PyCaret is an open-source, low-code machine learning library in Python that aims to reduce the cycle time and allows you to go from preparing your data to deploying your model within seconds using your choice of notebook environment.

P yCaret是Python中的一种开放源代码，低代码的机器学习库，旨在减少周期时间，并允许您使用选择的笔记本环境从准备数据到在几秒钟内部署模型。

This article is aimed at someone who is familiar with machine learning concepts, and also knows how to implement the various Machine Learning algorithms using different libraries such as Scikit-Learn. The perfect reader is aware of the need for automation and doesn’t want to spend so much time seeking the optimal algorithm and its hyperparameters.

本文针对的对象是熟悉机器学习概念的人，并且知道如何使用不同的库(例如Scikit-Learn)来实现各种机器学习算法。 完美的读者已经意识到了自动化的必要性，并且不想花太多时间寻找最佳算法及其超参数。

As machine learning practitioners, we know that there are several steps involved in the life cycle of a complete Data Science project and these include Data Preprocessing — missing value treatment, null value treatment, changing the data types, encoding techniques for categorical features, data transformation — log, box cox transformations, feature engineering, Exploratory Data Analysis (EDA), etc. before we can actually start the model building, evaluation and prediction. So we use various libraries such as numpy, pandas, matplotlib scikit-learn, etc in python for accomplishing these tasks. So Pycaret is a very powerful library that helps us in the automation of the process.

作为机器学习的从业者，我们知道一个完整的数据科学项目的生命周期涉及几个步骤，其中包括数据预处理-缺失值处理，空值处理，更改数据类型，分类特征的编码技术，数据转换—日志，Box Cox转换，功能工程，探索性数据分析(EDA)等，然后我们才能真正开始模型的建立，评估和预测。 因此，我们在python中使用了各种库(例如numpy，pandas，matplotlib scikit-learn等)来完成这些任务。 因此，Pycaret是一个非常强大的库，可以帮助我们实现流程的自动化。

安装Pycaret (Installing Pycaret)

!pip install pycaret==2.0

Once Pycaret is installed, we are ready to go! I am going to discuss a regression problem here and Pycaret can be used for many problems such as classification, anomaly detection, clustering, Natural Language Processing.

一旦安装了Pycaret，我们就可以开始了！ 我将在这里讨论回归问题，Pycaret可以用于许多问题，例如分类，异常检测，聚类，自然语言处理。

I am going to use the Laptop Prices dataset here which I have obtained from scraping Flipkart website.

我将在这里使用笔记本电脑价格数据集 我是从抓取Flipkart网站获得的。

df = pd.read_csv('changed.csv') # Reading the datasetdf.head()

from pycaret.regression import *reg = setup(data = df, target = 'Price')

The setup() function of Pycaret does most of the correction, which is normally done with many lines of code — is done in a single line of code! That’s the beauty of this amazing library!

Pycaret的setup()函数进行了大部分校正，这通常是用多行代码完成的—只需一行代码即可完成！ 这就是这个令人惊叹的图书馆的美！

We use the setup variable, and in the target, we mention the feature name (dependent variable)-here we want to predict the Price of the laptop so that becomes the dependent variable.

我们使用设置变量，在目标中，我们提到功能名称(因变量)，此处我们要预测笔记本电脑的价格，以使其成为因变量。

X = df.drop('Price',axis=1) Y = df['Price'] Y = pd.DataFrame(Y)

Comparing all the regression models

比较所有回归模型

compare_models()

Training all the regression models. So after this, we can create any model-either CatBoost or else XGBoost regressor model, and then we can perform hyperparameter tuning.

训练所有回归模型。 因此，在此之后，我们可以创建任何模型-CatBoost或XGBoost回归模型，然后执行超参数调整。

We can see that our Gradient Boosting Regressor (GBR) model has performed relatively better when compared to all the other models. But I have performed the analysis using the XGBoost model as well, and this model performed better than the GBR Model.

我们可以看到，与所有其他模型相比，我们的Gradient Boosting Regressor(GBR)模型的性能相对较好。 但是我也使用XGBoost模型进行了分析，并且该模型的性能优于GBR模型。

As we have identified the best model to be XGBoost so we create xgboost model with the help of create_model function and mention the max_depth(number of iteration for which the model will run)

由于我们已确定最佳模型为XGBoost，因此我们在create_model函数的帮助下创建了xgboost模型，并提到了max_depth(该模型将针对其运行的迭代次数)

Creating the model

建立模型

xgboost = create_model('xgboost', max_depth = 10)

So after creating the model with a depth of 10, it runs 10 iterations and calculates the MAE(Mean Absolute Error), MSE (Mean Squared Error), RMSE (Root Mean Squared Error), R2(R2_score-R squared value), MAPE (Mean Absolute Percentage Error) in every iteration. Finally, it displays the mean and standard deviation of all the errors in these 10 iterations. Lesser the error better is the machine learning model! So in order to reduce the error, we try to find out the hyperparameters which can minimize the error.

因此，在创建深度为10的模型之后，它将运行10次迭代并计算MAE(均值绝对误差)，MSE(均方误差)，RMSE(均方根误差)，R2(R2_score-R平方值)，MAPE (平均绝对百分比误差)。 最后，它显示了这10次迭代中所有误差的平均值和标准偏差。 误差越小，机器学习模型就越好！ 因此，为了减少错误，我们尝试找出可以使错误最小化的超参数。

For this purpose, we apply the tune_model function and apply K-fold cross-validation to find out the best hyperparameters.

为此，我们应用tune_model函数并应用K折交叉验证以找出最佳的超参数。

Hyper tuning of the model

超调模型

xgboost = tune_model(xgboost, fold=5)

The model runs 5 iterations and gives us the mean and standard deviation of all the errors. The mean value of MAE after 5 iterations was almost the same for both GBR and XGBoost models, but after hyper tuning and making the predictions, the XGBoost model had less error and performed better than the GBR model.

该模型运行5次迭代，并为我们提供所有误差的均值和标准差。 对于GBR和XGBoost模型，经过5次迭代后，MAE的平均值几乎相同，但是经过超调和做出预测之后，XGBoost模型的误差较小，并且性能优于GBR模型。

Making predictions using the best model

使用最佳模型进行预测

predict_model(xgboost)

Checking the scores after applying Cross Validation (we mainly need the Mean Absolute Error). Here we can see that the MAE for the best model has come down to 10847.2257 so the Mean Absolute Error is approximately 10,000.

应用交叉验证后检查分数(我们主要需要平均绝对误差)。 在这里，我们可以看到最佳模型的MAE已降至10847.2257，因此平均绝对误差约为10,000。

Checking all the parameters of the xgboost model

检查xgboost模型的所有参数

print(xgboost)

XGBoost model hyperparamaters

XGBoost模型超参数

plot_model(xgboost, plot='parameter')

Residuals Plot

残差图

The distances (errors) between the actual and predicted values

实际值与预测值之间的距离(误差)

plot_model(xgboost, plot='residuals')

We can clearly see that my model is overfitting as the R squared for training set is 0.999 and test set is 0.843. This is actually not surprising because my dataset contains a total of only 168 rows! But the main point here is to highlight the excellent features of Pycaret as you can create plots and curves with just one line of code!

我们可以清楚地看到，我的模型过度拟合，因为训练集的R平方为0.999，而测试集的R平方为0.843。 这实际上不足为奇，因为我的数据集总共仅包含168行！ 但是这里的重点是要突出Pycaret的出色功能，因为您只需一行代码就可以创建绘图和曲线！

Plotting the Prediction Error

绘制预测误差

plot_model(xgboost, plot='error')

The value of R squared for the model is 0.843.

该模型的R平方的值为0.843。

Cooks Distance Plot

厨师距离图

plot_model(xgboost, plot='cooks')

Learning Curve

学习曲线

plot_model(xgboost, plot='learning')

Validation Curve

验证曲线

plot_model(xgboost, plot='vc')

These 2 plots also show us that the model is clearly overfitting!

这两个图也向我们显示该模型显然过拟合！

Plot of Feature Importance

特征重要性图

plot_model(xgboost, plot='feature')

By this plot, we can see that Processor_Type_i9 (i9 CPU) is a very important feature for determining the price of the laptop.

通过此图，我们可以看到Processor_Type_i9(i9 CPU)是确定笔记本电脑价格的非常重要的功能。

Splitting the dataset into training and testing set

将数据集分为训练和测试集

from sklearn.model_selection import train_test_splitX_train,X_test,Y_train,Y_test = train_test_split(X,Y,test_size=0.2)

Final XGBoost parameters for deployment

最终的XGBoost部署参数

final_xgboost = finalize_model(xgboost)

Making the prediction on the unseen data ( Test set data)

对看不见的数据(测试集数据)进行预测

new_predictions = predict_model(xgboost, data=X_test)new_predictions.head()

Saving the transformation pipeline and model

保存转换流程和模型

save_model(xgboost, model_name = 'deployment_08082020')Transformation Pipeline and Model Succesfully Saveddeployment_08082020 = load_model('deployment_08082020')Transformation Pipeline and Model Sucessfully Loadeddeployment_08082020

So this is the final Machine Learning model that can be used for deployment.

因此，这是可用于部署的最终机器学习模型。

The model is saved in the pickle format!

模型以pickle格式保存！

For more info, check the documentation here

有关更多信息，请查看文档 这里

In this article, I have not discussed everything in detail. But you can always refer to my GitHub Repository for the whole code. My conclusion from this article is that don’t expect a perfect model, but expect something you can use in your own company/project today!

在本文中，我没有详细讨论所有内容。 但是您始终可以参考我的GitHub存储库以获取整个代码。 我从本文得出的结论是，不要期望一个完美的模型，而是希望您今天可以在自己的公司/项目中使用某些东西！

Shout out to Moez Ali for this absolutely brilliant library!

为这个绝对出色的图书馆大喊Moez Ali ！

Connect with me on LinkedIn here

在此处通过LinkedIn与我联系

The bottom line is that the automation lowers the risk of human error and adds some intelligence to the enterprise system. — Stephen Elliot

最重要的是，自动化降低了人为错误的风险，并为企业系统增加了一些智能。 —斯蒂芬·艾略特(Stephen Elliot)

I hope you found the article insightful. I would love to hear feedback to improvise it and come back with better content.

我希望您发现这篇文章很有见地。 我很想听听反馈以即兴创作，并以更好的内容回来。

Thank you so much for reading!

非常感谢您的阅读！