Trending November 2023 # What Role Does Machine Learning Play In Biotechnology? # Suggested December 2023 # Top 14 Popular

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ML is changing biological research. This has led to new discoveries in biotechnology and healthcare.

Machine Learning and Artificial Intelligence are changing the way that people live and work. These fields have been praised and criticized. AI and ML, or as they are commonly known, have many applications and benefits across a wide variety of industries. They are changing biological research and resulting in new discoveries in biotechnology and healthcare.

What are the Applications of Machine Learning in Biotechnology?

Here are some use cases of ML in biotech:

Identifying Gene Coding Regions

Next-generation sequencing is a fast and efficient way to study genomics. The machine-learning approach to discovering gene coding regions in a genome is now being used. These machine-learning-based gene prediction techniques are more sensitive than traditional sequence analysis based on homology.

Structure Prediction

PPI has been mentioned in the context of proteomics before. However, ML has improved structure prediction accuracy by more than 70% to over 80%. Text mining has great potential. Training sets can be used to identify new or unusual pharmacological targets using many journals articles and secondary databases.

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Neural Networks

Deep learning, an extension of neural networks, is a relatively recent topic in ML. Deep learning refers to the number of layers that data can be changed. Deep learning is therefore analogous to a multilayer neural structure. Multi-layer nodes simulate the brain’s workings to help solve problems. ML already uses neural networks. Neural network-based ML algorithms need to be able to analyze the raw data. It is becoming more difficult to analyze significant data due to the increasing amount of information generated by genome sequencing. Multiple layers of neural networks filter information and interact with one another, which allows for refined output.

Mental Illness

AI in Healthcare

Final Thoughts

Every business sector and industry has been affected by digitization. These effects aren’t limited to the biotech, healthcare, and biology industries. Companies are looking for a way to combine their operations and allow them to exchange and transmit data more efficiently, faster, and in a more efficient manner. Bioinformatics and biomedicine have struggled for years with processing biological data.

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What Machine Learning Is Rocket Science?

If you have been interested in machine learning, this guide is a fantastic place to begin researching it. Aside from introducing readers to the fundamentals, in addition, it motivates you to find out from pointing you in the path of different online libraries and courses. Rapid improvements in this field have surely driven people to feel this will induce innovation for at least a couple of years.

There are some extraordinary improvements in AI which have led many to think it’s going to be the technology which will form our future.

1. As stated by many:  Move is regarded as the most complicated professional sport due to a massive number of possible moves which may be made.

2. AI predicted US election outcomes: Many people were amazed by the results of this US presidential election outcomes, however, a startup named MogIA established in Mumbai managed to forecast it a month before the results had been announced. The organization analysed social networking opinion through countless social networking data points. This was the company’s fourth successful forecast in a row.

3. AI enhances cancer diagnosis: There are some path-breaking innovations within the business of healthcare. It’s thought that the healthcare market will benefit the most from AI.

You will find Artificial intelligence app that may now predict the incidence of cancer using 90 percent accuracy by simply analysing the signs of a patient, which can assist a physician to begin treatment early. However, these aren’t the very same things. It’s been shown that computers could be programmed to execute quite complex tasks which were previously only performed by people. It’s regarded as one the most prosperous methods to AI, however, is only one strategy. As an instance, there are lots of chatbots which are principle-based, i.e., they could reply only certain queries, based on the way they were programmed.

However, they won’t be able to find out anything new from these queries. So this is sometimes categorized as AI since the discussion bots replicate human behaviour, but cannot be termed as machine learning. The question is: Can machines actually ‘know’? How can it be possible to get a system to find out if it does not have a mind and an intricate nervous system like individuals? In accordance with Arthur Samuel, “Machine learning could be described as a subject of research that provides computers the ability to master without being explicitly programmed.”

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We could even specify it as the computer’s capacity to learn from experience to execute a particular job, whereby the operation will improve with experience. This is comparable to a computer software playing chess, which is often abbreviated as machine learning, even in case it learns from prior experiences and then makes better motions to win a match. It utilizes neural networks to mimic human decision-making abilities. A neural network is made up of neurons and hence looks like a human nervous system. Have you ever thought about how Facebook finds your head amongst many, in a picture? Picture detection is among those cases of profound learning, which is quite a bit more complicated since it requires tons of information to train. As an example, a profound learning algorithm may learn how to recognise a vehicle but might need to be educated on a massive data set which is composed of automobiles in addition to some other objects. If that isn’t done, it may make a wrong choice like identifying a bus for a vehicle. Hence, in contrast to other machine learning algorithms, a profound learning algorithm requires more information so as to detect and understand every minute detail to make the proper decisions.

Now you have recognized the differences between artificial intelligence, machine learning and profound learning, let us dig deeper in machine learning.

There are 3 chief kinds of machine learning algorithms.

1. Supervised learning: The information collection in supervised learning is made up of input information in addition to the anticipated output. The plan is a function that maps this input to the anticipated result. Then this model may be applied to fresh sets of information, for which the anticipated outcome isn’t available but has to be called from a given set of information.

For better results, the business may use a data collection of automobile models of different manufacturers and their costs. This would assist the organization in establishing a competitive cost.

In machine learning, the top results aren’t attained using a fantastic algorithm but using the maximum data.

2. Unsupervised learning: The sole difference between supervised and unsupervised learning is the information collection does not have the anticipated outcome as from the supervised learning version. The data collection will just have input (or attributes) and also the algorithm is going to need to forecast the results. As an example, if a top manufacturing firm is seeking to fabricate three distinct forms of shirts (small, medium and big ), its own information includes the shoulder, waist and torso dimensions of its clients. Now, based upon this massive data collection, the business should set the dimensions into three classes so that there could be the best match for everybody. Here unsupervised learning tool may be used to set different information points in three distinct sizes and forecast a suitable top size for every single client.

In accordance with the chart given in Figure 2, let us consider a business which has just the shoulder and waist measurements as the input of this data collection. It is going to finally have to categorize this data collection into three classes, which can enable the business forecast the top size for every single client. This technique is referred to as clustering, where the information set is clustered to the desired variety of clusters. The majority of the time, the information collection isn’t just like the one displayed in this case. Data points which are extremely near each other make it tricky to implement clustering. Additionally, clustering is simply one of many techniques used in learning to forecast the results.

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3. Reinforcement learning: In reinforcement learning, a system or a broker trains itself when subjected to specific surroundings, with a process of trial and error. Let us think about a child who wants to learn how to ride a bike. To begin with, she’ll attempt to learn from a person who already knows how to ride a bike. Afterwards, she’ll try out riding her own and may fall down lots of occasions. Learning from her prior mistakes, she’ll attempt to ride without decreasing.

When she eventually rides the bicycle without decreasing, it could be regarded as a reward for her efforts. Now let us think about this child for a machine or a broker who’s getting punished (decreasing) for committing an error and making a reward (not decreasing) for not committing any error.

A chess-playing program may be a fantastic illustration of this, where one wrong move will penalize the broker and it might eliminate a match, even though a mix of one or more appropriate moves will make it a reward by creating it triumph. In accordance with the requirement, these versions may be utilised in combination to yield a new version. For example, supervised learning can at times be used alongside unsupervised learning, determined by the data collection in addition to the anticipated result.

People frequently believe machine learning is simply for somebody who’s great with math or numbers, and will not be possible to learn for anybody. Machine learning isn’t rocket science after all. The one thing that’s required to learn it’s eagerness and curiosity. The amount of libraries and tools available is now easier to learn it. Google’s TensorFlow library, that is now open source, or even the many Python libraries such as NumPy and scikit-learn, are only a couple of them. Everyone can make use of these libraries and also bring about them to address issues since they are open source. You do not need to be concerned about the intricacies involved with your algorithm, such as complicated mathematical computations (like gradient, matrix multiplication, etc) because this task could be abandoned for all these libraries to execute. Libraries make it a lot easier for everybody so that rather than becoming involved in executing complicated computations, the consumer is now able to concentrate on the use of this algorithm.

In addition, there are many APIs available which may be utilized to execute an artificial development app. Watson is really capable of performing many tasks such as answering a user’s concerns, helping physicians to identify diseases, and far more.

If you’re excited about the prospects that machine learning provides, our electronic schooling era has made matters simpler for you. There are lots of massive open online classes (MOOC) provided by many businesses. 1 such class is supplied by Coursera-Machine Learning. That can be taught by Andrew Ng, among those co-founders of all Coursera. This class will provide you a simple comprehension of the algorithms which are employed in machine learning, and it comprises both supervised learning and unsupervised learning. It is a self-paced class but designed to be completed within 12 weeks. If you would like to dig deeper and research profound learning, which will be a subset of machine learning, then you can learn it via a different course supplied by chúng tôi This training course is divided into two components: Practical profound learning to get coders (Component 1) and Cutting border deep learning to get coders (Component 2). Both are designed for seven months each and supply you with a fantastic insight into profound learning. If you want to concentrate in profound learning, then you can elect for a profound learning specialisation class by Coursera and chúng tôi So, for one to practice, there are lots of resources that may supply you a massive data collection to check your own expertise and execute what you’ve learned. 1 such site is Kaggle, which offers a varied data set and will be able to assist you to conquer your important obstacle, i.e., obtaining information to check your learning version.

In the event that you sometimes feel lost in this journey of learning, as soon as your algorithm doesn’t function as anticipated or when you do not know an intricate equation, don’t forget the famous dialogue from the film, The Pursuit of Happiness:”Do not ever let someone tell you you can not do something. I. You have a fantasy; you have ta shield it. When folks cannot do anything, they are gonna inform you you can not do it.”

Hyperparameters In Machine Learning Explained

To improve the learning model of machine learning, there are various concepts given in machine learning. Hyperparameters are one of such important concepts that are used to improve the learning model. They are generally classified as model hyperparameters that are not included while setting or fitting the machine to the training set because they refer to the model selection task. In deep learning and machine learning, hyperparameters are the variables that you need to apply or set before the application of a learning algorithm to a dataset.

What are Hyperparameters?

Hyperparameters are those parameters that are specifically defined by the user to improve the learning model and control the process of training the machine. They are explicitly used in machine learning so that their values are set before applying the learning process of the model. This simply means that the values cannot be changed during the training of machine learning. Hyperparameters make it easy for the learning process to control the overfitting of the training set. Hyperparameters provide the best or optimal way to control the learning process.

Hyperparameters are externally applied to the training process and their values cannot be changed during the process. Most of the time, people get confused between parameters and hyperparameters used in the learning process. But parameters and hyperparameters are different in various aspects. Let us have a brief look over the differences between parameters and hyperparameters in the below section.

Parameters Vs Hyperparameters

These are generally misunderstood terms by users. But hyperparameters and parameters are very different from each other. You will get to know these differences as below −

Model parameters are the variables that are learned from the training data by the model itself. On the other hand, hyperparameters are set by the user before training the model.

The values of model parameters are learned during the process whereas, the values of hyperparameters cannot be learned or changed during the learning process.

Model parameters, as the name suggests, have a fixed number of parameters, and hyperparameters are not part of the trained model so the values of hyperparameters are not saved.

Classification of Hyperparameters

Hyperparameters are broadly classified into two categories. They are explained below −

Hyperparameter for Optimization

The hyperparameters that are used for the enhancement of the learning model are known as hyperparameters for optimization. The most important optimization hyperparameters are given below −

Learning Rate − The learning rate hyperparameter decides how it overrides the previously available data in the dataset. If the learning rate hyperparameter has a high value of optimization, then the learning model will be unable to optimize properly and this will lead to the possibility that the hyperparameter will skip over minima. Alternatively, if the learning rate hyperparameter has a very low value of optimization, then the convergence will also be very slow which may raise problems in determining the cross-checking of the learning model.

Batch Size − The optimization of a learning model depends upon different hyperparameters. Batch size is one of those hyperparameters. The speed of the learning process can be enhanced using the batch method. This method involves speeding up the learning process of the dataset by dividing the hyperparameters into different batches. To adjust the values of all the hyperparameters, the batch method is acquired. In this method, the training model follows the procedure of making small batches, training them, and evaluating to adjust the different values of all the hyperparameters. Batch size affects many factors like memory, time, etc. If you increase the size of the batch, then more learning time will be needed and more memory will also be required to process the calculation. In the same manner, the smaller size of the batch will lower the performance of hyperparameters and it will lead to more noise in the error calculation.

Number of Epochs − An epoch in machine learning is a type of hyperparameter that specifies one complete cycle of training data. The epoch number is a major hyperparameter for the training of the data. An epoch number is always an integer value that is represented after every cycle. An epoch plays a major role in the learning process where repetition of trial and error procedure is required. Validation errors can be controlled by increasing the number of epochs. Epoch is also named as an early stopping hyperparameter.

Hyperparameter for Specific Models

Number of Hidden Units − There are various neural networks hidden in deep learning models. These neural networks must be defined to know the learning capacity of the model. The hyperparameter used to find the number of these neural networks is known as the number of hidden units. The number of hidden units is defined for critical functions and it should not overfit the learning model.

Number of Layers − Hyperparameters that use more layers can give better performance than that of less number of layers. It helps in performance enhancement as it makes the training model more reliable and error-free.


Hyperparameters are those parameters that are externally defined by machine learning engineers to improve the learning model.

Hyperparameters control the process of training the machine.

Parameters and hyperparameters are terms that sound similar but they differ in nature and performance completely.

Parameters are the variables that can be changed during the learning process but hyperparameters are externally applied to the training process and their values cannot be changed during the process.

There are various methods categorized in different types of hyperparameters that enhance the performance of the learning model and also make error-free learning models.

What Is A Cio And Why Does This Role Matter?

blog / Senior Executive Programs What is a CIO and Why Does This Role Matter?

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As one of the high-ranking executive members within a company, the Chief Information Officer (CIO) oversees the company’s entire computer and information technology (IT) systems. To understand why this is such a key role, here is a more detailed understanding on what is a CIO, their responsibilities, and the skills and qualifications required to get this job.

What is a CIO?

A CIO oversees the management and implementation of information and computer technologies to deliver desired business outcomes. In companies where technology is core to the business, this position is crucial for driving strategic, technical, and management initiatives to achieve business growth. They do this by leveraging technology on the one hand, and mitigating the risks associated with using technology, on the other.

Evolution of the CIO’s Role What Does a CIO Do?

Now that we’ve answered what is a CIO, let’s look at what they do. They are responsible for implementing the right information technology and computer systems in an organization. Some of the day-to-day tasks include:

Approving the purchase of IT equipment

Managing the IT department and its team members

Overseeing network and system implementations 

Vendor management to optimize for factors such as costs

Staying abreast with the latest IT trends and technologies

Strategizing to create solutions that serve business needs

Coordinating with other executives to determine best practices

How Important is a CIO?

In an increasingly technology-driven world, this c-suite position has to take up strategic responsibilities like other C-suite executives to drive the company’s vision. Also, as companies are increasingly dependent on technology, downtime costs are worsening, making the role of a CIO even more critical. 

What are the Key Differences Between a CIO and IT Director?

Many times, people wonder what is a CIO and do they fulfill the same role as an IT Director. A CIO focuses on an organization’s technology needs on a strategic level, wherein they formulate the required policies and processes to maintain the proper IT infrastructure. IT Directors, on the other hand, focus on supervising the daily operations of the company’s computer network and identifying areas of improvement. Typically, IT Directors report to CIOs.

What are the Key Differences Between a CIO and CTO?

Though the CIO and the Chief Technology Officer (CTO) are in executive positions, they have different daily responsibilities and areas of focus. While a CIO oversees all the IT operations, a CTO has to manage the vertical consisting of engineers, product developers, and designers who create software and applications for the company’s customers or external stakeholders. In short, CIOs focus on internal stakeholders, while CTOs focus on external stakeholders and deliverables.

What are the Key Differences Between a CIO and CISO? What are the Qualifications for Becoming a CIO?

Till a few years back, a background in computer science or a related field and a decade or more relevant experience were sufficient for this position. However, in current times, the qualifications are more complex. Experience in areas such as project management and information technology governance can help you stand out in the highly competitive pool of candidates contending for the position. There are also some specialized CIO courses tailor-made for this C-suite position. 

How to Gain the Skills to Become a CIO

Below are some of the most commonly found skills of a CIO. You can also read more about how to become a CIO in our related post. 

1. Ability to Transform

Technology implementation can be a challenge and affect the behavior of both internal and external stakeholders. For this role, companies tend to want a candidate with experience in transforming the organization and its processes via technology. Focus on driving such changes in your current role to help you prepare for your next role as a CIO.

2. Business Acumen

A CIO must have the business acumen to ensure that the technology changes made by the company have the requisite business impact. This includes anticipating and tracking changes in customer behavior, increasing sales, or even enhancing company data security after the technology implementation. In your current role, show a keen interest in your work’s impact on the business.

3. Strategic Thinking

CIOs need to bring the company’s vision and business plans to life through the strategic implementation of technology solutions. Therefore, anyone getting interviewed for the CIO position will be tested on their strategy for implementing specific technology to bring about business outcomes such as reduction in cost, increase in sales, and so on.

4. Driving Project Efficiencies

In addition to implementing new projects, a CIO needs to have the skill of resuscitating past projects, such that they can have better outcomes. Learn how to improve the efficiency of various projects to show your competence in this area.

5. Vendor Management

Traditionally, vendor management has been more about optimizing for cost efficiencies and Service Level Agreements (SLAs). But with the rapid technological evolution, a CIO needs to work with new vendors that can provide the latest technology solutions at affordable prices. So start building a vendor network that can help your role as a CIO.

6. Affinity for Data

Data is the new currency, and businesses leveraging the power of data can evolve much faster. While all technology tools come with dashboards and reports, a CIO must guide the team on implementing these correctly to drive better business outcomes.

What Makes a Great CIO?

Up-to-date knowledge and relevant skills

Ability to communicate with various stakeholders

Leadership qualities

Data-driven decision-making

Problem-solving ability

Having understood ‘what is a CIO’, if you want to take the next step in your career and enter the executive circle in this role, you need to hone your leadership ability. A great place to start is through the Chief Information Officer (CIO) Program at NYU Tandon School of Engineering which is designed by world-class faculty using the latest industry research and insights.

By Priya S

Write to us at [email protected]

Edtech’s Evolving Role In Corporate Learning & Development

blog / General EdTech’s evolving role in Corporate Learning & Development

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The evolution of the professional education industry has been quite dramatic over the past decade, and I can truly testify to its considerable shift since its early days. Over these years, I have had the privilege to gain invaluable insight into the sector, and have had the chance to derive enriching experiences from serving Fortune 500 companies, government sector clients and organizations across the globe.

Succinctly put, there have been two major shifts in EdTech. The first being the evolution of the different formats of professional education, from mainly being classroom-based, to now being blended with online and classroom-based learning, and enhanced with experiential and simulation-based set-ups. The second being, the increased acceptance of these new formats by organizations and employees. Now, organizations are looking to do a lot more learning online or in blended formats, and have even questioned the need to employ standalone classroom-based learning. About 8-10 years ago, there were MOOCs (Massive Open Online Courses) that came about – these were mostly an initial attempt at online learning by most schools or organizations in a self-paced format. The world of online learning has significantly evolved more recently, there has been a shift to SPOC (Small Private Online Courses) learning, focused on blending live learning, recorded modules, application based assignments, simulations and smaller cohorts resulting in a deeper impact with higher completion (85% or higher) rates and a better return on investment. According to a recent research report by Emeritus, majority of professionals (over 80%) – ranging from age 21-65 across the globe believe that online learning adoption will increase in the near term. Over 77% of respondents across nine countries stated they would consider a fully online or hybrid approach to learning.

The draw of EdTech

The education industry has always held a significant amount of attraction and interest for me, and I think my four years at the Eruditus group has only further strengthened this view. I also largely attribute my choosing of the Eruditus group based on the goal of making higher-education accessible and affordable across the globe. The emergence of these two organizations was of great consequence, as before this, if you had to undertake higher-education, it would entail taking a break from your career for a year or two, and going headlong into a full-time program at a higher-education institution. However, through our university partners, and the programs developed, both in the blended formats and the online formats, we provide participants with unprecedented access, and an alternative that’s free of compromises. In our State of Executive Education 2023 survey conducted, over 74% senior executives reported having seen a positive impact as a result of executive education.  

The way forward for EdTech

The hastened changes, as necessitated by COVID-19, have ensured that the world has had to resort to online or remote learning in the primary, secondary, tertiary and professional education sectors. Previously, while there was a hesitancy to fully pivot to online-learning, there is now an increased need to re-think and re-imagine the curriculum to suit this medium across all levels of education. In a recent article, Ashley Chiampo and I shared our views on how organizations can determine ways of moving their face to face workforce learning online.

The next few months are going to be crucial as well, as organizations grapple with the new normal and the implications thereof. I think this is where we could step in and help organizations address their talent transformation needs. Provide them insights, and introduce them to a series of knowledge interventions from leading universities & educators across topics of innovation, digitization and data-analytics, as well as design programs that best equip their teams to bring about a smoother transition to the new normal of their choosing. 

Compliments and challenges

A good day at work for me would be one where I hear from our corporate clients on the impact we’ve made on their lives and careers, and the tangible benefit to their organizations. Typically, a good day is also one where I get to spend time with clients, listening to them, understanding their particular needs and thereby helping them design and develop effective programs. On the other hand, a challenging day at work is characterized by a client approaching us with something unusual or ‘not standard’. This is what we enjoy & what keeps us driven & motivated – designing solutions to transform their teams and deliver impact across their organization.

The impetus to innovate

There has hardly been a better time to be innovative in EdTech than the present, as the pandemic has prompted us to push the innovation button more than ever. In keeping with this, we developed and launched a series of short online learning sessions with our educator network that involved renowned and expert speakers from academic and practitioner backgrounds from across the globe. These sessions, named Emeritus Knowledge Bytes, delivered informative and valuable discussions and debates on interesting topics that enabled us to keep our employees and clients motivated and engaged over the last few months.

Vision for 2030

While it’s difficult to predict the future, if I may take a stab at it, I see our companies being the largest education enablers and the largest education platform in the world by 2030, as we continue to make professional education universally accessible. As such, there has never been a better time to be a part of the Eruditus family, and the future looks bright indeed!

This article first appeared on LinkedIn Pulse

Dealing With Sparse Datasets In Machine Learning

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


Missing data in machine learning is a type of data that contains null values, whereas Sparse data is a type of data that does not contain the actual values of sing data.

Sparse datasets with high zero values can cause problems like over-fitting in the machine learning models and several other problems. That is why dealin arse data is one of the most hectic processes in machine learning.

Most of the time, sparsity in the dataset is not a good fit for the machine learning problems in it should be handled properly. Still, sparsity in the dataset is good in some cases as it reduces the memory footprint of regular networks to fit mobile devices and shortens training time for ever-growing networks in deep learning. 

In the above Image, we can see the dataset with a high amount of zeros, meaning that the dataset is sparse. Most of the time, while working with a one-hot encoder, this type of sparsity is observed due to the working principle of the one-hot encoder.

The Need For Sparse Data


Several problems with the sparse datasets cause problems while training machine learning models. Due to the problem associated with sparse data, it should be handled properly.

A common problem with sparse data is:

1. Over-fitting: 

if there are too many features included in the training data, then while training a model, the model with tend to follow every step of the training data, results in higher accuracy in training data and lower performance in the testing dataset.

In the above image, we can see that the model is over-fitted on the training data and tries to follow or mimic every trend of the training data. This will result in lower performance of the model on testing or unknown data.

2. Avoiding Important Data:

Some machine-learning algorithms avoid the importance of sparse data and only tend to train and fit on the dense dataset. They do not tend to fit on sparse datasets.

The avoided sparse data can also have some training power and useful information, which the algorithm neglects. So it is not always a better approach to deal with sparse datasets.

3. Space Complexity 

If the dataset has a sparse feature, it will take more space to store than dense data; hence, the space complexity will increase. Due to this, higher computational power will be needed to work with this type of data.

4. Time Complexity

If the dataset is sparse, then training the model will take more time to train compared to the dense dataset on the data as the size of the dataset is also higher than the dense dataset.

5. Change in Behavior of the algorithms

Some of the algorithms might perform badly or low on sparse datasets. Some algorithms tend to perform badly while training them on sparse datasets. Logistic Regression is one of the algorithms which shows flawed behavior in the best fit line while training it on a space dataset.

Ways to Deal with Sparse Datasets

As discussed above, sparse datasets can be proven bad for training a machine learning model and should be handled properly. There are several ways to deal with sparse datasets.

1. Convert the feature to dense from sparse

It is always good to have dense features in the dataset while training a machine learning model. If the dataset has sparse data, it would be a better approach to convert it to dense features.

There are several ways to make the features dense:

1. Use Principle Component Analysis:

PCA is a dimensionality reduction method used to reduce the dimension of the dataset and select important features only in the output.


Implementing PCA on the dataset

from sklearn.decomposition import PCA pca = PCA(n_components=2) principalComponents = pca.fit_transform(df) pca_df = pd.DataFrame(data = principalComponents , columns = ['principal component 1', 'principal component 2']) df = pd.concat([pca_df, df[['label']]], axis = 1)

2. Use Feature Hashing:

Feature hashing is a technique used on sparse datasets in which the dataset can be binned into the desired number of outputs.

from sklearn.feature_extraction import FeatureHasher h = FeatureHasher(n_features=10) p = [{'dog': 1, 'cat':2, 'elephant':4},{'dog': 2, 'run': 5}] f = h.transform(p) f.toarray()


array([[ 0., 0., -4., -1., 0., 0., 0., 0., 0., 2.], [ 0., 0., 0., -2., -5., 0., 0., 0., 0., 0.]])

3. Perform Feature Selection and Feature Extraction

4. Use t-Distributed Stochastic Neighbor Embedding (t-SNE)

5. Use low variance filter

2. Remove the features from the model

It is one of the easiest and quick methods for handling sparse datasets. This method includes removing some of the features from the dataset which are not so important for the model training.

However, it should be noted that sometimes sparse datasets can also have some useful and important information that should not be removed from the dataset for better model training, which can cause lower performance or accuracy.

Dropping a whole column having sparse data:

import pandas as pd df = pd.drop(['SparseColumnName'],axis=1)

Dropping a column having sparse datatype:

import pandas as pd import numpy as np df = pd.DataFrame({"A": pd.arrays.SparseArray([0, 1, 0])}) df.sparse.to_dense() print(df)

3. Use methods that are not affected by sparse datasets

Some of the machine learning models are robust to the sparse dataset, and the behavior of the models is not affected by the sparse datasets. This approach can be used if there is no restriction to using these algorithms.

For example, Normal K means the algorithm is affected by sparse datasets and performs badly, resulting in lower accuracy. Still, the entropy-weighted k means algorithm is not affected by the sparse data, giving reliable results. So it can be used while dealing with sparse datasets.


Sparse data in machine learning is a widespread problem, especially when working with one hot encoding. Due to the problem caused by sparse data (like over-fitting, lower performance of the models, etc.), handling these types of data is more recommended for better model building and higher performance of the machine-learning models.

Some Key Insights from this blog are:

1. Sparse data is completely different from missing data. It is a form of data that contains a high amount of zero values.

2. The sparse data should be handled properly to avoid problems like time and space complexity, lower performance of the models, over-fitting, etc.

3. Dimensionality reduction, converting the sparse features into dense features and using algorithms like entropy-weighted k means, which are robust to sparsity, can be the solution while dealing with sparse datasets.

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