Effect of more data on logistic regression

Older algorithms know what to do with all data

Learning curve of older algorithms _____ out

Terms related to algorithm performance plateaus

The process of an older algorithm reaching a plateau

Question:
Explain what it means for older learning algorithms, such as logistic regression, to "plateau" as more data is added. Why is this a limitation?

Sample answer:
When older learning algorithms like logistic regression "plateau," it means that their performance stops improving regardless of how much additional training data is provided. Their learning curves "flatten out." This is a significant limitation today because we have massive amounts of data available, and these older algorithms essentially do not have the capacity to learn from or effectively utilize this vast surplus of information.

Key points:
- Plateauing means performance stops improving with more data.
- The learning curve flattens out.
- An example is logistic regression.
- Older algorithms cannot effectively utilize the massive amounts of data available today.

Rubric:
The essay should define the term "plateau" as the flattening of the learning curve and the cessation of performance improvement. It should also note the inability of older algorithms to utilize large modern datasets.

Explaining performance plateaus in older algorithms

Case context:
You are working on a classification problem and have started with a logistic regression model. Over the past year, your team has collected ten times as much training data as you originally had. You train the model on this massive new dataset, hoping for a significant jump in accuracy. However, upon testing, the model's accuracy has barely increased at all.

Question:
Based on the concept of older algorithms plateauing, diagnose why the logistic regression model's accuracy has not improved and explain what this implies about the model's capacity to handle your new data.

Sample answer:
The logistic regression model has likely reached a performance "plateau." This means its learning curve has flattened out because it is an older learning algorithm. As a result, it stops improving even though you have provided it with significantly more data. This implies that the algorithm lacks the capacity to figure out what to do with all the new data and cannot extract further meaningful patterns from the increased volume.

Key points:
- Diagnose the issue as a performance plateau.
- Explain that logistic regression is an older algorithm.
- Note that the learning curve has flattened out.
- State that the algorithm cannot utilize the additional data effectively.

Rubric:
The response should correctly diagnose the issue as a performance plateau and explain that logistic regression, as an older algorithm, eventually stops improving and its learning curve flattens out, showing an inability to utilize vast amounts of data.

Addressing a plateauing logistic regression model

Question:
What happens to the learning curve of an older algorithm like logistic regression as you accumulate massive amounts of data?

Sample answer:
The learning curve of an older algorithm like logistic regression typically "flattens out" or plateaus, meaning it stops improving even with more data.

Key points:
- The learning curve flattens out.
- The performance plateaus.
- It stops improving with more data.

Rubric:
The answer must mention that the learning curve flattens out, plateaus, or that the algorithm stops improving.

Logistic regression performance with large data

Identifying a plateauing algorithm

Plateaus mean learning curves flatten

Older learning algorithms such as logistic regression can plateau as more data is added, meaning their learning curves flatten and the algorithms stop improving even with more data.

Google

Recent progress in deep learning has been driven by data availability and computational scale.

Scale Drives Machine Learning Progress

Machine Learning Yearning, a free ebook from Andrew Ng, teaches you how to structure Machine Learning projects.
https://www.deeplearning.ai/machine-learning-yearning/

Note: The content of the book is aligned with the Coursera Deeplearning.ai specialization.  https://www.deeplearning.ai/deep-learning-specialization/ 

Machine Learning Yearning (Deeplearning.ai)

Data availability is a major driver of recent deep learning progress because digital-device activity creates large amounts of data that can be fed to learning algorithms.

Data Availability as a Driver of Deep Learning Progress

Computational scale is a major driver of recent deep learning progress because it makes it possible to train neural networks large enough to take advantage of huge datasets.

Computational Scale as a Driver of Deep Learning Progress

Older Learning Algorithms Can Plateau with More Data

Larger neural networks can obtain better performance, and the best performance comes from training a very large neural network while also having a huge amount of data.

Large Neural Networks Benefit from Huge Data

In a small data regime, traditional algorithms may or may not do better depending on how features are hand-engineered. With only about 20 training examples, feature hand-engineering can matter more than choosing logistic regression or a neural network.

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