Issues in Machine Learning

"Machine Learning" is one of the most popular technology among all data scientists and machine learning enthusiasts. It is the most effective Artificial Intelligence technology that helps create automated learning systems to take future decisions without being constantly programmed. It can be considered an algorithm that automatically constructs various computer software using past experience and training data. It can be seen in every industry, such as healthcare, education, finance, automobile, marketing, shipping, infrastructure, automation, etc. Almost all big companies like Amazon, Facebook, Google, Adobe, etc., are using various machine learning techniques to grow their businesses. But everything in this world has bright as well as dark sides. Similarly, Machine Learning offers great opportunities, but some issues need to be solved.

This article will discuss some major practical issues and their business implementation, and how we can overcome them. So let's start with a quick introduction to Machine Learning.

What is Machine Learning?

Machine Learning is defined as the study of computer algorithms for automatically constructing computer software through past experience and training data.

It is a branch of Artificial Intelligence and computer science that helps build a model based on training data and make predictions and decisions without being constantly programmed. Machine Learning is used in various applications such as email filtering, speech recognition, computer vision, self-driven cars, Amazon product recommendation, etc.

Commonly used Algorithms in Machine Learning

Machine Learning is the study of learning algorithms using past experience and making future decisions. Although, Machine Learning has a variety of models, here is a list of the most commonly used machine learning algorithms by all data scientists and professionals in today's world.

• Linear Regression
• Logistic Regression
• Decision Tree
• Bayes Theorem and Naïve Bayes Classification
• Support Vector Machine (SVM) Algorithm
• K-Nearest Neighbor (KNN) Algorithm
• K-Means
• Gradient Boosting algorithms
• Dimensionality Reduction Algorithms
• Random Forest

Common issues in Machine Learning

Although machine learning is being used in every industry and helps organizations make more informed and data-driven choices that are more effective than classical methodologies, it still has so many problems that cannot be ignored. Here are some common issues in Machine Learning that professionals face to inculcate ML skills and create an application from scratch.

1. Inadequate Training Data

The major issue that comes while using machine learning algorithms is the lack of quality as well as quantity of data. Although data plays a vital role in the processing of machine learning algorithms, many data scientists claim that inadequate data, noisy data, and unclean data are extremely exhausting the machine learning algorithms. For example, a simple task requires thousands of sample data, and an advanced task such as speech or image recognition needs millions of sample data examples. Further, data quality is also important for the algorithms to work ideally, but the absence of data quality is also found in Machine Learning applications. Data quality can be affected by some factors as follows:

• Noisy Data- It is responsible for an inaccurate prediction that affects the decision as well as accuracy in classification tasks.
• Incorrect data- It is also responsible for faulty programming and results obtained in machine learning models. Hence, incorrect data may affect the accuracy of the results also.
• Generalizing of output data- Sometimes, it is also found that generalizing output data becomes complex, which results in comparatively poor future actions.

2. Poor quality of data

As we have discussed above, data plays a significant role in machine learning, and it must be of good quality as well. Noisy data, incomplete data, inaccurate data, and unclean data lead to less accuracy in classification and low-quality results. Hence, data quality can also be considered as a major common problem while processing machine learning algorithms.

3. Non-representative training data

To make sure our training model is generalized well or not, we have to ensure that sample training data must be representative of new cases that we need to generalize. The training data must cover all cases that are already occurred as well as occurring.

Further, if we are using non-representative training data in the model, it results in less accurate predictions. A machine learning model is said to be ideal if it predicts well for generalized cases and provides accurate decisions. If there is less training data, then there will be a sampling noise in the model, called the non-representative training set. It won't be accurate in predictions. To overcome this, it will be biased against one class or a group.

Hence, we should use representative data in training to protect against being biased and make accurate predictions without any drift.

4. Overfitting and Underfitting

Overfitting:

Overfitting is one of the most common issues faced by Machine Learning engineers and data scientists. Whenever a machine learning model is trained with a huge amount of data, it starts capturing noise and inaccurate data into the training data set. It negatively affects the performance of the model. Let's understand with a simple example where we have a few training data sets such as 1000 mangoes, 1000 apples, 1000 bananas, and 5000 papayas. Then there is a considerable probability of identification of an apple as papaya because we have a massive amount of biased data in the training data set; hence prediction got negatively affected. The main reason behind overfitting is using non-linear methods used in machine learning algorithms as they build non-realistic data models. We can overcome overfitting by using linear and parametric algorithms in the machine learning models.

Methods to reduce overfitting:

• Increase training data in a dataset.
• Reduce model complexity by simplifying the model by selecting one with fewer parameters
• Ridge Regularization and Lasso Regularization
• Early stopping during the training phase
• Reduce the noise
• Reduce the number of attributes in training data.
• Constraining the model.

Underfitting:

Underfitting is just the opposite of overfitting. Whenever a machine learning model is trained with fewer amounts of data, and as a result, it provides incomplete and inaccurate data and destroys the accuracy of the machine learning model.

Underfitting occurs when our model is too simple to understand the base structure of the data, just like an undersized pant. This generally happens when we have limited data into the data set, and we try to build a linear model with non-linear data. In such scenarios, the complexity of the model destroys, and rules of the machine learning model become too easy to be applied on this data set, and the model starts doing wrong predictions as well.

Methods to reduce Underfitting:

• Increase model complexity
• Remove noise from the data
• Trained on increased and better features
• Reduce the constraints
• Increase the number of epochs to get better results.

5. Monitoring and maintenance

As we know that generalized output data is mandatory for any machine learning model; hence, regular monitoring and maintenance become compulsory for the same. Different results for different actions require data change; hence editing of codes as well as resources for monitoring them also become necessary.

6. Getting bad recommendations

A machine learning model operates under a specific context which results in bad recommendations and concept drift in the model. Let's understand with an example where at a specific time customer is looking for some gadgets, but now customer requirement changed over time but still machine learning model showing same recommendations to the customer while customer expectation has been changed. This incident is called a Data Drift. It generally occurs when new data is introduced or interpretation of data changes. However, we can overcome this by regularly updating and monitoring data according to the expectations.

7. Lack of skilled resources

Although Machine Learning and Artificial Intelligence are continuously growing in the market, still these industries are fresher in comparison to others. The absence of skilled resources in the form of manpower is also an issue. Hence, we need manpower having in-depth knowledge of mathematics, science, and technologies for developing and managing scientific substances for machine learning.

8. Customer Segmentation

Customer segmentation is also an important issue while developing a machine learning algorithm. To identify the customers who paid for the recommendations shown by the model and who don't even check them. Hence, an algorithm is necessary to recognize the customer behavior and trigger a relevant recommendation for the user based on past experience.

9. Process Complexity of Machine Learning

The machine learning process is very complex, which is also another major issue faced by machine learning engineers and data scientists. However, Machine Learning and Artificial Intelligence are very new technologies but are still in an experimental phase and continuously being changing over time. There is the majority of hits and trial experiments; hence the probability of error is higher than expected. Further, it also includes analyzing the data, removing data bias, training data, applying complex mathematical calculations, etc., making the procedure more complicated and quite tedious.

10. Data Bias

Data Biasing is also found a big challenge in Machine Learning. These errors exist when certain elements of the dataset are heavily weighted or need more importance than others. Biased data leads to inaccurate results, skewed outcomes, and other analytical errors. However, we can resolve this error by determining where data is actually biased in the dataset. Further, take necessary steps to reduce it.

Methods to remove Data Bias:

• Research more for customer segmentation.
• Be aware of your general use cases and potential outliers.
• Combine inputs from multiple sources to ensure data diversity.
• Include bias testing in the development process.
• Analyze data regularly and keep tracking errors to resolve them easily.
• Review the collected and annotated data.
• Use multi-pass annotation such as sentiment analysis, content moderation, and intent recognition.

11. Lack of Explainability

This basically means the outputs cannot be easily comprehended as it is programmed in specific ways to deliver for certain conditions. Hence, a lack of explainability is also found in machine learning algorithms which reduce the credibility of the algorithms.

12. Slow implementations and results

This issue is also very commonly seen in machine learning models. However, machine learning models are highly efficient in producing accurate results but are time-consuming. Slow programming, excessive requirements' and overloaded data take more time to provide accurate results than expected. This needs continuous maintenance and monitoring of the model for delivering accurate results.

13. Irrelevant features

Although machine learning models are intended to give the best possible outcome, if we feed garbage data as input, then the result will also be garbage. Hence, we should use relevant features in our training sample. A machine learning model is said to be good if training data has a good set of features or less to no irrelevant features.

Conclusion

An ML system doesn't perform well if the training set is too small or if the data is not generalized, noisy, and corrupted with irrelevant features. We went through some of the basic challenges faced by beginners while practicing machine learning. Machine learning is all set to bring a big bang transformation in technology. It is one of the most rapidly growing technologies used in medical diagnosis, speech recognition, robotic training, product recommendations, video surveillance, and this list goes on. This continuously evolving domain offers immense job satisfaction, excellent opportunities, global exposure, and exorbitant salary. It is high risk and a high return technology. Before starting your machine learning journey, ensure that you carefully examine the challenges mentioned above. To learn this fantastic technology, you need to plan carefully, stay patient, and maximize your efforts. Once you win this battle, you can conquer the Future of work and land your dream job!