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Introduction

Data Scientists have an important role in the modern machine-learning world. Leveraging ML pipelines can save them time, money, and effort and ensure that their models make accurate predictions and insights. This blog will look at the value ML pipelines bring to data science projects and discuss why they should be adopted.

Data scientists are always looking for ways to maximize their efficiency and the quality of their results. Machine learning pipelines offer an effective and automated solution to this problem. This blog will discuss the various stages of a machine learning pipeline and explain why data scientists should adopt this approach to optimize their workflow. So, In this article, we will see how Machine Learning Pipelines can help you in Data Science Projects.

Machine learning pipelines are a structured and efficient way of developing, deploying, and maintaining machine learning models. By automating the various stages of the machine learning process, including data preprocessing, feature selection, model training and evaluation, hyperparameter tuning, and model deployment and monitoring, pipelines help data scientists avoid common pitfalls and ensure high-quality results.

Learning Objectives

Understand the benefits and importance of using machine learning pipelines in data science.

It highlights how pipelines can streamline the data preprocessing, feature selection, model training, evaluation, and deployment steps, leading to more efficient and accurate results.

Ensure consistency and reproducibility of results.

Speed up the time-to-market of machine learning models.

Improve the accuracy and performance of models.

Enable effective model versioning and management.

Facilitate deployment and monitoring of models in production environments.

The article also covers best practices for implementing machine learning pipelines and the benefits that can be achieved through their use.

This article was published as a part of the Data Science Blogathon.

Table of Contents

Introduction

Overview of Machine Learning Pipelines

Advantages of Machine Learning Pipelines

Feature selection and Engineering

Model Training and Evaluation

Hyperparameter Tuning

Model Deployment and Monitoring

Best practices for Machine Learning Pipelines

Current Industry use-cases

Conclusion

Overview of Machine Learning Pipelines

Machine learning (ML) pipelines are a crucial aspect of the data science process. They allow data scientists to streamline their work and automate many tedious and time-consuming tasks in building and deploying ML models. A well-designed ML pipeline can make the model development process more efficient and reproducible while reducing the risk of errors and promoting best practices. By breaking down the ML process into manageable steps, data scientists can focus on individual tasks, such as feature engineering and model selection, while relying on the pipeline to manage the overall process and keep everything organized. ML pipelines also provide a clear and auditable record of all the steps taken in the model-building process, making it easier to understand and explain the results. In short, ML pipelines are an essential tool for data scientists who want to build high-quality ML models quickly and effectively.

Advantages of Machine Learning Pipelines

Consider a scenario where a company wants to build a machine-learning model to predict customer churn. This involves several steps, including data preprocessing, feature selection, model training, evaluation, and deployment.

Without a machine learning pipeline, these steps would typically be performed manually, leading to various problems such as:

Inefficient Manual Processes: Data preprocessing, feature selection, and model training require significant time and effort. Without a machine learning pipeline, these processes are performed manually, leading to increased time and effort and a higher risk of errors.

Inconsistent Results: The manual process of data preprocessing, feature selection, and model training can lead to different results each time, making it difficult to compare models and ensure consistent results.

Lack of Transparency: The manual process of data preprocessing, feature selection, and model training can make it difficult to understand the reasoning behind the model decisions and identify potential biases or errors.

With a machine learning pipeline, these problems can be avoided. The pipeline can automate the data preprocessing, feature selection, model training, evaluation, and deployment steps, leading to the following benefits:

Improved Efficiency and Productivity: Data preprocessing, feature selection, and model training require significant time and effort. Without a machine learning pipeline, these processes are performed manually, leading to increased time and effort and a higher risk of errors.

Better Accuracy: ML pipelines help to ensure consistency and reproducibility of results, reducing the risk of human error and allowing for better quality control. A well-defined pipeline can help to ensure that data is preprocessed consistently and that models are trained and evaluated consistently. This can lead to more reliable results and reduced risk of errors or bias in the machine learning process.

Improved Collaboration: ML pipelines provide a clear and standardized process for developing machine learning models, making it easier for data scientists to collaborate and share their work. A well-defined pipeline can reduce the time and effort required to onboard new team members and provide a common understanding of the data, models, and results. This can lead to better communication, reduced confusion, and increased team productivity.

Faster Iteration:  ML pipelines can help to speed up the development and experimentation process by automating many of the steps involved in model development. This can reduce the time required to test different models, features, and parameters, leading to faster iterations and improved results.

Increased Transparency: ML pipelines can help to track the progress of machine learning projects, allowing data scientists to keep track of different versions of models, features, and parameters. This can improve the transparency and accountability of machine learning projects and help to identify and resolve issues more quickly.

Better Management of Data and Models: ML pipelines can help manage the data and models used in machine learning projects, ensuring that data is stored securely and organized and that models are versioned and tracked. This can help ensure that machine learning project results are reliable, repeatable, and can be audited.

Easy Deployment and Scaling: ML pipelines can help to automate the deployment process, making it easier to move machine learning models from development to production. This can reduce the time required to deploy models and make it easier to scale machine-learning solutions as needed. Additionally, ML pipelines can help to manage the resources required for model deployment, ensuring that resources are used efficiently and cost-effectively.

Better Alignment with Business Requirements: The pipeline can incorporate domain knowledge and business requirements, making it easier to align the models with the problem requirements and ensure better business outcomes.

Scalability and Flexibility: The pipeline can be built on cloud computing platforms such as Google Cloud Platform (GCP), providing the necessary resources for large-scale data processing and model training.

Reusability and Consistency: The pipeline can be reused across different projects and teams, ensuring consistent and reproducible results.

Feature Selection and Engineering

Feature selection and engineering are crucial steps in building a successful machine-learning model. Feature selection is selecting the most relevant features or variables from a large data pool to build the model. The goal is to reduce the dimensionality of the data, prevent overfitting, and improve the model’s accuracy and interpretability.

For example, consider a dataset of customer information that includes features such as age, income, location, and purchasing history. In this case, feature selection would involve selecting the most relevant variables to build the model. A data scientist might use only the age, income, and purchasing history variables, as they are believed to have the most impact on the target variable (e.g., likelihood of customer churn).

On the other hand, feature engineering involves creating or transforming new features to improve the model’s performance. For example, encoding categorical variables, normalizing numeric variables, or creating interaction terms between features. In the customer information example, a data scientist might create a new feature that represents the average purchase amount, as this feature may strongly impact the target variable.

By automating the feature selection and engineering process, machine learning pipelines can save time for data scientists, reduce the risk of human error, and make it easier to reproduce results. Additionally, pipelines can be designed to optimize the feature selection and engineering process using techniques like feature importance, feature correlation, or feature significance tests.

Model Training and Evaluation

Model training and evaluation is a crucial steps in the machine-learning pipeline. This step involves creating a machine-learning model using a set of algorithms and then evaluating the model’s performance using various performance metrics. (Testers guide for Testing Machine Learning Models)

For example, a data scientist might train a decision tree model on a dataset to predict customer churn. The model would then be evaluated using accuracy, precision, recall, and F1 score metrics. Based on the evaluation results, the data scientist might fine-tune the model by adjusting the parameters, trying a different algorithm, or even starting the process with a different set of features.

By automating the model training and evaluation step, a machine learning pipeline can save data scientists time and ensure that the best-performing model is selected and deployed in production. The pipeline can also help data scientists to make better decisions about model selection by providing a clear and objective evaluation of the models.

Hyperparameter Tuning

Hyperparameter tuning selects a machine-learning model’s best set of hyperparameters to improve its performance. Hyperparameters are the parameters set before training the model and are used to control the model’s behavior and generalization. For example, the learning rate of a deep learning model, the number of trees in a random forest, or the regularization parameter in a linear regression model are all hyperparameters.

During the model training and evaluation step, you can perform hyperparameter tuning to find the best hyperparameters for your model. There are different techniques for hyperparameter tuning, including grid search, random search, and Bayesian optimization. The objective is to find the best hyperparameters on a validation set.

For example, you train a deep-learning model to classify images into different categories. You can set the learning rate and the number of neurons in the hidden layers as hyperparameters and perform a grid or random search to find the best combination of these hyperparameters that result in the best accuracy on the validation set.

Model Deployment and Monitoring

Model deployment and monitoring refer to putting a trained machine learning model into production and tracking its performance over time.

For example, after training a model to predict customer churn, the deployment process would involve integrating the model into a live production environment, such as a web application or a mobile app. This would allow the model to make real-time predictions based on new data inputs.

The monitoring process involves tracking the performance of the deployed model to ensure that it continues to produce accurate predictions over time. This can be done by regularly comparing the model’s predictions to actual outcomes and using tools to detect changes in the data distribution over time. If performance degradation is detected, the model may need to be retrained or its hyperparameters adjusted.

Data scientists can ensure that their machine learning models positively impact the business and continuously deliver value by having a well-defined model deployment and monitoring process.

Best practices for Machine Learning Pipelines

There are several best practices that data scientists can follow when building and using machine learning pipelines, including:

Automate as much as possible: Automating the different stages of the pipeline can help ensure that the process is consistent and reduces the risk of manual errors.

Use version control: Keeping track of pipeline changes and their components can be challenging. By using version control, you can easily keep track of changes, revert to previous versions if necessary, and share your work with others.

Validate inputs and outputs: Ensure that the inputs and outputs of each stage of the pipeline are valid. This can help prevent issues later on and increase the reliability of the pipeline.

Monitor pipeline performance: Monitor the performance of the pipeline to identify and address any bottlenecks or issues that arise.

Evaluate multiple models: Don’t limit yourself to a single model. Try out different models and compare their performance.

Document the pipeline: Documenting the pipeline and its components can help others understand it and be useful when making changes to the pipeline later.

Continuously improve the pipeline: Refine the pipeline over time by incorporating feedback and making improvements based on experience and performance metrics.

Current Industry Use Cases

There are several current industry applications where the use of machine learning pipelines is critical:

Healthcare: Machine learning pipelines build predictive models to diagnose diseases, predict patient outcomes, and optimize treatment plans.

Finance: Pipelines are used to build models to detect fraud, predict stock prices, and automate loan underwriting processes.

Retail: Machine learning pipelines build models to recommend products, personalize promotions, and optimize supply chain management.

Manufacturing: Pipelines are used to build models to optimize production processes, predict equipment failures, and improve quality control.

Energy: Machine learning pipelines are used to build models to predict energy consumption, optimize renewable energy production, and forecast energy prices.

Conclusion

Adopting machine learning pipelines can greatly benefit data scientists by improving the machine learning process’s efficiency, repeatability, and transparency. By automating and streamlining various tasks such as data preprocessing, feature selection, model training and evaluation, hyperparameter tuning, and model deployment and monitoring, data scientists can avoid common pitfalls and increase the accuracy of their models. Implementing best practices in creating and maintaining machine learning pipelines can further enhance the benefits of this approach.

The key takeaways from this article are:

Machine learning pipelines help automate building a machine learning model, from data preprocessing to deployment.

Pipelines help avoid manual errors and inconsistencies in the model-building process.

The pipeline allows for standardized and repeatable workflows, leading to improved collaboration and knowledge sharing within an organization.

Pipelines can speed up the model-building process, allowing data scientists to focus on more strategic tasks such as feature selection and model design.

Using pipelines can result in better model performance as it facilitates hyperparameter tuning and enables easy comparison.

Pipelines help ensure the reproducibility of results, making it easier to track and replicate experiments.

Finally, pipelines can help organizations scale their machine-learning initiatives, making monitoring and managing models in production easier.

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion. 

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You're reading Why Data Scientists Should Adopt Machine Learning Pipelines?

What Capabilities A Cloud Machine Learning Platform Should Have?

How to pick a cloud machine learning platform?

To create an effective 

Know your data well

If you have the extensive amount of data to create precise models, you may not want to ship it halfway across the world. Distance isn’t a problem here; however, it’s about time. Data transmission speed is bounded by the speed of light, even on a perfect network with infinite bandwidth. Long-distance indicates latency. The ideal case for large datasets is to create a model where the data already exists so that mass data transmission can be avoided. Several databases support it to a limited extent.  

Support an ETL or ELT pipeline

ETL (export, transform, and load) and ELT (export, load, and transform) are two common data pipeline configurations in the database world. Machine learning and deep learning increases the need for these, especially the transform part. ELT provides more flexibility when your transformations need to alter as the load phase is usually the most time-consuming for big data.  

Support an online environment for model building

The conventional wisdom is that you should import your data to your desktop for model building. The unlimited data needed to build good machine learning, and deep learning models changes the picture. Although you can download a small sample of data to your desktop for exploratory data analysis and model building, you need to have access to the entire data for production models.  

Support scale-up and scale-out training

Except for training models, the compute and memory requirements of notebooks are usually minimal. It helps a notebook to spawn training jobs that run on multiple large virtual machine or containers. It also aids the training to access accelerators such as GPUs, TPUs, and FPGAs. These can reduce days of training to hours.  

Support AutoML and automatic feature engineering

You might not be good at picking machine learning models selecting features and engineering new features from the raw observations. These are time-consuming and can be automated to a large extent. 

Offer tuned AI services

The giant cloud platforms offer robust and tuned AI services or solutions for many applications, not just image detection. These include language translation, speech to text, text to speech, forecasting, and recommendations. These services have already been trained and examined on more datasets than is generally available to businesses. These are also installed on service endpoints with sufficient computational resources, including accelerators to confirm good response times under worldwide load.  

Control Costs

Last, you require ways to control the costs incurred by the models. Deploying production models frequently accounts for 90% of the value of deep learning. On the other hand, the training accounts for only 10% of the cost.

To create an effective machine learning and deep learning model, you need more data, a way to clean the data and perform feature engineering on it. It is also a way to train models on your data in a reasonable amount of time. After that, you need a way to install your models, surveil them for drift over time, and retrain them as required. If you have invested in compute resources and accelerators such as GPUs, you can do all of that on-premises. However, you may find that if your resources are adequate, they are also inactive much of the time. On the other side, it can sometimes be more cost-effective to run the entire pipeline in the cloud, applying large amounts of compute resources and accelerators and then releasing them. The cloud providers have put significant effort into building out their machine learning platforms to support the entire machine learning lifecycle, from planning a project to maintaining a model during production. What are the capabilities every end-to-end machine learning platform should provide?If you have the extensive amount of data to create precise models, you may not want to ship it halfway across the world. Distance isn’t a problem here; however, it’s about time. Data transmission speed is bounded by the speed of light, even on a perfect network with infinite bandwidth. Long-distance indicates latency. The ideal case for large datasets is to create a model where the data already exists so that mass data transmission can be avoided. Several databases support it to a limited chúng tôi (export, transform, and load) and ELT (export, load, and transform) are two common data pipeline configurations in the database world. Machine learning and deep learning increases the need for these, especially the transform part. ELT provides more flexibility when your transformations need to alter as the load phase is usually the most time-consuming for big chúng tôi conventional wisdom is that you should import your data to your desktop for model building. The unlimited data needed to build good machine learning, and deep learning models changes the picture. Although you can download a small sample of data to your desktop for exploratory data analysis and model building, you need to have access to the entire data for production models.Except for training models, the compute and memory requirements of notebooks are usually minimal. It helps a notebook to spawn training jobs that run on multiple large virtual machine or containers. It also aids the training to access accelerators such as GPUs, TPUs, and FPGAs. These can reduce days of training to chúng tôi might not be good at picking machine learning models selecting features and engineering new features from the raw observations. These are time-consuming and can be automated to a large extent. AutoML systems frequently try out many models to see which result in the best objective function values to minimize squared error for regression problems. An ideal AutoML system can also perform feature engineering, and use their resources effectively to pursue the best possible models with the best possible sets of chúng tôi giant cloud platforms offer robust and tuned AI services or solutions for many applications, not just image detection. These include language translation, speech to text, text to speech, forecasting, and recommendations. These services have already been trained and examined on more datasets than is generally available to businesses. These are also installed on service endpoints with sufficient computational resources, including accelerators to confirm good response times under worldwide load.Last, you require ways to control the costs incurred by the models. Deploying production models frequently accounts for 90% of the value of deep learning. On the other hand, the training accounts for only 10% of the cost. The best way to control prediction costs lies on your load and the complexity of your model. If the load is high, you might be able to use an accelerator to avoid adding more virtual machine instances. If the load is variable, you might be able to dynamically change the size or number of instances or containers as the load varies up and down. In case of a low or occasional load, you can use a tiny instance with a partial accelerator to handle the predictions.

Big Data Protection In The Age Of Machine Learning

The concept of machine learning has been around for decades, primarily in academia. Along the way it has taken various forms and adopted various terminologies, including pattern recognition, artificial intelligence, knowledge management, computational statistics, etc.

Regardless of terminology, machine learning enables computers to learn on their own without being explicitly programmed for specific tasks. Through the use of algorithms, computers are able to read sample input data, build models and make predictions and decisions based on new data. This concept is particularly powerful when the set of input data is highly variable and static programming instructions cannot handle such scenarios.

In recent years, the proliferation of digital information through social media, the Internet of Things (IoT) and e-commerce, combined with accessibility to economical compute power, has enabled machine learning to move into the mainstream. Machine learning is now commonly used across various industries including finance, retail, healthcare and automotive. Inefficient tasks once performed using human input or static programs have now been replaced by machine learning algorithms.

Here are a few examples:

Prior to the use of machine learning, fraud detection involved following a set of complex rules as well as following a checklist of risk factors to detect potential security threats. But with the growth in the volume of transactions and the number of security threats, this method of fraud detection did not scale. The finance industry is now using machine learning to identify unusual activity and anomalies and reporting those to the security teams. PayPal is also using machine learning to compare millions of transactions to identify fraudulent and money laundering activity.

Without machine learning, recommendations on product purchases and which movies to watch were mainly by word of mouth. Companies like Amazon and Netflix changed that by adopting machine learning to make recommendation to their customers based on data they had collected from other similar users. Using machine learning to recommend movies and products is now fairly common. Intelligent machine learning algorithms analyze your profile and activity against the millions of other users they have in their database and recommend products that you are likely to buy or movies that you may be interested in watching.

For all its increased popularity and use, machine learning still hasn’t yet made its way into any part of data protection, and that is being acutely felt in big data. Specifically, backup and recovery for NoSQL databases (Cassandra, Couchbase, etc.), Hadoop, and emerging data warehouse technologies (HPE Vertica, Impala, Tez, etc.) is a very manual process with a lot of human interaction and input. It is quite a paradox that these big data platforms are used for machine learning while the underlying data protection processes supporting these platforms rely on human intervention and input.

For example, an organization may have a defined recovery point objective (RPO) and recovery time objective (RTO) for a big data application. Based on those objectives, an IT or DevOps engineer determines the schedule and frequency for backing up application data. If the RPO is 24 hours, the engineer may decide to perform backups once per day starting at 11:00 p.m.

While this logically makes sense, the answer is not as simple as that, especially in a big data environment. the big data environments are often very dynamic and unpredictable. These systems may be unusually busy at 11:00 p.m., loading new data or running nightly reports and making that time least optimal for scheduling a backup.

Why can’t the data protection application recommend the best time to schedule a backup task to meet the recovery point objective?

Another common example of inefficiency in data protection relates to storing backup data. Typically, techniques such as compression and de-duplication are applied to backup data to reduce the backup storage footprint. The algorithms used for these techniques are static and follow the same mechanism independent of the type of data being dealt with. Given that big data platforms use many different compressed and uncompressed file formats (Record Columnar (RC), Optimized Row Columnar (ORC), Parquet, Avro, etc.), a static algorithm for deduplication and compression does not yield the best results.

Why can’t the data management application learn and adopt the best deduplication and compression techniques for each of the file formats?

Machine learning certainly could aid in optimizing a company’s data protection processes for big data. All pertinent data needs to be collected and analyzed dynamically using machine learning algorithms. Only then will we be able to do efficient, machine-driven data protection for big data. The question is not if but when!

By Jay Desai, VP, product management, Talena, Inc.

Photo courtesy of Shutterstock.

Julia On The Upswing: Why Data Scientists Are Choosing Julia

In the ever-developing field of data science, the onus is on data scientists, to keep track of developments in algorithms, technology stacks, databases, and languages. One such development is a programming language called Julia, which has received a fair bit of attention in the past few years because of its high speed and ease of use.

What is Julia? 

Julia, a newcomer to the programming languages for data science, is a high-level, general-purpose programming language, that was developed specifically for scientific computing. The developers of Julia, Jeff Bezanson, Alan Edelman, Stefan Karpinski, and Viral B. Shah, while coming from different backgrounds, were interested in the collective power of all programming languages. They wanted Julia to have the best of all the languages.

In short, Julia would be open-source with a liberal license, as fast as C, as general-purpose as Python, as statistics-friendly as R, easy to learn, and a compiled language. With that vision in mind, Julia’s first version went live in 2012.  

Julia’s claim to fame There are many reasons why Julia is preferable in the Computation and Machine learning (ML) world: 

Free and Open Source: The license is held by MIT and the code is hosted on Git where everyone can view and make changes to it.

Parallelism: Julia was designed for parallel processing and provides primitives for parallel computing unlike Python and any other programming languages.

High execution speed: Julia matches the speed of C and FORTRAN, which are among the fastest languages. 

Compatible with Jupyter: It is compatible with Jupyter and many other IDEs such as VS Code and Vim.

Tailored for ML: It does not require external packages (such as NumPy for Python) for ML calculations. ‘Vanilla’ Julia supports matrices and equations.

Julia for Data Science  Julia compared to Python and R

Julia was built to provide the best of what pre-existing languages offered. Python and R are the most widely used languages for ML, statistical analytics, and data visualization. Together, they have been ruling the data world, casting a shadow on other similar languages. But Julia has distinguished itself from the pack and has slowly been moving towards the light. It’s important to understand how Julia compares to the language giants: 

Benchmark time normalized against the C implementation 

Speed and Performance:

Using C as the benchmark for the fastest language, Python is slower than C and, R is slower than Python. Julia’s execution time, however, is comparable to that of C’s. This is because Julia is a compiled language whereas R and Python are interpreted. 

Sources/Libraries:

 A vast number of libraries and APIs are available for Python, whereas a lesser number is available for R. Being one of the new languages, there are limited libraries and APIs available for Julia. 

Community Support:

Python has a very large developer community and community support, whereas R has a comparatively smaller developer community. Julia, being in the initial stages, has a much smaller but growing developer community.

Machine Learning Support in Julia Common libraries

• GmmFlow.jl

•  Clustering.jl

•  chúng tôi (Hierarchical clustering)

•  chúng tôi (PCA)

Julia has vast support for a range of problems in Machine Learning such as supervised learning, classification, regression, unsupervised learning, cluster analysis, dimensionality reduction.

It also has support for Deep Learning algorithms – ConvNet, TextRNN and many more.

Pros and Cons of Julia Pros:

1.Julia’s speed and ease of implementation certainly makes it a desirable programming language for data science.

2.It has an intuitive syntax just like Python.

3.It has multiple wrapper libraries on top of Python libraries and a functionality to call Python functions.

4.It has support for Machine Learning algorithms.

Cons:

1.While its community support is not great, it is developing steadily.

2.Some wrapper libraries such as Pandas have slow execution in local Jupyter.

3.It has high initial compile time for imported libraries, and sometimes requires multiple libraries to perform a single task. For e.g., reading a csv as dataframe requires 2 libraries: DataFrames and CSV.

4.Some deep learning functions don’t have the same flexibility in parameter tuning as that of Python counterparts.

Julia on the rise

Julia was developed specifically for scientific computing. Since it went live, it has seen a wide range of applications across multiple industries. NASA has been using it to model animal, plant, and human migration patterns and their responses to climate change. BlackRock, one of the largest asset management companies, has been using Julia for time series data analytics and big-data applications. Even MIT has used Julia to program robots to climb stairs and walk on hazardous, difficult, and uneven terrain. 

The rise of data and data science has been exponential thereby increasing the importance of faster and simpler programming languages. Julia has a few more miles to go in developing its data science ecosystem i.e., documentation, community support, libraries, and packages but does great in terms of speed. Julia can potentially reduce time-to-market in places where code execution time is the major roadblock. It can also be experimented in places where simple ML algorithms are used, or complex ­­computations are performed as the community support is good for basic algorithms. Julia is evolving steadily and is a language to watch out for data science.

References Resources Author:

Vedang Dalal, Lead Analyst, Merkle

Catboost: A Machine Learning Library To Handle Categorical (Cat) Data Automatically

Introduction

I bet most of us! At least in the initial days.

This error occurs when dealing with categorical (string) variables. In sklearn, you are required to convert these categories in the numerical format.

In order to do this conversion, we use several pre-processing methods like “label encoding”, “one hot encoding” and others.

In this article, I will discuss a recently open sourced library ” CatBoost” developed and contributed by Yandex. CatBoost can use categorical features directly and is scalable in nature.

“This is the first Russian machine learning technology that’s an open source,” said Mikhail Bilenko, Yandex’s head of machine intelligence and research.

P.S. You can also read this article written by me before “How to deal with categorical variables?“.

Table of Contents

What is CatBoost?

Advantages of CatBoost library

CatBoost in comparison to other boosting algorithms

Installing CatBoost

Solving ML challenge using CatBoost

End Notes

1. What is CatBoost?

CatBoost is a recently open-sourced machine learning algorithm from Yandex. It can easily integrate with deep learning frameworks like Google’s TensorFlow and Apple’s Core ML. It can work with diverse data types to help solve a wide range of problems that businesses face today. To top it up, it provides best-in-class accuracy.

It is especially powerful in two ways:

It yields state-of-the-art results without extensive data training typically required by other machine learning methods, and

Provides powerful out-of-the-box support for the more descriptive data formats that accompany many business problems.

“CatBoost” name comes from two words “Category” and “Boosting”.

As discussed, the library works well with multiple Categories of data, such as audio, text, image including historical data.

“Boost” comes from gradient boosting machine learning algorithm as this library is based on gradient boosting library. Gradient boosting is a powerful machine learning algorithm that is widely applied to multiple types of business challenges like fraud detection, recommendation items, forecasting and it performs well also. It can also return very good result with relatively less data, unlike DL models that need to learn from a massive amount of data.

Here is a video message of Mikhail Bilenko, Yandex’s head of machine intelligence and research and Anna Veronika Dorogush, Head of Tandex machine learning systems.

2. Advantages of CatBoost Library

Performance: CatBoost provides state of the art results and it is competitive with any leading machine learning algorithm on the performance front.

Handling Categorical features automatically: We can use CatBoost without any explicit pre-processing to convert categories into numbers. CatBoost converts categorical values into numbers using various statistics on combinations of categorical features and combinations of categorical and numerical features. You can read more about it here.

Easy-to-use: You can use CatBoost from the command line, using an user-friendly API for both Python and R.

3. CatBoost – Comparison to other boosting libraries

We have multiple boosting libraries like XGBoost, H2O and LightGBM and all of these perform well on variety of problems. CatBoost developer have compared the performance with competitors on standard ML datasets:

The comparison above shows the log-loss value for test data and it is lowest in the case of CatBoost in most cases. It clearly signifies that CatBoost mostly performs better for both tuned and default models.

In addition to this, CatBoost does not require conversion of data set to any specific format like XGBoost and LightGBM.

4. Installing CatBoost

CatBoost is easy to install for both Python and R. You need to have 64 bit version of python and R.

Below is installation steps for Python and R:

4.1 Python Installation:

pip install catboost

4.2 R Installation

install.packages('devtools') devtools::install_github('catboost/catboost', subdir = 'catboost/R-package') 5. Solving ML challenge using CatBoost

The CatBoost library can be used to solve both classification and regression challenge. For classification, you can use “CatBoostClassifier” and for regression, “CatBoostRegressor“.

Here’s a live coding window for you to play around the CatBoost code and see the results in real-time:



In this article, I’m solving “Big Mart Sales” practice problem using CatBoost. It is a regression challenge so we will use CatBoostRegressor, first I will read basic steps (I’ll not perform feature engineering just build a basic model).

import pandas as pd import numpy as np from catboost import CatBoostRegressor #Read trainig and testing files train = pd.read_csv("train.csv") test = pd.read_csv("test.csv") #Identify the datatype of variables train.dtypes #Finding the missing values train.isnull().sum() #Imputing missing values for both train and test train.fillna(-999, inplace=True) test.fillna(-999,inplace=True) #Creating a training set for modeling and validation set to check model performance X = train.drop(['Item_Outlet_Sales'], axis=1) y = train.Item_Outlet_Sales from sklearn.model_selection import train_test_split X_train, X_validation, y_train, y_validation = train_test_split(X, y, train_size=0.7, random_state=1234) #Look at the data type of variables X.dtypes

Now, you’ll see that we will only identify categorical variables. We will not perform any preprocessing steps for categorical variables:

categorical_features_indices = np.where(X.dtypes != np.float)[0] #importing library and building model from catboost import CatBoostRegressor model=CatBoostRegressor(iterations=50, depth=3, learning_rate=0.1, loss_function='RMSE') model.fit(X_train, y_train,cat_features=categorical_features_indices,eval_set=(X_validation, y_validation),plot=True)

As you can see that a basic model is giving a fair solution and training & testing error are in sync. You can tune model parameters, features to improve the solution.

Now, the next task is to predict the outcome for test data set.

submission = pd.DataFrame() submission['Item_Identifier'] = test['Item_Identifier'] submission['Outlet_Identifier'] = test['Outlet_Identifier'] submission['Item_Outlet_Sales'] = model.predict(test) submission.to_csv("Submission.csv")

That’s it! We have built first model with CatBoost

6. End Notes

In this article, we saw a recently open sourced boosting library “CatBoost” by Yandex which can provide state of the art solution for the variety of business problems.

One of the key features which excites me about this library is handling categorical values automatically using various statistical methods.

We have covered basic details about this library and solved a regression challenge in this article. I’ll also recommend you to use this library to solve a business solution and check performance against another state of art models.

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Data Mining Vs. Machine Learning: What Are The Top 9 Differences?

blog / Artificial Intelligence and Machine Learning 9 Ways to Distinguish Between Data Mining and Machine Learning

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The overlapping methods and applications involved in data mining and Machine Learning (ML) often make the terms, wrongly, interchangeably used. These are widely different concepts, despite functional similarities such as working with large datasets. While data mining is concerned only with pattern identification, ML further utilizes pattern recognition to develop a system of future prediction without human intervention. This blog discusses in depth the essentials of data mining vs. machine learning features and why these are distinct data science branches.

What is Data Mining?

Data mining refers to the different processes involved in uncovering hidden patterns, anomalies, and trends in large datasets, and yielding favorable decision-making processes. 

Key Features of Data Mining

The core concept of data mining can be divided into four stages:

Data gathering:

Identifying relevant data for a specific analytics operation

Data preparation:

Data cleaning procedures and data exploration, among other actions, to make datasets consistent

Data mining:

Application of appropriate data mining techniques

Data interpretation:

Preparation of analytical models to drive a variety of decision-making processes

ALSO READ: 4 Types of Machine Learning and How to Build a Great Career in Each

Benefits of Data Mining

Helps identify the relevant fields and data sources for business growth

Much more cost-effective

Allows businesses to optimize operations and make informed decisions

Increases organizational efficiency by unearthing trends previously not found

What is Machine Learning?

Machine learning refers to a branch of Artificial Intelligence (AI) that emulates the learning mechanism of human beings through data and analytical algorithms.

Key Features of Machine Learning

The core functional elements of a machine learning model are:

Supervised learning:

A model is trained on datasets whose correct inputs are pre-determined

Unsupervised learning:

The AI agent learns to find the structure of data without any supervision or the presence of labeled datasets

Reinforcement learning:

The AI agent makes decisions based on a stringent feedback mechanism of rewards and punishments. The agent tries to maximize the rewards to reach the best outcome in an environment

Benefits of Machine Learning

Identifies complex data relationships without human intervention

Automates fraud detection mechanisms

Engages in continuous learning and improvement

Provides deep insights into business processes, accelerating automation of repetitive tasks, and increasing the value of human resources in a company

ALSO READ: What is Unsupervised Learning? What Benefits Does it Offer?

What is the Difference Between Data Mining and Machine Learning?

In the data mining vs. machine learning discussion, the following are some notable differences:

Responsibility

The prime course of action in data mining is finding out the hidden rules of data governing two or more datasets and predicting an outcome. On the other hand, machine learning algorithms are primarily responsible for teaching an AI agent how to learn, comprehend, and implement the rules of a system and apply them in real-world scenarios. 

Use Cases

Data mining vs. machine learning use cases falls into the categories of theoretical and practical applications, respectively. Data mining finds its use in research cases such as understanding and setting realistic business goals, collecting the kind of data relevant to a specific domain, market research, retail, and e-commerce, among others. Machine learning finds its use in developing real-life applications including self-driving cars, speech, image recognition, and medical diagnosis.

Accuracy

The involvement of human resources in the collection of data and finding possible patterns in data mining reduces the overall accuracy of the process. There are many intricate relationships and key associations among datasets that only ML algorithms can uncover by adjusting themselves according to the changing nature of the presented data in real-time. 

Use of Data

ML algorithms demand a significantly higher volume of data than the process of data mining. Moreover, they can perform automated data processing only after the entire batch of data is converted to a standard supported format. Meanwhile, data mining can provide results with lesser volumes of data and supports data reading in their native formats as well. 

Scope

ML algorithms learn from experience by analyzing extensive volumes of data; they can anticipate future outcomes in innumerable spheres of daily life, such as product recommendations, traffic prediction, self-driving cars, spam reduction, biological data analysis, and others. Data mining limits itself to finding hidden trends and enhances realistic business decisions, but with more human intervention and lesser operational bandwidth.

Techniques Involved

The primary data mining techniques involved are an association, prediction, classification, clustering, regression, and sequential analysis. A combination of these enables data scientists to research the different kinds of associations among datasets in a batch format. ML models, on the other hand, use regression analysis, supervised and unsupervised learning, and reinforcement learning to improve on the existing analysis without human intervention continuously. 

Nature

To do a data mining vs. machine learning comparison, we also need to recognize their respective natures. Data Mining is a manual process of using data analysis techniques to find hidden patterns and actionable insights. On the other hand, the entire process of ML is automated, which once implemented, is independent of human intervention.

Abstraction Human Factor

Data mining demands human intervention and intelligence at every step of the process, right up to the final analysis. Only the supervised learning module of ML models demands active human intervention and considerable feedback-based training with reinforcement techniques. Based on their history of reactions to previous events, they eventually sharpen their ability to function independently and process actionable outcomes.

ALSO READ: What is AIOps: How Businesses Use AI to Improve IT Operations

Which One is Better – Data Mining vs Machine Learning?

In the data mining vs machine learning comparison, ML is one step ahead. This is because ML models often utilize similar data mining techniques within a self-evolving learning environment to produce better predictions. Although ML is more expensive, data mining is primarily concerned with manually revealing associations between datasets. 

Frequently Asked Questions 1. What are the Similarities between Data Mining and Machine Learning?

Machine learning is often used to conduct data mining, and interesting data patterns found from data mining techniques are used to teach machines. Moreover, analysis methods often overlap in the two phenomena—for instance, the use of regression analysis and dealing with large datasets. 

2. What are the Main Types of Machine Learning?

There are four main types of Machine Learning:

Supervised learning

Unsupervised learning

Semi-supervised learning

Reinforcement learning

3. Which is Better Data Mining or Machine Learning?

Due to the automation involved, Machine Learning is more accurate than data mining.

As technology progresses, new terminologies will continue to flood the tech vocabulary. Thus, to save ourselves from erroneous usage and stay abreast of developments, constant knowledge upgrading is important. Explore the artificial intelligence and machine learning courses on the Emeritus platform to learn these concepts better.

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