This article data science blogthon.
prologue
Machine learning requires quantity and quality of data to model training and performance. The amount of data greatly affects machine learning and deep learning algorithms. Most of the algorithm’s behavior changes as the amount of data increases or decreases. However, in the case of limited data, machine learning algorithms need to be effectively processed to get better results and accurate models. Deep learning algorithms are also data intensive and require large amounts of data to improve accuracy.
In this article, we will discuss the relationship between data quantity and quality by machine learning and deep learning algorithms, the pain points of limited data, and the accuracy of processing it. Knowledge of these key concepts will help you understand algorithms and data scenarios, allowing you to process limited data efficiently.
Data Volume vs. Performance Graph
In machine learning, a question comes to mind: How exact is the data required to train a good machine learning or deep learning model? There is no threshold level or fixed answer for this, because each piece of information is different and has different characteristics and patterns. Still, there are some threshold levels where machine learning or deep learning algorithm performance tends to be constant.
Most of the time, machine learning and deep learning models tend to perform well as the amount of data they are fed increases, but after a certain point or amount of data, the behavior of the model becomes constant and the data Stop learning from
The pictures above show the performance of well-known machine learning and deep learning architectures and the amount of data fed to the algorithms. Here we can see that traditional machine learning algorithms learn a lot from preliminary period data when the amount of data being fed is increasing, but after some time the threshold level is reached and the performance remains constant. Become. Now, if you feed the algorithm more data, the algorithm will not learn anything and the version will not increase or decrease.
For deep learning algorithms, there are a total of three types of deep learning architectures in the diagram. A shallow layer of deep learning structure is a minor deep learning architecture in terms of depth. This means that there are few hidden layers and neurons in the external deep learning architecture. For deep neural networks, the number of hidden layers and neurons is very large and very carefully designed.[eofdeeplearningstrictureisaminordeeplearningarchitectureintermsofdepthmeaningthattherearefewhiddenlayersandneuronsinexternaldeeplearningarchitecturesInthecaseodeepneuralnetworksthenumberofhiddenlayersandneuronsisveryhighanddesignedveryprofoundly[eofdeeplearningstrictureisaminordeeplearningarchitectureintermsofdepthmeaningthattherearefewhiddenlayersandneuronsinexternaldeeplearningarchitecturesInthecaseodeepneuralnetworksthenumberofhiddenlayersandneuronsisveryhighanddesignedveryprofoundly
From this diagram, we can see a total of three deep learning architectures. All three show different performance when fed with some amount of data and scaled up. Shallow and deep neural networks tend to behave like traditional machine learning algorithms, with constant performance once a certain amount of data is accumulated. At the same time, the deep neural network continues to learn from the data as it is fed new data.
From the figure we can conclude that:
“Deep neural networks require a lot of data”
What problems do you run into with limited data?
Limited data can cause some problems, and models can perform better when trained on limited data. Here are some common problems encountered with limited data:
1. Classification:
In classification, the model misclassifies observations when the amount of data supplied is small. That means you don’t get the exact output class for a particular word.
2. Regression:
In regression problems, if the model accuracy is low, the model will make very wrong predictions. That is, since it is a regression problem, numbers are expected. Still, with limited data, you may see a scary amount far from the actual output.
3. Clustering:
When trained on limited data, the model can classify various points in the wrong clusters in clustering issues.
4. Time series:
Time series analysis forecasts some data in the future. Nevertheless, time series models with poor accuracy can produce poor forecast results and can introduce many time-related errors.
5. Object detection:
If the object detection model is trained on limited data, it may detect objects incorrectly or classify objects incorrectly.
How to deal with the limited data problem?
I need a precise or fixed way to handle limited data. Each machine learning problem is different, and so are the ways to solve specific problems. However, some standard techniques often help.
1. Data augmentation
Data augmentation is the technique of using existing data to generate new data. The extra information generated here looks like old data, but some of the values and parameters are different here.
This approach can increase the amount of data and is likely to improve model performance.
Data augmentation is preferred for most deep learning problems with limited data, including images.
2. Don’t drop and assign:
Some datasets have a high percentage of invalid or empty data. Therefore, some data is dropped so as not to complicate the process, but doing so reduces the amount of data and may cause various problems.
To address this, data imputation methods can be applied to attribute data. Emptying data is not a simple and precise method, but some advanced attributes such as KNNImputer and IterativeImputer can be used for precise and efficient data imputation.
3. Custom approach:
If your data is limited, you can search the internet to find similar data. Once this type of data is obtained, it can be used to generate more data or merged with existing data.
This is where domain knowledge comes in handy. A domain expert can advise and guide you in this matter very efficiently and accurately.
Conclusion
This article will cover limited data, the performance of some machine learning and deep learning algorithms, increasing and decreasing amounts of data, the types of problems that limited data can pose, and working with limited data in general. explained how. This article will help you understand the limited data process, its impact on performance, and how to handle it.
A few important point From this article:
1. Machine learning and shallow neural networks are algorithms that are immune to data volumes above a certain threshold level.
2. Deep neural networks are data-hungry algorithms that never stop learning from data.
3. Limited data can cause problems in all areas of machine learning applications such as classification, regression, time series, and image processing.
4. Apply data augmentation, imputation, and other custom approaches based on domain knowledge to handle limited data.
Want to contact the author?
Follow Parth Shukla @AnalyticsVidhya. LinkedIn, twitterWhen Moderate for more content.
Contact Part Shukla @Part Shukla | Portfolio or Parth Shukla | email to contact me.
Media shown in this article are not owned by Analytics Vidhya and are used at the author’s discretion.
