalOcean account using Docker Machine's docker-machine create command. If you've not done so already, assign your DigitalOcean API token to an environment variable:

      • export DOTOKEN=your-api-token

      NOTE: This tutorial uses DOTOKEN as the bash variable for the DO API token. The variable name does not have to be DOTOKEN, and it does not have to be in all caps.

      To make the variable permanent, put it in your ~/.bashrc file. This step is optional, but it is necessary if you want to the value to persist across shell sessions.

      Open that file with nano:

      Add this line to the file:

      ~/.bashrc

      export DOTOKEN=your-api-token
      

      To activate the variable in the current terminal session, type:

      To call the docker-machine create command successfully you must specify the driver you wish to use, as well as a machine name. The driver is the adapter for the infrastructure you're going to create. There are drivers for cloud infrastructure providers, as well as drivers for various virtualization platforms.

      We'll use the digitalocean driver. Depending on the driver you select, you'll need to provide additional options to create a machine. The digitalocean driver requires the API token (or the variable that evaluates to it) as its argument, along with the name for the machine you want to create.

      To create your first machine, type this command to create a DigitalOcean Droplet called docker-01:

      • docker-machine create --driver digitalocean --digitalocean-access-token $DOTOKEN docker-01

      You'll see this output as Docker Machine creates the Droplet:

      Output

      ... Installing Docker... Copying certs to the local machine directory... Copying certs to the remote machine... Setting Docker configuration on the remote daemon... Checking connection to Docker... Docker is up and running! To see how to connect your Docker Client to the Docker Engine running on this virtual machine, run: docker-machine env ubuntu1804-docker

      Docker Machine creates an SSH key pair for the new host so it can access the server remotely. The Droplet is provisioned with an operating system and Docker is installed. When the command is complete, your Docker Droplet is up and running.

      To see the newly-created machine from the command line, type:

      The output will be similar to this, indicating that the new Docker host is running:

      Output

      NAME ACTIVE DRIVER STATE URL SWARM DOCKER ERRORS docker-01 - digitalocean Running tcp://209.97.155.178:2376 v18.06.1-ce

      Now let's look at how to specify the operating system when we create a machine.

      Step 4 — Specifying the Base OS and Droplet Options When Creating a Dockerized Host

      By default, the base operating system used when creating a Dockerized host with Docker Machine is supposed to be the latest Ubuntu LTS. However, at the time of this publication, the docker-machine create command is still using Ubuntu 16.04 LTS as the base operating system, even though Ubuntu 18.04 is the latest LTS edition. So if you need to run Ubuntu 18.04 on a recently-provisioned machine, you'll have to specify Ubuntu along with the desired version by passing the --digitalocean-image flag to the docker-machine create command.

      For example, to create a machine using Ubuntu 18.04, type:

      • docker-machine create --driver digitalocean --digitalocean-image ubuntu-18-04-x64 --digitalocean-access-token $DOTOKEN docker-ubuntu-1804

      You're not limited to a version of Ubuntu. You can create a machine using any operating system supported on DigitalOcean. For example, to create a machine using Debian 8, type:

      • docker-machine create --driver digitalocean --digitalocean-image debian-8-x64 --digitalocean-access-token $DOTOKEN docker-debian

      To provision a Dockerized host using CentOS 7 as the base OS, specify centos-7-0-x86 as the image name, like so:

      • docker-machine create --driver digitalocean --digitalocean-image centos-7-0-x64 --digitalocean-access-token $DOTOKEN docker-centos7

      The base operating system is not the only choice you have. You can also specify the size of the Droplet. By default, it is the smallest Droplet, which has 1 GB of RAM, a single CPU, and a 25 GB SSD.

      Find the size of the Droplet you want to use by looking up the corresponding slug in the DigitalOcean API documentation.

      For example, to provision a machine with 2 GB of RAM, two CPUs, and a 60 GB SSD, use the slug s-2vcpu-2gb:

      • docker-machine create --driver digitalocean --digitalocean-size s-2vcpu-2gb --digitalocean-access-token $DOTOKEN docker-03

      To see all the flags specific to creating a Docker Machine using the DigitalOcean driver, type:

      • docker-machine create --driver digitalocean -h

      Tip: If you refresh the Droplet page of your DigitalOcean dashboard, you will see the new machines you created using the docker-machine command.

      Now let's explore some of the other Docker Machine commands.

      Step 5 — Executing Additional Docker Machine Commands

      You've seen how to provision a Dockerized host using the create subcommand, and how to list the hosts available to Docker Machine using the ls subcommand. In this step, you'll learn a few more useful subcommands.

      To obtain detailed information about a Dockerized host, use the inspect subcommand, like so:

      • docker-machine inspect docker-01

      The output includes lines like the ones in the following output. The Image line reveals the version of the Linux distribution used and the Size line indicates the size slug:

      Output

      ... { "ConfigVersion": 3, "Driver": { "IPAddress": "203.0.113.71", "MachineName": "docker-01", "SSHUser": "root", "SSHPort": 22, ... "Image": "ubuntu-16-04-x64", "Size": "s-1vcpu-1gb", ... }, ---

      To print the connection configuration for a host, type:

      • docker-machine config docker-01

      The output will be similar to this:

      Output

      --tlsverify --tlscacert="/home/kamit/.docker/machine/certs/ca.pem" --tlscert="/home/kamit/.docker/machine/certs/cert.pem" --tlskey="/home/kamit/.docker/machine/certs/key.pem" -H=tcp://203.0.113.71:2376

      The last line in the output of the docker-machine config command reveals the IP address of the host, but you can also get that piece of information by typing:

      • docker-machine ip docker-01

      If you need to power down a remote host, you can use docker-machine to stop it:

      • docker-machine stop docker-01

      Verify that it is stopped:

      The output shows that the status of the machine has changed:

      Ouput

      NAME ACTIVE DRIVER STATE URL SWARM DOCKER ERRORS docker-01 - digitalocean Stopped Unknown

      To start it again, use the start subcommand:

      • docker-machine start docker-01

      Then review its status again:

      You will see that the STATE is now set Running for the host:

      Ouput

      NAME ACTIVE DRIVER STATE URL SWARM DOCKER ERRORS docker-01 - digitalocean Running tcp://203.0.113.71:2376 v18.06.1-ce

      Next let's look at how to interact with the remote host using SSH.

      Step 6 — Executing Commands on a Dockerized Host via SSH

      At this point, you've been getting information about your machines, but you can do more than that. For example, you can execute native Linux commands on a Docker host by using the ssh subcommand of docker-machine from your local system. This section explains how to perform ssh commands via docker-machine as well as how to open an SSH session to a Dockerized host.

      Assuming that you've provisioned a machine with Ubuntu as the operating system, execute the following command from your local system to update the package database on the Docker host:

      • docker-machine ssh docker-01 apt-get update

      You can even apply available updates using:

      • docker-machine ssh docker-01 apt-get upgrade

      Not sure what kernel your remote Docker host is using? Type the following:

      • docker-machine ssh docker-01 uname -r

      Finally, you can log in to the remote host with the docker machine ssh command:

      docker-machine ssh docker-01
      

      You'll be logged in as the root user and you'll see something similar to the following:

      Welcome to Ubuntu 16.04.5 LTS (GNU/Linux 4.4.0-131-generic x86_64)
      
       * Documentation:  https://help.ubuntu.com
       * Management:     https://landscape.canonical.com
       * Support:        https://ubuntu.com/advantage
      
        Get cloud support with Ubuntu Advantage Cloud Guest:
          http://www.ubuntu.com/business/services/cloud
      
      14 packages can be updated.
      10 updates are security updates.
      

      Log out by typing exit to return to your local machine.

      Next, we'll direct Docker's commands at our remote host.

      Step 7 — Activating a Dockerized Host

      Activating a Docker host connects your local Docker client to that system, which makes it possible to run normal docker commands on the remote system.

      First, use Docker Machine to create a new Docker host called docker-ubuntu using Ubuntu 18.04:

      • docker-machine create --driver digitalocean --digitalocean-image ubuntu-18-04-x64 --digitalocean-access-token $DOTOKEN docker-ubuntu

      To activate a Docker host, type the following command:

      • eval $(docker-machine env machine-name)

      Alternatively, you can activate it by using this command:

      • docker-machine use machine-name

      Tip When working with multiple Docker hosts, the docker-machine use command is the easiest method of switching from one to the other.

      After typing any of these commands, your prompt will change to indicate that your Docker client is pointing to the remote Docker host. It will take this form. The name of the host will be at the end of the prompt:

      username@localmachine:~ [docker-01]$
      

      Now any docker command you type at this command prompt will be executed on that remote host.

      Execute docker-machine ls again:

      You'll see an asterisk under the ACTIVE column for docker-01:

      Output

      NAME ACTIVE DRIVER STATE URL SWARM DOCKER ERRORS docker-01 * digitalocean Running tcp://203.0.113.71:2376 v18.06.1-ce

      To exit from the remote Docker host, type the following:

      Your prompt will no longer show the active host.

      Now let's create containers on the remote machine.

      Step 8 — Creating Docker Containers on a Remote Dockerized Host

      So far, you have provisioned a Dockerized Droplet on your DigitalOcean account and you've activated it — that is, your Docker client is pointing to it. The next logical step is to spin up containers on it. As an example, let's try running the official Nginx container.

      Use docker-machine use to select your remote machine:

      • docker-machine use docker-01

      Now execute this command to run an Nginx container on that machine:

      • docker run -d -p 8080:80 --name httpserver nginx

      In this command, we're mapping port 80 in the Nginx container to port 8080 on the Dockerized host so that we can access the default Nginx page from anywhere.

      Once the container builds, you will be able to access the default Nginx page by pointing your web browser to http://docker_machine_ip:8080.

      While the Docker host is still activated (as seen by its name in the prompt), you can list the images on that host:

      The output includes the Nginx image you just used:

      Output

      REPOSITORY TAG IMAGE ID CREATED SIZE nginx latest 71c43202b8ac 3 hours ago 109MB

      You can also list the active or running containers on the host:

      If the Nginx container you ran in this step is the only active container, the output will look like this:

      Output

      CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES d3064c237372 nginx "nginx -g 'daemon of…" About a minute ago Up About a minute 0.0.0.0:8080->80/tcp httpserver

      If you intend to create containers on a remote machine, your Docker client must be pointing to it — that is, it must be the active machine in the terminal that you're using. Otherwise you'll be creating the container on your local machine. Again, let your command prompt be your guide.

      Docker Machine can create and manage remote hosts, and it can also remove them.

      Step 9 – Removing Docker Hosts

      You can use Docker Machine to remove a Docker host you've created. Use the docker-machine rm command to remove the docker-01 host you created:

      • docker-machine rm docker-01

      The Droplet is deleted along with the SSH key created for it. List the hosts again:

      This time, you won't see the docker-01 host listed in the output. And if you've only created one host, you won't see any output at all.

      Be sure to execute the command docker-machine use -u to point your local Docker daemon back to your local machine.

      Step 10 — Disabling Crash Reporting (Optional)

      By default, whenever an attempt to provision a Dockerized host using Docker Machine fails, or Docker Machine crashes, some diagnostic information is sent to a Docker account on Bugsnag. If you're not comfortable with this, you can disable the reporting by creating an empty file called no-error-report in your local computer's .docker/machine directory.

      To create the file, type:

      • touch ~/.docker/machine/no-error-report

      Check the file for error messages if provisioning fails or Docker Machine crashes.

      Conclusion

      You've installed Docker Machine and used it to provision multiple Docker hosts on DigitalOcean remotely from your local system. From here you should be able to provision as many Dockerized hosts on your DigitalOcean account as you need.

      For more on Docker Machine, visit the official documentation page. The three Bash scripts downloaded in this tutorial are hosted on this GitHub page.



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      Como Construir um Classificador de Machine Learning em Python com Scikit-learn


      Introdução

      Machine learning ou Aprendizado de máquina é um campo de pesquisa em ciência da computação, inteligência artificial, e estatística. O foco do Machine Learning é treinar algoritmos para aprender padrões e fazer previsões a partir de dados. Machine learning é especialmente valioso porque ele nos leva a utilizar computadores para automatizar o processo de tomada de decisões.

      Você encontrará aplicações de Machine learning em todos os lugares. Netflix e Amazon usam machine learning para fazer novas recomendações de produtos. Bancos usam machine learning para detectar atividades fraudulentas em transações de cartões de crédito, e empresas de assistência à saúde estão começando a usar machine learning para monitorar, avaliar e diagnosticar pacientes.

      Neste tutorial vamos implementar um algoritmo simples de machine learning em Python utilizando Scikit-learn, uma ferramenta de machine learning para Python. Usando um banco de dados de informações sobre tumores de câncer de mama, iremos usar um classificador Naive Bayes (NB) que prevê se um tumor é maligno ou benigno.

      No final deste tutorial, você saberá como construir o seu próprio modelo de machine learning em Python.

      Pré-requisitos

      Para completar este tutorial, você precisará de:

      Passo 1 — Importando o Scikit-learn

      Vamos começar instalando o módulo Python Scikit-learn, um das melhores e mais bem documentadas bibliotecas de machine learning para Python.

      Para começar com nosso projeto de codificação, vamos ativar nosso ambiente de programação Python 3. Certifique-se de estar no diretório onde o seu ambiente está localizado, e execute o seguinte comando:

      Com seu ambiente de programação ativado, verifique se o módulo Scikit-learn já está instalado:

      • python -c "import sklearn"

      Se o sklearn estiver instalado, este comando irá completar sem erros. Se ele não estiver instalado, você verá a seguinte mensagem de erro:

      Output

      Traceback (most recent call last): File "<string>", line 1, in <module> ImportError: No module named 'sklearn'

      A mensagem de erro indica que o módulo sklearn não está instalado, então baixe o biblioteca usando o pip:

      • pip install scikit-learn[alldeps]

      Quando a instalação estiver concluída, inicie o Jupyter Notebook:

      No Jupyter, crie um novo Python Notebook chamado ML Tutorial. Na primeira célula do Notebook, importe o módulo sklearn.

      ML Tutorial

      
      import sklearn
      
      

      Seu notebook deve se parecer com a figura a seguir:

      Agora que temos o sklearn importado em nosso notebook, podemos começar a trabalhar com o dataset para o nosso modelo de machine learning.

      Passo 2 — Importando o Dataset do Scikit-learn

      O dataset com o qual estaremos trabalhando neste tutorial é o Breast Cancer Wisconsin Diagnostic Database. O dataset inclui várias informações sobre tumores de câncer de mama, bem como rótulos de classificação como malignos ou benignos. O dataset tem 569 instâncias, ou dados, sobre 569 tumores e inclui informações sobre 30 atributos, ou características, tais como o raio do tumor, textura, suavidade, e área.

      Utilizando este dataset, construiremos um modelo de machine learning para utilizar as informações sobre tumores para prever se um tumor é maligno ou benigno.

      O Scikit-learn vem instalado com vários datasets que podemos carregar no Python, e o dataset que queremos está incluído. Importe e carregue o dataset:

      ML Tutorial

      
      ...
      
      from sklearn.datasets import load_breast_cancer
      
      # Carregar o dataset
      data = load_breast_cancer()
      
      

      A variável data representa um objeto Python que funciona como um dicionário. As chaves importantes do dicionário a considerar são os nomes dos rótulos de classificação (target_names), os rótulos reais (target), os nomes de atributo/característica (feature_names), e os atributos (data).

      Atributos são uma parte crítica de qualquer classificador. Os atributos capturam características importantes sobre a natureza dos dados. Dado o rótulo que estamos tentando prever (tumor maligno versus benigno), os possíveis atributos úteis incluem o tamanho, raio, e a textura do tumor.

      Crie novas variáveis para cada conjunto importante de informações e atribua os dados:

      ML Tutorial

      
      ...
      
      # Organizar nossos dados
      label_names = data['target_names']
      labels = data['target']
      feature_names = data['feature_names']
      features = data['data']
      
      

      Agora temos listas para cada conjunto de informações. Para entender melhor nosso conjunto de dados, vamos dar uma olhada em nossos dados imprimindo nossos rótulos de classe, o primeiro rótulo da instância de dados, nossos nomes de características, e os valores das características para a primeira instância de dados.

      ML Tutorial

      
      ...
      
      # Olhando para os nossos dados
      print(label_names)
      print(labels[0])
      print(feature_names[0])
      print(features[0])
      
      

      Você verá os seguintes resultados se você executar o código:

      Como mostra a imagem, nossos nomes de classes são malignant and benign (maligno e benigno), que são então mapeados para valores binários de 0 e 1, onde 0 representa tumores malignos e 1 representa tumores benignos. Portanto, nossa primeira instância de dados é um tumor maligno cujo raio médio é 1.79900000e+01.

      Agora que temos nossos dados carregados, podemos trabalhar com eles para construir nosso classificador de machine learning.

      Passo 3 — Organizando Dados em Conjuntos

      Para avaliar o desempenho de um classificador, você deve sempre testar o modelo em dados não visualizados. Portanto, antes da construção de um modelo, divida seus dados em duas partes: um conjunto de treinamento e um conjunto de testes.

      Você usa o conjunto de testes para treinar e avaliar o modelo durante o estágio de desenvolvimento. Então você usa o modelo treinado para fazer previsões no conjunto de testes não visualizado. Essa abordagem lhe dá uma noção do desempenho e robustez do modelo.

      Felizmente, o sklearn tem uma função chamada train_test_split(), que divide seus dados nesses conjuntos. Importe a função e em seguida utilize-a para dividir os dados:

      ML Tutorial

      
      ...
      
      from sklearn.model_selection import train_test_split
      
      # Dividir nossos dados
      train, test, train_labels, test_labels = train_test_split(features,
                                                                labels,
                                                                test_size=0.33,
                                                                random_state=42)
      
      

      A função divide aleatoriamente os dados usando o parâmetro test_size. Neste exemplo, agora temos um conjunto de testes (test) que representa 33% do dataset original. Os dados restantes (train) formam então os dados de treinamento. Também temos os respectivos rótulos para ambas as variáveis train/test, ou seja, train_labels e test_labels.

      Agora podemos passar para o treinamento do nosso primeiro modelo.

      Passo 4 — Construindo e Avaliando o Modelo

      Existem muitos modelos para machine learning, e cada modelo tem seus pontos fortes e fracos. Neste tutorial, vamos nos concentrar em um algoritmo simples que geralmente funciona bem em tarefas de classificação binária, a saber Naive Bayes (NB).

      Primeiro, importe o módulo GaussianNB. Em seguida inicialize o modelo com a função GaussianNB(), depois treine o modelo, ajustando-o aos dados usando gnb.fit():

      ML Tutorial

      
      ...
      
      from sklearn.naive_bayes import GaussianNB
      
      # Inicializar nosso classificador
      gnb = GaussianNB()
      
      # Treinar nosso classificador
      model = gnb.fit(train, train_labels)
      
      

      Depois de treinarmos o modelo, podemos usar o modelo treinado para fazer previsões no nosso conjunto de teste, o que fazemos utilizando a função predict(). A função predict() retorna uma matriz de previsões para cada instância de dados no conjunto de testes. Podemos então, imprimir nossas previsões para ter uma ideia do que o modelo determinou.

      Utilize a função predict() com o conjunto test e imprima os resultados:

      ML Tutorial

      
      ...
      
      # Fazer previsões
      preds = gnb.predict(test)
      print(preds)
      
      

      Execute o código e você verá os seguintes resultados:

      Como você vê na saída do Jupyter Notebook, a função predict() retornou uma matriz de 0s e 1s que representa nossos valores previstos para a classe tumor (maligno vs. benigno).

      Agora que temos nossas previsões, vamos avaliar o desempenho do nosso classificador.

      Passo 5 — Avaliando a Precisão do Modelo

      Usando a matriz de rótulos de classe verdadeira, podemos avaliar a precisão dos valores previstos do nosso modelo comparando as duas matrizes (test_labels vs. preds). Utilizaremos a função accuracy_score() do sklearn para determinar a precisão do nosso classificador de machine learning.

      ML Tutorial

      
      ...
      
      from sklearn.metrics import accuracy_score
      
      # Avaliar a precisão
      print(accuracy_score(test_labels, preds))
      
      

      Você verá os seguintes resultados:

      Como você vê na saída, o classificador NB é 94.15% preciso. Isso significa que 94,15 porcento do tempo o classificador é capaz de fazer a previsão correta se o tumor é maligno ou benigno. Esses resultados sugerem que nosso conjunto de características de 30 atributos são bons indicadores da classe do tumor.

      Você construiu com sucesso seu primeiro classificador de machine learning. Vamos reorganizar o código colocando todas as declarações import no topo do Notebook ou script. A versão final do código deve ser algo assim:

      ML Tutorial

      
      from sklearn.datasets import load_breast_cancer
      from sklearn.model_selection import train_test_split
      from sklearn.naive_bayes import GaussianNB
      from sklearn.metrics import accuracy_score
      
      # Carregar o dataset
      data = load_breast_cancer()
      
      # Organizar nossos dados
      label_names = data['target_names']
      labels = data['target']
      feature_names = data['feature_names']
      features = data['data']
      
      # Olhando para os nossos dados
      print(label_names)
      print('Class label = ', labels[0])
      print(feature_names)
      print(features[0])
      
      # Dividir nossos dados
      train, test, train_labels, test_labels = train_test_split(features,
                                                                labels,
                                                                test_size=0.33,
                                                                random_state=42)
      
      # Inicializar nosso classificador
      gnb = GaussianNB()
      
      # Treinar nosso classificador
      model = gnb.fit(train, train_labels)
      
      # Fazer previsões
      preds = gnb.predict(test)
      print(preds)
      
      # Avaliar a precisão
      print(accuracy_score(test_labels, preds))
      
      

      Agora você pode continuar trabalhando com seu código para ver se consegue fazer com que seu classificador tenha um desempenho ainda melhor. Você pode experimentar com diferentes subconjuntos de características ou mesmo tentar algoritmos completamente diferentes. Confira o website do Scikit-learn para mais ideias sobre machine learning.

      Conclusão

      Neste tutorial, você aprendeu como construir um classificador de machine learning em Python. Agora você pode carregar dados, organizar dados, treinar, prever e avaliar classificadores de machine learning em Python usando o Scikit-learn. Os passos deste tutorial devem ajudá-lo a facilitar o processo de trabalhar com seus próprios dados no Python.

      Traduzido Por Fernando Pimenta



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