Trending December 2023 # Top 8 Hidden Python Packages For Machine Learning In 2023 # Suggested January 2024 # Top 15 Popular

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

Introduction

As a data science enthusiast, I have seen people always talking about some famous libraries like for data manipulation pandas and NumPy, for data visualization matplotlib, seaborn, plotly, and many more, for modeling scikit-learn, TensorFlow, etc. In this article I’m not going to cover these libraries as tons of blogs are already available, check my article on the most used python libraries here. But in his article, I am going to cover some hidden gems of python libraries that are unknown to the data science world. These are some important libraries you can check out in 2023.

These libraries include functionality such as handling missing values in an organized way, handle emojis, converting numbers into ints and floats, visualization intelligence tools, time series modelling and many more. It covers a vast range of topics like from natural language processing to data visualization and time series. So let’s get started.

  Table of Contents

Missingo

Emot

Bamboolib

ppscore

AutoViz

Numerizer

PyFlux

Flash Text

  Missingo

The real-world datasets generally contain a lot of missing and null values. This might be due to various reasons like data leakage, data is not available etc. Sometimes, it is very irritating to deal with these kinds of messy data. This messy data requires special attention before feeding into machine learning algorithms as these algorithms don’t handle missing values.

We need a better approach to handle these missing values. Here comes the magic of the python library called missingo. It helps us to deal with missing values with the help of data visualisations in a much better way. This is based on matplotlib. As of now April 2023, it has four types of plots for the understanding distribution of missing data namely bar chart. heatmap, matrix, and dendrogram. So let’s get started.

Installation pip install missingo Importing  the library import missingo as msns

In the below bar plot, you can see the number of missing values in each column:

For more information, check the official documentation: Link

Emot

Emojis are very common in chats. When you deal with natural language processing tasks, it is very tedious to deal with Emojis. Here comes a very handy library to get rid of the emoticons from the text data. It is a famous python library that is very useful when we have to deal with Emoji and Emoticons. It works well with Python 2 and Python 3. It takes a string as an input and returns a list of the dictionary. So let’s get started.

Installation

pip install emot import emot

Code

import emot text = "I love python 👨 :-)" emot.emoji(text) [{'value': '👨', 'mean': ':man:', 'location': [14, 14], 'flag': True}] emot.emoticons(text) {'value': [':-)'], 'location': [[16, 19]], 'mean': ['Happy face smiley'], 'flag': True}

For more information, check the official documentation: Link

Bamboolib

Analyzing and Visualizing the information is the most significant and time taking interaction. We need to put a great deal of time to unmistakably investigate what is the issue here and what it is attempting to tell. We utilize various sorts of python libraries to envision the examples and oddities in the dataset to get comfortable with the dataset.

Bamboolib is GUI for pandas DataFrames that empowers anybody to work with python in Jupyter Notebook or JupyterLab. Bamboolib is a profoundly intelligent and broadly supportive library to examine, imagine, and control information.

Indeed, even an individual with a non-programming foundation can utilize it to draw bits of knowledge from information since it doesn’t need any coding experience. Bamboolib isn’t open-source which implies that you need to purchase bamboolib to utilize it, yet it gives a 14-day free preliminary form so you can completely investigate it and perceive how it very well may be valuable for you.

Installation pip install bamboolib import bamboolib

For more information, check the official documentation: Link

Ppscore

Full from of ppscore is Predictive Power Score. This python library is made by bamboolib developers. The Predictive Power Score is an alternative to the correlation matrix. This score is asymmetric and can detect the linear or non-linear relationships between two columns in our dataset. So let’s get started with this library.

Installation

pip install ppscore import ppscore

For more information, check the official documentation: Link

AutoViz

Installation pip install autoviz Importing the library import autoviz

For more information, check the official documentation: Link

Numerizer

etc. So let’s get started.

Installation pip install numerizer Importing the library from numerizer import numerize Code numerize(‘forty-two’) '42' numerize('

one billion and one

') '

1000000001'

For more information, check the official documentation: Link

PyFlux

Time series investigation is quite possibly the most oftentimes experienced issues in the Machine learning area. PyFlux is an open-source library in Python that unequivocally worked for working with time series issues. The library has a brilliant cluster of present-day time arrangement models including yet not restricted to ARIMA, GARCH, and VAR models. So, PyFlux offers a probabilistic way to deal with time arrangement displaying. So let’s get started.

Installation pip install pyflux Importing the library import pyflux

For more information, check the official documentation: Link

FlashText

FlashText is a Python library made explicitly to search at replacing the words in a record. Presently, how FlashText works is that it requires a word or a rundown of words and a string. The words which FlashText calls keywords are then looked at or supplanted in the string.

Allow us to look at in insight regarding FlashText working. At the point when keywords are passed to FlashText for looking or supplanting, they are put away as a Trie Data Structure which is productive at Retrieval assignments. So let’s get started.

Installation

pip install flashtext import flashtext

Searching:

 

Replacing:

For more information, check the official documentation: Link

Final Note

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Top Machine Learning Jobs To Apply In November 2023

Apply to these top machine learning  jobs Machine Learning Specialist at Standard Chartered Bank

Chennai, Tamil Nadu, India

Requirements

Experience 0-6 years

A strong foothold on machine learning and deep learning concepts

Preference for work experience in unstructured data-based models using NLP and computer vision

Knowledge of full-stack machine learning on all phases of model design to deployment

Python/Django, API development, Git, TensorFlow, Numpy, Pandas, Jenkins

Ability to do fast prototypes and interest to work in cutting edge research areas such as explainable AI, federated learning, reinforcement learning etc.

Knowledge of cloud (AWS Preferred)

Github links/blogs that can showcase your work.

Microsoft Azure Machine Learning Application Lead at Accenture 

Bengaluru, Karnataka, India Project Role: Application Lead Project Role Description: Lead the effort to design, build and configure applications, acting as the primary point of contact. Management Level:9 Work Experience:6-8 years’ Work location: Bengaluru Must Have Skills: Microsoft Azure Machine Learning Good To Have Skills: No Function Specialization Key Responsibilities: Solely responsible for the machine learning-based software solution and work independently based on inputs from the other department’s design, develop, troubleshoot and debug products/solutions in the AI/ML domain Work with partners within/outside BU to develop and commercialize products/solutions Help to create a cloud-based machine learning environment 2 support the overall development support firmware development / embedded system. Technical Experience: Strong Knowledge of machine learning, deep learning, natural language processing, and neural networks 2 experience with any of the languages Nodejs, python or Java familiarity with ML tools, and packages like OpenNLP, Caffe, Torch, TensorFlow, etc also knowledge on SQL, Azure DevOps CI/CD, Docker, etc. Professional Attributes : He / She must be a good team player with good analytical skills, good communication and Interpersonal skills 2 Should have good work ethics, always can-do attitude, good maturity and professional attitude should be able to understand the organizational and business goal and work with the team.  

Machine Learning Engineer at Pratiti Technologies

Pune, Maharashtra, India

Job Profile:

Design and build machine learning models and pipeline Role Description: The role requires you to think critically and design with first principles. You should be comfortable with multiple moving parts, microservices architecture, and de-coupled services. Given you are constructing the foundation on which data and our global system will be built, you need to pay close attention to detail and maintain a forward-thinking outlook as well as scrappiness for the present needs. You are very comfortable learning new technologies, and systems. You thrive in an iterative but heavily test-driven development environment. You obsess over model accuracy + performance and thrive on applied machine learning techniques to business problems.  

You are a good fit if you:

Have strong experience building natural language processing (NLP) based systems, specifically in areas such as event and topic detection, relation extraction, summarization, entity recognition, document classification, and knowledge-based generation

Have experience with NLP and machine learning tools and libraries such as NumPy, Genism, SpaCy, NLTK, Scikit-learn, Tensorflow, Kerasetc

Learn new ways of thinking about age-old problems

Are passionate about driving the performance of machine learning algorithms towards the state of the art and in challenging us to continually improve what is possible

Have experience in distributed training infrastructure and ML pipelines.

Senior Machine Learning Engineer – India at Bungee Tech

 India Remote

Job Responsibilities:

Work with Business stakeholders to understand the customer requirements of our SaaS products and expand the product vision using the power of AI/ML

Provide clear, compelling analysis that shapes the direction of our business

Build neural net models that contribute to the enhancement of our image and text processing algorithms

Harness neural net-based natural language processing models to create new path-breaking capabilities in the retail business

Use machine learning, data mining, statistical techniques, etc. to create actionable, meaningful, and scalable solutions for business problems

Analyze and extract relevant information from large amounts of data and derive useful insights at a big data scale.

Work with software engineering teams, data engineers, and ML operations team (Data Labelers, Auditors) to deliver production systems with your deep learning models

Architecturally optimize the deep learning models for efficient inference, reduce latency, improve throughput, reduce memory footprint without sacrificing model accuracy

Establish scalable, efficient, automated processes for large-scale data analyses, model development, model validation, and model implementation

Create and enhance a model monitoring system that could alert real-time anomalies with the model.

Streamline ML operations by envisioning human-in-the-loop kind of workflows, collect necessary labels/audit information from these workflows/processes, that can feed into improved training and algorithm development process

Maintain multiple versions of the model and ensure the controlled release of models.

Machine Learning Ops Engineer – Analytics at Optimal Strategix Group, Inc.

Bengaluru, Karnataka, India Hybrid  

Key Responsibilities:

Takes ownership for MLOPs for product development (on the cloud using microservices architecture)

Connect AI/ML modules to frontend and backend to build scalable and reproducible solutions

Setup pipelines, design and develop RESTful APIs for ML solution deployment

Ensuring code/solution delivery within schedule

Coordinating with external stakeholders and internal cross-functional teams in ensuring timelines are met and clear communication is established

Has an automation mindset and continuously focus on improving current processes

Proactively find issues and consult on possible solutions. Should be good at problem-solving.

Skills / Competencies:

Experience in NLP, AWS, and/or Azure Infrastructure and comfortable with frameworks like MLFLow, AirFlow, Git, Flash, Docker, Spark.

Must have experience including hands-on skills in Python, SQL, Dockers

Hands-on experience in AWS like S3, VPC, EC2, Route-53, RDS, cloud formation, cloud watch, Lambda

Should have experience in architecting and developing solutions for end-to-end pipelines

Proficiency in Python programming – data wrangling, data visualization, machine learning, statistical analysis

Handling of unstructured data – Video/Image/Sound/Text

The ability to keep current with the constantly changing technology

Ability to design, develop, test, deploy, maintain, and improve ML Modules and infra

Ability to manage project priorities, deadlines, and deliverables

Design and build new data pipelines from scratch till deployment for projects

Mentor other MLOPs engineers

Excellent verbal and written communication skills

Should be able to articulate ideas clearly

Ability to think independently and take responsibility

Should show inquisitiveness in understanding business needs

Should be able to understand the business context

Good time management and organizational skills

Sense of urgency in completing project deliverables in time.

Chennai, Tamil Nadu, IndiaBengaluru, Karnataka, India Project Role: Application Lead Project Role Description: Lead the effort to design, build and configure applications, acting as the primary point of contact. Management Level:9 Work Experience:6-8 years’ Work location: Bengaluru Must Have Skills: Microsoft Azure Machine Learning Good To Have Skills: No Function Specialization Key Responsibilities: Solely responsible for the machine learning-based software solution and work independently based on inputs from the other department’s design, develop, troubleshoot and debug products/solutions in the AI/ML domain Work with partners within/outside BU to develop and commercialize products/solutions Help to create a cloud-based machine learning environment 2 support the overall development support firmware development / embedded system. Technical Experience: Strong Knowledge of machine learning, deep learning, natural language processing, and neural networks 2 experience with any of the languages Nodejs, python or Java familiarity with ML tools, and packages like OpenNLP, Caffe, Torch, TensorFlow, etc also knowledge on SQL, Azure DevOps CI/CD, Docker, etc. Professional Attributes : He / She must be a good team player with good analytical skills, good communication and Interpersonal skills 2 Should have good work ethics, always can-do attitude, good maturity and professional attitude should be able to understand the organizational and business goal and work with the team.Pune, Maharashtra, IndiaDesign and build machine learning models and pipeline Role Description: The role requires you to think critically and design with first principles. You should be comfortable with multiple moving parts, microservices architecture, and de-coupled services. Given you are constructing the foundation on which data and our global system will be built, you need to pay close attention to detail and maintain a forward-thinking outlook as well as scrappiness for the present needs. You are very comfortable learning new technologies, and systems. You thrive in an iterative but heavily test-driven development environment. You obsess over model accuracy + performance and thrive on applied machine learning techniques to business problems.India RemoteBengaluru, Karnataka, India Hybrid

Top 8 Python Libraries For Natural Language Processing (Nlp) In 2023

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

Introduction

Natural language processing (NLP) is a field situated at the convergence of data science and Artificial Intelligence (AI) that – when reduced to the basics – is all about teaching machines how to comprehend human dialects and extract significance from the text. This is additionally why Artificial Intelligence is regularly essential for NLP projects.

So in this article, we are going to cover the top 8 Natural Language Processing(NLP) libraries and tools that could be useful for build real-world projects. So let’s start!

Table Of Contents

Natural Language Toolkit(NLTK)

GenSim

SpaCy

CoreNLP

TextBlob

AllenNLP

polyglot

scikit-learn

Natural Language Toolkit (NLTK)

Entity Extraction

Part-of-speech tagging

Tokenization

Parsing

Semantic reasoning

Stemming

Text classification

GenSim

For more information, check official documentation: Link.

SpaCy

SpaCy is an open-source python Natural language processing library. It is mainly designed for production usage- to build real-world projects and it helps to handle a large number of text data. This toolkit is written in python in Cython which’s why it much faster and efficient to handle a large amount of text data. Some of the features of SpaCy are shown below:

It provides multi trained transformers like BERT

It is way faster than other libraries

Provides tokenization that is motivated linguistically In more than 49 languages

Provides functionalities such as text classification, sentence segmentation, lemmatization, part-of-speech tagging, named entity recognition and many more

has 55 trained pipelines in more than 17 languages.

For more information, check official documentation: Link.

CoreNLP

Stanford CoreNLP contains a grouping of human language innovation instruments. It means to make the use of semantic analysis tools to a piece of text simple and proficient. With CoreNLP, you can extract a wide range of text properties (like part-of-speech tagging,named-entity recognition and so forth) in a couple of lines of code.

Since CoreNLP is written in Java, it requests that Java be introduced on your device. Notwithstanding, it offers programming interfaces for some well-known programming languages, including Python. The tool consolidates various Stanford’s NLP tools like the sentiment analysis, part-of-speech (POS) tagger, bootstrapped pattern learning, parser, named entity recognizer (NER), coreference resolution system, to give some examples. Besides, CoreNLP upholds four dialects separated from English – Arabic, Chinese, German, French, and Spanish.

For more information, check official documentation: Link.

TextBlob

TextBlob is an open-source Natural Language Processing library in python (Python 2 and Python 3) powered by NLTK. It is the fastest NLP tool among all the libraries. It is beginners friendly. It is a must learning tool for data scientist enthusiasts who are starting their journey with python and NLP. It provides an easy interface to help beginners and has all the basic NLP functionalities such as sentiment analysis, phrase extraction, parsing and many more. Some of the features of TextBlob are shown below:

Sentiment analysis

Parsing

Word and phrase frequencies

Part-of-speech tagging

N-grams

Spelling correction

Tokenization

Classification( Decision tree. Naïve Bayes)

Noun phrase extraction

WordNet integration

For more information, check official documentation: Link.

AllenNLP

For more information, check official documentation: Link.

Polyglot

This marginally lesser-realized library is one of my top choices since it offers an expansive scope of analysis and great language inclusion. On account of NumPy, it likewise works super quick. Utilizing multilingual is like spaCy – it’s proficient, clear, and fundamentally a fantastic choice for projects including a language spaCy doesn’t uphold.

Following are the features of Polyglot:

Tokenization (165 Languages)

Language detection (196 Languages)

Named Entity Recognition (40 Languages)

Part of Speech Tagging (16 Languages)

Sentiment Analysis (136 Languages)

Word Embeddings (137 Languages)

Morphological analysis (135 Languages)

Transliteration (69 Languages)

For more information, check official documentation: Link.

Scikit-Learn

For more information, check official documentation: Link

Conclusion

So in this article, we have covered the top 8 Natural Language Processing libraries in python for machine learning in 2023. I hope you learn something from this blog and it will turn out best for your project. Thanks for reading and your patience. Good luck!

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Top 10 Machine Learning Model Monitoring Tools Of 2023

All you need to about top machine learning model monitoring tools

Many companies in the modern world are greatly reliant on machine learning models and monitoring tools. These tools help in animation, unsupervised learning, avoid prediction errors, self-iteration based on data, and dataset visualization. The market for these tools is expected to grow by US$4 billion.  

Anodot

You might have plenty of data in your bag, but it is useless if you can’t use it to understand your business. Anodot is an AI monitoring tool that understands your data automatically. It can monitor multiple things simultaneously, such as customer experience, partners, revenue, and Telco networking. The software is built from the ground to ensure it interprets the data, analyzes it, and correlates it to better your company’s performance.  

KFServing

KFServing is an ML model that generously provides Tensorflow, XGBoost, PyTorch, high abstract interfaces, Performant, and ONNX to help solve production model serving use cases by a great degree. It works with Kubeflow 1.3. the tool is dedicated to making deployments scalable and simple. The tool comes with many features that can benefit companies that use it.  

Pachyderm

Many companies try to find free machine learning software and the ones that function the best. Let’s shed some light on Pachyderm. You can try the features for free. Pachyderm has a lot to offer, thanks to its automotive abilities. It can control and analyze petabytes of data for companies. Its scientific lineage helps data scientists introduce repeatable and scalable experiments. Pachyderm has a foundation of Kubernetes and dockers, which deploy machine learning projects on various cloud platforms. Pachyderm possesses over 5000 stars on GitHub and ensures that the data it works with is retractable and vision as it is ingested into a machine learning system. Many forward-thinking companies like a woven planet, digital reasoning, and general fusion trust this tool.  

Fiddler

One of the top ten tools in the world of model monitoring is Fiddler. It comes with a fine, user-friendly interface that is easy to use and quite clear. With the help of Fiddler, you can debug predictions, explain them, analyze model behavior, manage datasets, and many other things.  

Seldon Core

Seldom Core is one of the best machine learning software you will ever come across. If you are going for an open-source platform, then don’t shy away from Seldon Core. It is an expert at deploying models. It will allow users to deploy, manage, monitor, and package multiple machine learning models. The best bit about Seldon Core is that it offers great support to ML libraries, languages, toolkits and can run on any cloud. In addition, the security system is robust and prevents the safety of data. The tool also has the power to convert your language wrappers like Java and Python along with your ML models like PyTorch into REST or GRPC production microservices.  

Google Cloud AI Platform

Although the open-source monitoring dashboard is limited to 25 running models in parallel and isn’t the ideal choice for hybrid cloud deployments, the Google cloud AI platform has a user-friendly interface and offers AI explanations and solutions. In addition, the out-of-the-box CV algorithms will leave your mind blown along with the video processing modules. Moreover, its connection with the TPUs and TensorFlow is quite reliable and good.  

Flyte

When we talk about open-source tools for data science, we can’t leave flyte out of the list. It is an MLOps platform that helps maintain, monitor, track, and automate Kubernetes. It constantly focuses on tracking any changes in the model and makes sure it is reproducible. Flyte containerized the model and is written in Python, designed to support the complicated workflows written in Java, Scala, and Python. The tool helps keep the firm compliant with any data changes. All workflows are properly typed as inputs and outputs to parameterize workflows, use pre-computed artifacts, and maintain a rich data lineage. Flyte’s smart use of cached output saves time and money! It handles data preparation, model training, computing metrics, and model validation like a pro.  

ZenML

ZenML is an open-source machine learning tool that offers comparability between two experiments. It also reproduces through automated tracked experiments, versioned code and data, and declarative pipeline configurations. The cached pipeline allows for quick experiment iterations. The tool has pre-built helpers that visualize and compare results and parameters. ZenML also has built-in extractions for training jobs (cloud-based), model serving, and distributed large datasets. The famous Python writes the framework. The tool is great for transforming and evaluating data. Additionally, it works with tools such as Jupyter notebooks. It does so to deploy machine learning models into employment.  

Anaconda

Anaconda is not just another ML tool, and it has a lot to offer. More than 20 million users rely on it. The tool comes with great management and environment that makes it user-friendly and has multiple libraries and Python versions. Indeed, Anaconda doesn’t have PyCharm, Docker, and Atom; nevertheless, it offers pre-installation of libraries and packages. There are more than 7500 Conda packages, and it only costs $14.95 per month! If you don’t want to pay, don’t worry; there is a free individual edition. It is a versatile tool that can solve multiple problems for you in no time.  

TensorFlow

How To Resume Python Machine Learning If The Machine Has Restarted?

Introduction

Python ranks as one of the most widely used programming languages for machine learning for its simplicity of being used, adaptability, and broad library and tool set. Yet, one challenge that many developers have when working with Python for machine learning is how to resume work if their system unexpectedly restarts. This is incredibly frustrating if you’ve spent hours or days training a machine learning model only to have all of your efforts destroyed due to a sudden shutdown or restart.

In this post, we’ll look at different ways for resuming Python machine-learning work once your system has restarted.

Strategies 1. Use a checkpoint system

A checkpoint system is one of the finest ways to resume your Python machine-learning work after a restart. This entails preserving your model’s parameters and state after every epoch so that if your system suddenly restarts, you can simply load the most recent checkpoint and begin training from where you left off.

Most machine learning packages, such as TensorFlow and PyTorch, have checkpoint creation capability. With TensorFlow, for example, you may use the tf.train.Checkpoint class to save and restore your model’s state. With PyTorch, you may use the torch.save() method to store the state of your model to a file and the torch.load() function to load it back into memory.

2. Save your data and preprocessed features

You should store your data as well as any heavily processed features you’ve developed in addition to the state of your model. You can save time and money by not having to repeat time-consuming preprocessing processes like normalization or feature scaling.

Data and highly processed features may be saved in a number of file formats, including CSV, JSON, and even binary formats like NumPy arrays or HDF5. Be sure to just save your data in a format compatible with your machine-learning library so that it can be loaded back into memory rapidly.

3. Use cloud-based storage solutions

A cloud-based storage solution, such as Google Drive or Amazon S3, is another choice for restarting your Python machine-learning work after a restart. These services let you to save your model checkpoints and data in the cloud and retrieve them from any workstation, even if your local system has restarted.

To use cloud-based storage options, you must first make an account with the service of your choosing, and then upload and download your files using a library or tool. You may use the down library, for example, to download files from Google Drive, or the boto3 library to communicate with Amazon S3.

4. Use containerization

Another approach for resuming your Python machine learning work after a restart is containerization. Containers allow you to combine your code and dependencies into a single, portable entity that can be easily transferred across machines or environments.

To use containerization, you must first create a Docker image including your Python code, dependencies, and any necessary data or checkpoints. You may then run this image on any system with Docker installed, eliminating the need to reload dependencies or rebuild your environment.

5. Use version control

Lastly, using version control is another method for continuing your Python machine-learning work after a restart. Version control solutions, such as Git or SVN, allow you to track changes to your code and data over time and can assist you in avoiding work loss due to unexpected restarts or failures.

To utilize version control, you must first build a repository for your project and then periodically commit changes to the repository. This records changes to your code and data and allows you to simply revert to a prior version if something goes wrong.

Apart from version control, using a cloud-based Git repository, such as GitHub or GitLab, can give other benefits like automated backups, collaboration capabilities, and connections with other services.

Conclusion

Coping with unexpected machine restarts may be an aggravating and time-consuming process, particularly when working on a machine learning project. But, by using some of the tactics discussed in this article, such as checkpoints, cloud-based storage solutions, containerization, and version control, you may help reduce the effect of unexpected restarts and continue your work more quickly and simply.

It is crucial to remember that based on your unique project and requirements, alternative tactics may be more or less suited. For example, if you deal with a significant volume of data, a cloud-based storage solution may be more practical than attempting to keep everything local.

Therefore, the key to properly continuing your Python machine learning work after a restart is to plan ahead of time and be ready for unforeseen interruptions. By adopting some of these tactics into your workflow, you may assist to make your work more robust and less vulnerable to unexpected disruptions.

Model Validation In Machine Learning

Introduction

Model validation is a technique where we try to validate the model that has been built by gathering, preprocessing, and feeding appropriate data to the machine learning algorithms. We can not directly feed the data to the model, train it and deploy it. It is essential to validate the performance or results of a model to check whether a model is performing as per our expectations or not. There are multiple model validation techniques that are used to evaluate and validate the model according to the different types of model and their behaviors.

What is Model Validation?

Machine learning is all about the data, its quality, quantity, and playing with the same. Here most of the time, we collect the data; we have to clean it, preprocess it, and then we have to apply the appropriate algorithm and get the best-fit model out of it. But after getting a model, the task is not done; the model validation is as important as the training.

Directly training and then deploying a model would not work, and in sensitive areas like the healthcare model, there is a huge amount of risk associated, and real-life predictions have to be made; in this case, there should not be an error in the model as it can cost a lot then.

Advantages of Model Validation Quality of the Model The flexibility of the Model

Secondly, validating the model makes it easy to get an idea about the flexibility. Model validation helps make the model more flexible also.

Overfitting and Underfitting

Model validation help identify if the model is underfitted or overfitted. In the case of underfitting, the model gives high accuracy in training data, and the model performs poorly during the validation phase. In the case of underfitting, the model does not perform well during either the training or validation phase.

There are many techniques available for validating the model; let us try to discuss them one by one.

Train Test Split

Train test split is one of the most basic and easy model validation techniques used to validate the data. Here we can easily split the data into two parts, the training set, and the testing set. Also, we can choose in which ratio we want to split the data. 

There is one problem associated with the train_test_split method if there is any subset of the data that is not present in the training set and is present in the testing set, then the model will give an error.

Hold Out Approach

Hold out approach is also very similar to the train test split method; just here, we have an additional split of the data. While using the train test split method, it may happen that there are two splits of the data, and the data can be leaked, due to which the overfitting of the model can take place. To overcome this issue, we can still split the data into one more part called hold out or validation split.

So basically, here, we train our data on the big training set and then test the model on the testing set. Once the model performs well on both the training and testing set, we try the model on the final validation split to get an idea about the behavior of the model in unknown datasets.

K Fold Cross Validation

K fold cross-validation is one of the widely used and most accurate methods for splitting the data into its training and testing points. In this approach, the logic or the working mechanism of the KNN algorithm is used. Same as the KNN algorithm, here we also have a term called K which is the number of splits of the data.

In this method, instead of splitting the data a single time, we split the data multiplied based on the value of K. Let us suppose that the value of K is defined as 5. Then the model will split the dataset five times and will choose different training and testing sets every single time.

Lean One Out Method

Leave one out is also a variant of the K fold cross-validation technique where we have defines the K as n. Where n is the number of samples or data observations we have in our dataset. Here the model trains and tests on every data sample, and the model considers each sample as a testing set and others as a training set.

Although this method is not used widely, the holdout and K fold approach solves most of the issues related to model validation.

Key Takeaways

Model validation is one of the most important tasks in machine learning which should be implemented for all models to be deployed.

Model validation gives us an idea about the behavior of the model, its performance on the data, problems like overfitting and underfitting, and the errors associated with the model.

Train test split and hold-out approaches are easy and the most common method for model validation where the data is splintered into two or three parts and the model is validated on the testing set.

Leave one out is a variant of the K fold approach where the model leaves one observation of the data out of the training set and use it as a testing set.

Conclusion

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