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Boosting is an ensemble learning technique where each model attempts to correct the errors of the previous model.
Learn about the Gradient boosting algorithm and the math behind it.Introduction
In this article, we are going to discuss an algorithm that works on boosting technique, The Gradient Boosting algorithm. It is more popularly known as Gradient boosting Machine or GBM.
Note: If you are more interested in learning concepts in an Audio-Visual format, We have this entire article explained in the video below. If not, you may continue reading.
The models in Gradient Boosting Machine are building sequentially and each of these subsequent models tries to reduce the error of the previous model. But the question is how does each model reduce the error of the previous model? It is done by building the new model over errors or residuals of the previous predictions.
This is done to determine if there are any patterns in the error that is missed by the previous model. Let’s understand this through an example.
Here we have the data with two features age and city and the target variable is income. So, based on the city and age of the person we have to predict the income. Note that throughout the process of gradient boosting we will be updating the following the Target of the model, The Residual of the model, and the Prediction.Steps to build Gradient Boosting Machine Model
To simplify the understanding of the Gradient Boosting Machine, we have broken down the process into five simple steps.Step 1
The first step is to build a model and make predictions on the given data. Let’s go back to our data, for the first model the target will be the Income value given in the data. So, I have set the target as original values of Income.
Now we will build the model using the features age and city with the target income. This trained model will be able to generate a set of predictions. Which are suppose as follows.
Now I will store these predictions with my data. This is where I complete the first step.Step 2
The next step is to use these predictions to get the error, which will be used further as a target. At the moment we have the Actual Income values and the predictions from the model1. Using these columns, we will calculate the error by simply subtracting the actual income and the predictions of income. A shown below.
As we mentioned previously the successive models focus on the error. So the errors here will be our new target. That covers up step two.Step 3
In the next step, we will build a model on these errors and make the predictions. Here the idea is to determine, Is any hidden pattern in the error.
So using the error as target and the original features Age and City, we will generate new predictions. Note that the predictions, in this case, will be the error values, not the predicted income values, since our target is the error. Let’s say the model gives the following predictionsStep 4
Now we have to update the predictions of model1. We will add the prediction from the above step and add that to the prediction from model1 and name it Model2 Income.
As you can see my new predictions are closer to my actual income values.
Finally, we will repeat steps 2 to 4, which means we will be calculating new errors and setting this new error as a target. We will repeat this process till the error becomes zero or we have reached the stopping criteria, which says the number of models we want to build. That’s the step-by-step process of building a gradient boosting model.
In a nutshell, We build our first model that has features x and target y, let’s called this model H0 that is a function of x and y. Then we build the next model on the errors of the last model and a third model on the errors of the previous model and so on. Till we build n models.
Each successive model works on the errors of all previous models to try and identify any pattern in the error. Effectively, I can say that each of these models is individual functions having independent variable x as the feature and the target is the error of the previous combined model.
So to determine the final equation of our model, we build our first model H0, which gave me some predictions and generated some errors. Let’s call this combined result F0(X).
Now we created our second model and added new predicted errors to F0(X), this new function will be F1(X). Similarly, we will build the next model and so on, till we had n models as shown below.
So, at every step, we are trying to model the errors, which helps us to reduce the overall error. Ideally, we want this ‘en’ to be zero. As you can see each model here is trying to boost the performance of the model hence we use the term boost.
But why we use the term gradient, here is the catch. Instead of adding directly these models, we add them with weight or coefficient, and the right value of this coefficient is decided using the gradient boosting technique.
Hence, a more generalized form of our equation will be as follows.The math behind Gradient Boosting Machine
I hope, now you have a broad idea of how gradient boosting works. Here onward, we will be focusing on how the value of Yn is calculated.
We will use the gradient descent technique to get the values of these coefficients gamma(Y), such that we minimize the loss function. Now let’s dive deeper into this equation and understand the role of the loss function and gamma.
Here, the loss function we are using is (y-y’)2. y is the actual value and y’ is the final predicted value by the last model. So, we can replace y’ with Fn(X) which represents the actual target minus the updated predictions from all the models we have built so far.Partial Differentiation
I believe you would be familiar with the gradient descent process as we are going to use the same concept. We will differentiate the equation of L with respect to Fn(X), you will get the following equation, which is also known as pseudo residual. Which is the negative gradient of the loss function.
To simplify this, we will multiply both sides with -1. The result will be something like this.
Now, we know the error in our equation of Fn+1(X) is the actual value minus updated predictions from all the models. Hence, we can replace the en in our final equation with these pseudo residuals as shown in the image below.
So this is our final equation. The best part about this algorithm is that it gives you the freedom to decide the loss function. The only condition is that the loss function should be differentiable. For ease of understanding, we used a very simple loss function (y-y’)2 but you can change it to a hinge loss or logit loss or anything.
The aim is to minimize the overall loss. Let’s see what would be the overall loss here, it will be the loss up to model n plus the loss from the current model we are building. Here is the equation.
In this equation, the first part is fixed but the second part is the loss from the model we are currently working on. The loss of this model still can not be changed but we can change the gamma value. Now we need to select the value of gamma such that the overall loss is minimized and this value is selected using the gradient descent process.
So the idea is to reduce the overall loss by deciding the optimum value of gamma for each model that we build.Gradient Boosting Decision Tree
I talk about a special case of gradient boosting i.e Gradient boosting decision tree (GBDT). Here, each model would be a tree and the value of gamma will be decided at each leaf-level, not at the overall model level. So as sown in the following image each leaf would have a gamma value.
That’s how Gradient Boosting Decision Tree work.End Notes
Boosting is a type of ensemble learning. It is a sequential process where each model attempts to correct the errors of the previous model. This means every successive model is dependent on its predecessors. In this article, we saw the gradient boosting algorithm and the math behind it.
As we have a clear idea of the algorithm, try to build the models and get some hands-on experience with it.
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Open-source data science projects are a great way to boost your resume
Try your hand at these 6 open source projects ranging from computer vision tasks to building visualizations in RLooking for open-source data science projects?
Projects play a HUGE part in cracking data science interviews. I’ve personally taken over a hundred interviews in the last year and quite often, the final round comes down to the quality of these data science projects. This is especially relevant for newcomers and freshers in data science.
What kind of projects have you picked up? How did you perform on these projects? Did you beat the benchmark model? Did you experiment with the source code and build something different?
These are critical questions that might make or break your data science interview. I always encourage folks to take up a diverse range of data science projects and try to learn from that as much as possible.
I will cover 6 such open-source data science projects in this article. I love putting this out at the start of every month (this is the 25th edition!). You’ll see a broad range of projects here, from performing computer vision tasks using MS Excel to drawing up a unique visualization in R.
You can check out the entire archive of open source data science projects here. And here’s the collection I picked out last month.6 Open-Source Data Science Projects Computer Vision using Deep Learning Open Source Projects
What’s the last MAJOR development you remember from the computer vision space? I’ve come across articles recently saying we’ve hit the proverbial deep learning wall – and there is no way up from there.
I respectfully disagree with this. There is a LOT more to uncover and unpack in deep learning (and computer vision in particular). If you’re wondering where I’m getting this level of confidence from, wait till you check out the below open-source computer vision using deep learning projects!
There are more jobs in deep learning and computer vision than ever before. And that trend is likely to increase exponentially in 2023. Time to get on board and polish up your computer vision skills!
You should check out the below resources to get started with deep learning and computer vision:
Real-time object detection has really gathered pace in the last year or so. I love the different applications we can design using real-time object detection, such as tracking a football or a player during a game.
Now here’s a really cool Hollywood-level computer vision project – removing people from complex backgrounds in real-time using deep learning! The developers off this project have used chúng tôi to build their model.
Check out this example:
This was done in real-time in a web browser! That’s the beauty of chúng tôi The GitHub repository I’ve linked above contains the code to implement the project in your own machine.
Here are a couple of in-depth computer vision tutorials to get you started with these concepts:
You can detect faces and find edges and lines using the tutorial provided in the project on GitHub. Here’s a quick look at what you’ll be building in Excel:
You don’t need any background in computer vision to work on this project. You will, however, need to know at least how a weighted average is calculated (and knowledge of Excel is required, of course).
So whether you’re a newcomer in deep learning and computer vision, or are coming from a software development background, this project is for you! Go ahead and try it out on your own machine and let me know about the crazy applications you build.
Here are a couple of resources to learn MS Excel:Other Open Source Data Science Projects
Here are a few non-computer vision and non-deep learning projects I wanted to highlight. These cover a range of data science topics, from data visualization in R to the importance of software engineering in machine learning.
If you’re looking for a comprehensive, end-to-end course on machine learning, look no further!
An R project! It’s a miracle! I’m a heavy R user and I love working with the wonderful ggplot2 library – but there haven’t been a lot of recent updates to report about. So I was thrilled when I came across ggbump last month.
ggbump is an R visualization package for, you guessed it, creating bump charts. Here’s an example of what you can draw using ggbump:
Bump charts are typically used to compare two dimensions against each other using one measure value (all you Tableau folks will understand this!). The majority of use cases focus on exploring the changes in the rank of a value over time (like the bump chart above).
ggbump isn’t on CRAN yet but you can install it directly in R using the below command:devtools::install_github("davidsjoberg/ggbump")
Here are a few resources to get you started with data visualization in R and Tableau:
Redshift Warehouse Module
I encourage you to go through the below tutorial on building your own machine learning pipeline using sklearn:
This is a fascinating project. Graphs can appear to be daunting at first, but once you get an idea of how they work, you’ll love working with them.
Graph neural networks (GNN) are behind applications like social media network analysis, knowledge trees, recommendation systems, and much more.
The GitHub repository I’ve linked above provides the implementation of various flavors of graph neural networks in TensorFlow 2.0. You have a few training script examples in the repository as well to get you on your way.
You can install the Python library from pip:pip install tf2_gnn
I’ve provided resources below to help you understand the various concepts behind graph neural networks:
Software engineering is a very under-rated part of the machine learning pipeline. Experts don’t discuss it, courses don’t usually cover it, and data science aspirants don’t study about it.
And yet, when you sit for a data science interview, you’ll inevitably face a ton of software engineering questions. How do you set up a machine learning pipeline? What is model deployment? And so on.
This wonderful repository offers a curated list of tutorials that cover software engineering best practices for building machine learning applications. Here’s what the repository currently covers:
Broad Overview of Software Engineering in Machine Learning
Deployment and Operation
Trust me, software engineering is a must-have skill in your data scientist’s resume. You need to get on board with this and start picking up these skills.End Notes
My pick of the above open-source projects:
Computer Vision using Microsoft Excel
ggbump in R
The hype for highest paying companies for Data Scientists attracts more Aspirants
TheTop companies paying high salaries to data scientists
Data Scientist’s salary: US$124,333 Oracle is one of the largest vendors in the enterprise IT market and the shorthand name of its flagship product, a relational database management system that’s formally called Oracle Database. In 1979, Oracle became the first company to commercialize an RDBMS platform. The enterprise software company offers a range of cloud-based applications and platforms as well as hardware and services to help companies improve their processes. Oracle recently announced the availability of its cloud data science platform, a native service on Oracle Cloud Infrastructure (OCI).
Data Scientist’s salary: US$162,931 Pinterest is a social sharing website where individuals and businesses can ‘pin’ images on ‘boards’ in order to share visual content with friends and followers. Today, many businesses are using interest as a source to enhance their business by promoting content in it. Pinterest creates a lot of online referral traffic so it’s great for attracting attention. Pinterest has a special data science lab where its leading data scientists work to accelerate the company’s development. So far, the data science team has created a systematic approach to data science, which gives them trustworthy conclusions that are both reproducible and automatable.
Data Scientist’s salary: US$157,798 Lyft is an online ridesharing provider that offers ride booking, payment processing, and car transport services to customers in the United States. Introduced in 2012, Lyft leverages a friendly, safe, and affordable transportation option that fills empty seats in passenger vehicles already on the road by matching drivers and riders via a smartphone application. Owing to its need for data science professionals, Lyft has so far assembled a team of over 200+ data scientists with a variety of backgrounds, interests, and expertise.
Data Scientist’s salary: US$146,032 Uber is also a transportation company, well-known for its ride-hailing taxi app. The company has since become synonymous with disruptive technology, with the taxi app has swept the world, transforming transportation and giving a different business model, dubbed uberisation. Founded in 2009, the app automatically figures out the navigational route for drivers, calculates the distance and fare, and transfers the payment to the driver from users’ selected payment method. Therefore, data science is an internal part of Uber’s products and philosophy.
Data Scientist’s salary: US$137,668 Walmart is one of the biggest retailers in the world started by Sam Walton. The company sells groceries and general merchandise, operating some 5,400 stores in the US, including about 4,800 Walmart stores and 600 Sam’s Club membership-only warehouses. Through continuous innovation and the implication of technology, the company has created a seamless experience to let its customers shop anytime and anywhere online and offline. Walmart has a broad big data ecosystem that attracts more data scientists into the entity.
Data Scientist’s salary: US$197,500 Nvidia is an artificial intelligence computing company that operates through two segments namely graphics and compute & networking. Nvidia is known as a market leader in the design of graphics processing units, or GPUs, for the gaming market, as well as systems on chips, or SOCs, for the mobile computing and automotive markets. Nvidia works on the motive that accelerated data science can dramatically boost the performance of end-to-end analytics workflows, spending up value generation while reducing cost.
Data Scientist’s salary: US$197,800 Airbnb takes a unique approach towards lodging by providing a shared economy. The platform offers someone’s home as a place to stay instead of a hotel. Airbnb began in 2008 when two designers who had space to share hosted three travelers looking for a place to stay. Today, millions of hosts and travelers choose to create an Airbnb account so they can list their space for rentals. The company is using data science to build new product offerings, improve its services, and capitalize on new marketing initiatives.
Data Scientist’s salary: US$173,503 Netflix is a streaming entertainment service company, which provides subscription services streaming movies and television episodes over the internet and sending DVDs by mail. For millions, Netflix is a de facto place to go for movies and series. Netflix was founded in 1997 by two serial entrepreneurs, Marc Randolph and Reed Hastings. Data science plays an important role in the Netflix routine. With the help of data science, the company gets a more realistic picture of its customers’ taste in form of graphs and charts. It eventually helps the platform’s recommendation service.
Data Scientist’s salary: US$145,172 Dropbox is a cloud storage service company that lets users save files online and sync them to their devices. Dropbox is one of the oldest and most popular cloud storage services that has strongly outperformed Microsoft’s OneDrive and Google Drive. Founded in 2007, the company offers a browser service, toolbars, and apps to upload, share, and sync files to the cloud that can be accessed across several devices.
Data Scientist’s salary: US$129,833
The data science landscape is filled with opportunities spanning diverse industries. As new technologies are being added to the digital sphere year-on-year, the transformation is likely to continue into the coming decade. Owing to the increasing influence of technology in our daily lives, the demand for data science jobs has drastically spiked. The openings for data scientists are expected to go beyond 2023, adding more than 150,000 jobs in the coming years. This trend is a natural response of the digital age for adding more data into its ecosystem. Besides paying high salaries, data science jobs are demanding when it comes to talent requirements and innovation. Data science requires the expertise of professionals, who possess the skill of collecting, structuring, storing, handling and analyzing data, allowing individuals and organizations to make decisions based on insights generated from the data. On a positive note, the nature of data science jobs allows an individual to take on flexible remote works and also to be self-employed. Despite the leniency, the hype for highest paying companies for data scientists remains at the top. In this article, Analytics Insight has listed the top 10 companies that are paying a fortune for data scientists in chúng tôi Scientist’s salary: US$124,333 Oracle is one of the largest vendors in the enterprise IT market and the shorthand name of its flagship product, a relational database management system that’s formally called Oracle Database. In 1979, Oracle became the first company to commercialize an RDBMS platform. The enterprise software company offers a range of cloud-based applications and platforms as well as hardware and services to help companies improve their processes. Oracle recently announced the availability of its cloud data science platform, a native service on Oracle Cloud Infrastructure (OCI).Data Scientist’s salary: US$162,931 Pinterest is a social sharing website where individuals and businesses can ‘pin’ images on ‘boards’ in order to share visual content with friends and followers. Today, many businesses are using interest as a source to enhance their business by promoting content in it. Pinterest creates a lot of online referral traffic so it’s great for attracting attention. Pinterest has a special data science lab where its leading data scientists work to accelerate the company’s development. So far, the data science team has created a systematic approach to data science, which gives them trustworthy conclusions that are both reproducible and chúng tôi Scientist’s salary: US$157,798 Lyft is an online ridesharing provider that offers ride booking, payment processing, and car transport services to customers in the United States. Introduced in 2012, Lyft leverages a friendly, safe, and affordable transportation option that fills empty seats in passenger vehicles already on the road by matching drivers and riders via a smartphone application. Owing to its need for data science professionals, Lyft has so far assembled a team of over 200+ data scientists with a variety of backgrounds, interests, and chúng tôi Scientist’s salary: US$146,032 Uber is also a transportation company, well-known for its ride-hailing taxi app. The company has since become synonymous with disruptive technology, with the taxi app has swept the world, transforming transportation and giving a different business model, dubbed uberisation. Founded in 2009, the app automatically figures out the navigational route for drivers, calculates the distance and fare, and transfers the payment to the driver from users’ selected payment method. Therefore, data science is an internal part of Uber’s products and chúng tôi Scientist’s salary: US$137,668 Walmart is one of the biggest retailers in the world started by Sam Walton. The company sells groceries and general merchandise, operating some 5,400 stores in the US, including about 4,800 Walmart stores and 600 Sam’s Club membership-only warehouses. Through continuous innovation and the implication of technology, the company has created a seamless experience to let its customers shop anytime and anywhere online and offline. Walmart has a broad big data ecosystem that attracts more data scientists into the chúng tôi Scientist’s salary: US$197,500 Nvidia is an artificial intelligence computing company that operates through two segments namely graphics and compute & networking. Nvidia is known as a market leader in the design of graphics processing units, or GPUs, for the gaming market, as well as systems on chips, or SOCs, for the mobile computing and automotive markets. Nvidia works on the motive that accelerated data science can dramatically boost the performance of end-to-end analytics workflows, spending up value generation while reducing chúng tôi Scientist’s salary: US$197,800 Airbnb takes a unique approach towards lodging by providing a shared economy. The platform offers someone’s home as a place to stay instead of a hotel. Airbnb began in 2008 when two designers who had space to share hosted three travelers looking for a place to stay. Today, millions of hosts and travelers choose to create an Airbnb account so they can list their space for rentals. The company is using data science to build new product offerings, improve its services, and capitalize on new marketing chúng tôi Scientist’s salary: US$173,503 Netflix is a streaming entertainment service company, which provides subscription services streaming movies and television episodes over the internet and sending DVDs by mail. For millions, Netflix is a de facto place to go for movies and series. Netflix was founded in 1997 by two serial entrepreneurs, Marc Randolph and Reed Hastings. Data science plays an important role in the Netflix routine. With the help of data science, the company gets a more realistic picture of its customers’ taste in form of graphs and charts. It eventually helps the platform’s recommendation chúng tôi Scientist’s salary: US$145,172 Dropbox is a cloud storage service company that lets users save files online and sync them to their devices. Dropbox is one of the oldest and most popular cloud storage services that has strongly outperformed Microsoft’s OneDrive and Google Drive. Founded in 2007, the company offers a browser service, toolbars, and apps to upload, share, and sync files to the cloud that can be accessed across several chúng tôi Scientist’s salary: US$129,833 Genentech is a biotechnology company that discovers, develops, manufactures, and commercializes medicines to treat patients. The company offers medicine for the prevention of oncology, immunology, metabolism, monoclonal antibodies, small molecules, tissue repair, and virology, as well as conducts scientific research to produce biologic medicines. The company uses its data science capabilities to enhance its performance in the market by unraveling effective medicines.
Analytics vendors and non-technical employees are democratizing data science. Organizations are looking at converting non-technical employees into data scientists so that they can combine their domain expertise with data science technology to solve business problems.What does citizen data scientist mean?
In short, they are non-technical employees who can use data science tools to solve business problems.
Citizen data scientists can provide business and industry domain expertise that many data science experts lack. Their business experience and awareness of business priorities enable them to effectively integrate data science and machine learning output into business processes.Why are citizen data scientists important now?
Interest in citizen data science is almost tripled between 2012-2023, as seen below.
Reasons for this growing interest are:
Though there is an increasing need for analytics due to increased popularity of data-driven decision making, data science talent is in short supply. As of 2023, there are three times more data science job postings than job searches.
As with any short supply product in the market, data science talent is expensive. According to the U.S. Bureau of Labor Statistics, the average data science salary is $101k.
Analytics tools are easier-to-use now, which reduces the reliance on data scientists.
Most industry analysts are also highlighting the increased role of citizen data scientists in organizations:
IDC big data analytics and AI research director Chwee Kan Chua mentions in an interview: “Lowering the barriers to allow even non-technical business users to be ‘data scientists’ is a great approach.”
Gartner defined the term and is heavily promoting it
Various solutions help businesses to democratize AI and analytics:
Citizen data scientists first need to understand business data and access it from various systems. Metadata management solutions like data catalogs or self-service data reporting tools can help citizen data scientists with this.
Automated Machine Learning (AutoML): AutoML solutions can automate manual and repetitive machine learning tasks to empower citizen data scientists. ML tasks AutoML tools can automate are
Algorithm selection & hyperparameter optimization
Augmented analytics /AI-driven analytics: ML-led analytics, where tools extract insights from data in two forms:
Search-driven: Software returns with results in various formats (reports, dashboards, etc.) to answer citizen data scientists’ queries.
Auto-generated: ML algorithms identify patterns to automate insight generation.
No/low-code and RPA solutions minimize coding with drag-and-drop interfaces which helps citizen developers place the models they prepare in production.
BotX’s no-code AI platform can empower citizen data scientists to build solutions faster while reducing development costs. BotX solutions allow developers and data scientists to launch apps and set infrastructure and IT systems through:What are best practices for citizen data science projects? Create a workspace where citizen data scientists and data science experts can work collaboratively
Most citizen data scientists are not trained in the foundations of data science. They rely on tools to generate reports, analyze data, create dashboards or models. To maximize citizen data scientists’ value, you should have teams that can support them which also includes data engineers and expert data scientists.Train citizen data scientists
use of BI/autoML tools for maximum efficiency
data security training to maintain data compliance
detecting AI biases and creating standards for model trust and transparency so that citizen data scientists can establish explainable AI (XAI) systems.Classify datasets based on accessibility
Due to data compliance issues, all data types should not be accessible to all employees. Classifying data sets that require limited access can help overcome this issue.Create a sandbox for testing
Sandboxes, software testing environment, which include synthetic data and which are not connected to production environments help citizen data scientists quickly test their models before rolling them to production.
If you still have questions on citizen data science, don’t hesitate to contact us:
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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This article was published as a part of the Data Science Blogathon.Introduction
In machine learning, the data’s mount and quality are necessary to model training and performance. The amount of data affects machine learning and deep learning algorithms a lot. Most of the algorithm’s behaviors change if the amount of data is increased or decreased. But in the case of limited data, it is necessary to effectively handle machine learning algorithms to get better results and accurate models. Deep learning algorithms are also data-hungry, requiring a large amount of data for better accuracy.
In this article, we will discuss the relationship between the amount and the quality of the data with machine learning and deep learning algorithms, the problem with limited data, and the accuracy of dealing with it. Knowledge about these key concepts will help one understand the algorithm vs. data scenario and will shape one so that one can deal with limited data efficiently.The “Amount of Data vs. Performace” Graph
In machine learning, a query could be raised to your mind, how strictly is the data required to train a good machine learning or deep learning model? Well, there is no threshold levels or fixed answer to this, as every piece of information is different and has different features and patterns. Still, there are some threshold levels after which the performance of the machine learning or deep learning algorithms tends to be constant.
Most of the time, machine learning and deep learning models tend to perform well as the amount of data fed is increased, but after some point or some amount of data, the behavior of the models becomes constant, and it stops learning from data.
The above pictures show the performance of some famous machine learning and deep learning architectures with the amount of data fed to the algorithms. Here we can see that the traditional machine learning algorithms learn a lot from the data in a preliminary period, where the amount of data fed is increasing, but after some time, when a threshold level comes, the performance becomes constant. Now, if you provide more data to the algorithm, it will not learn anything, and the version will not increase or decrease.
In the case of deep learning algorithms, there are a total of three types of deep learning architectures in the diagram. The shallow ty[e of deep learning stricture is a minor deep learning architecture in terms of depth, meaning that there are few hidden layers and neurons in external deep learning architectures. In the case o deep neural networks, the number of hidden layers and neurons is very high and designed very profoundly.
From the diagram, we can see a total of three deep learning architectures, and all three perform differently when some amount of data is fed and increased. The shallow, deep neural networks tend to function like traditional machine learning algorithms, where the performance becomes constant after some threshold amount of data. At the same time, deep neural networks keep learning from the data when new data is fed.
From the diagram, we can conclude that,
” THE DEEP NEURAL NETWORKS ARE DATA HUNGRY “What Problems Arise with Limited Data?
Several problems occur with limited data, and the model could perform better if trained with limited data. The common issues that arise with limited data are listed below:
In classification, if a low amount of data is fed, then the model will classify the observations wrongly, meaning that it will not give the accurate output class for given words.
In a regression problem, if the model’s accuracy is low, then the model will predict very wrong, meaning that as it is a regression problem, it will be expecting the number. Still, limited data may show a horrifying amount far from the actual output.
The model can classify the different points in the wrong clusters in the clustering problems if trained with limited data.
4. Time Series:
In time series analysis, we forecast some data for the future. Still, a low-accurate time series model can give us inferior forecast results, and there may be a lot of errors related to time.
5. Object Detection:
If an object detection model is trained on limited data, it might not detect the object correctly, or it can classify the thing incorrectly.How to Deal With Problems of Limited Data?
There needs to be an accurate or fixed method for dealing with the limited data. Every machine learning problem is different, and the way of solving the particular problem is other. But some standard techniques are helpful for many cases.
1. Data Augmentation
Data augmentation is the technique in which the existing data is used to generate new data. Here the further information generated will look like the old data, but some of the values and parameters would be different here.
This approach can increase the amount of data, and there is a high likelihood of improving the model’s performance.
Data augmentation is preferred in most deep-learning problems, where there is limited data with images.
2. Don’t Drop and Impute:
In some of the datasets, there is a high fraction of invalid data or empty. Due to that, some amount of data s dropped not to make the process complex, but by doing this, the amount of data is decreased, and several problems can occur.
3. Custom Approach:
If there is a case of limited data, one could search for the data on the internet and find similar data. Once this type of data is obtained, it can be used to generate more data or be merged with the existing data.Conclusion
In this article, we discussed the limited data, the performance of several machine learning and deep learning algorithms, the amount of data increasing and decreasing, the type of problem that can occur due to limited data, and the common ways to deal with limited data. This article will help one understand the process of restricted data, its effects on performance, and how to handle it.
Some Key Takeaways from this article are:
1. Machine Learning and shallow neural networks are the algorithms that are not affected by the amount of data after some threshold level.
2. Deep neural networks are data-hungry algorithms that never stop learning from data.
3. Limited data can cause problems in every field of machine learning applications, e.g., classification, regression, time series, image processing, etc.
4. We can apply Data augmentation, imputation, and some other custom approaches based on domain knowledge to handle the limited data.
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Introduction to Data Preprocessing in Machine Learning
The following article provides an outline for Data Preprocessing in Machine Learning. Data pre-processing also knows as data wrangling is the technique of transforming the raw data i.e. an incomplete, inconsistent, data with lots of error, and data that lack certain behavior, into understandable format carefully using the different steps (i.e. from importing libraries, data to checking of missing values, categorical followed by validation and feature scaling ) so that proper interpretations can be made from it and negative results can be avoided, as the quality of the model in machine learning highly depends upon the quality of data we train it on.
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Data collected for training the model is from various sources. These collected data are generally in their raw format i.e. they can have noises like missing values, and relevant information, numbers in the string format, etc. or they can be unstructured. Data pre-processing increases the efficiency and accuracy of the machine learning models. As it helps in removing these noises from and dataset and giving meaning to the datasetSix Different Steps Involved in Machine Learning
Following are six different steps involved in machine learning to perform data pre-processing:
Step 1: Import libraries
Step 2: Import data
Step 3: Checking for missing values
Step 4: Checking for categorical data
Step 5: Feature scaling1. Import Libraries
The very first step is to import a few of the important libraries required in data pre-processing. A library is a collection of modules that can be called and used. In python, we have a lot of libraries that are helpful in data pre-processing.
A few of the following important libraries in python are:
Numpy: Mostly used the library for implementing or using complicated mathematical computation of machine learning. It is useful in performing an operation on multidimensional arrays.
Pandas: It is an open-source library that provides high performance, and easy-to-use data structure and data analysis tools in python. It is designed in a way to make working with relation and labeled data easy and intuitive.
Matplotlib: It’s a visualization library provided by python for 2D plots o array. It is built on a numpy array and designed to work with a broader Scipy stack. Visualization of datasets is helpful in the scenario where large data is available. Plots available in matplot lib are line, bar, scatter, histogram, etc.
Seaborn: It is also a visualization library given by python. It provides a high-level interface for drawing attractive and informative statistical graphs.2. Import Dataset
Once the libraries are imported, our next step is to load the collected data. Pandas library is used to import these datasets. Mostly the datasets are available in CSV formats as they are low in size which makes it fast for processing. So, to load a csv file using the read_csv function of the panda’s library. Various other formats of the dataset that can be seen are
Once the dataset is loaded, we have to inspect it and look for any noise. To do so we have to create a feature matrix X and an observation vector Y with respect to X.3. Checking for Missing Values
Once you create the feature matrix you might find there are some missing values. If we won’t handle it then it may cause a problem at the time of training.
Removing the entire row that contains the missing value, but there can be a possibility that you may end up losing some vital information. This can be a good approach if the size of the dataset is large.
If a numerical column has a missing value then you can estimate the value by taking the mean, median, mode, etc.4. Checking for Categorical Data
Data in the dataset has to be in a numerical form so as to perform computation on it. Since Machine learning models contain complex mathematical computation, we can’t feed them a non-numerical value. So, it is important to convert all the text values into numerical values. LabelEncoder() class of learned is used to covert these categorical values into numerical values.5. Feature Scaling
The values of the raw data vary extremely and it may result in biased training of the model or may end up increasing the computational cost. So it is important to normalize them. Feature scaling is a technique that is used to bring the data value in a shorter range.
Methods used for feature scaling are:
Rescaling (min-max normalization)
Standardization (Z-score Normalization)
Scaling to unit length6. Splitting Data into Training, Validation and Evaluation Sets
Finally, we need to split our data into three different sets, training set to train the model, validation set to validate the accuracy of our model and finally test set to test the performance of our model on generic data. Before splitting the Dataset, it is important to shuffle the Dataset to avoid any biases. An ideal proportion to divide the Dataset is 60:20:20 i.e. 60% as the training set, 20% as test and validation set. To split the Dataset use train_test_split of sklearn.model_selection twice. Once to split the dataset into train and validation set and then to split the remaining train dataset into train and test set.Conclusion – Data Preprocessing in Machine Learning
Data Preprocessing is something that requires practice. It is not like a simple data structure in which you learn and apply directly to solve a problem. To get good knowledge on how to clean a Dataset or how to visualize your dataset, you need to work with different datasets. The more you will use these techniques the better understanding you will get about it. This was a general idea of how data processing plays an important role in machine learning. Along with that, we have also seen the steps needed for data pre-processing. So next time before going to train the model using the collected data be sure to apply data pre-processing.Recommended Articles
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