Generate a series of (instruction, response) pairs that are similar in context and structure to the example provided below. Each pair should consist of a concise instruction followed by an appropriate, detailed response. The instruction should pose a clear task or question, while the response should provide a comprehensive answer or solution that could be understood by someone with a basic understanding of the subject.

Example pair: 
Instruction: Briefly tell me what the following text is about. Thanks.

The text:
Blue eyed people listen up.

Ever wondered why your eyes are the colour they are? Well wonder no more.

Every blue eyed person is descended from a single European who lived around 6,000 to 10,000 years ago, according to scientists.

How did they work it out? Originally, all humans had brown eyes in various shades until there was a specific mutation that made the change.

Ancient formation discovered wrapped around Earth's core
A black hole is speeding through its galaxy giving birth to baby stars
Every blue eyed person on the planet comes from one single person
The mutation is a gene called HERC2 and it switches off OCA2, the gene that determines how much brown pigment we make.

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So that's why eyes become blue.

As for being descended from the same person, the evidence for this is because every blue eyed person alive today has this same mutation.
Response: Originally, every human had brown eyes until a single European who lived around 6,000 to 10,000 years ago had a gene mutation that caused their eyes to be blue. The gene in question is a mutation from HERC2 and scientists believe that every blue-eyed person alive today has descended from this one individual.

Your task is to generate more pairs that maintain this level of clarity and detail. The topic is Summarization. Write a long text of instruction by yourself, then summarize the given instruction in a response. Ensure that the responses are informative and accurate, suitable for an educational context. 

Store the generated pairs in valid JSON format, with each pair as an object within an array. Each object should have two key-value pairs: "instruction" and "response". For instance: 

{"contents": [{"instruction": "text", "response": "text"}, {"instruction": "text", "response": "text"}, ...]} 

Remember to maintain consistency in the format and ensure the generated pairs are diverse and cover a broad range of subjects. You must return the response in the asked format and you must not add any additional text in your response.

Summarize the main points from the following passage on using ensemble methods to improve machine learning model performance:

Ensemble methods combine predictions from multiple models to improve overall performance on a task. They leverage the idea that a group of models can make better predictions than any individual constituent model. Ensembles typically reduce overfitting on training data and achieve better generalization.
  
Popular ensemble techniques include bagging, boosting, and stacking. Bagging trains models in parallel on different subsamples of the data and combines their outputs by majority voting or averaging. Boosting builds models sequentially, with later models focusing on instances misclassified by earlier ones. Stacking uses a meta-model to combine predictions from base models trained on the original data.
  
Random Forests and Gradient Boosted Trees are powerful tree-based ensemble methods that have demonstrated state-of-the-art performance on many predictive modeling tasks. Ensemble models are also widely used in deep learning, e.g. combining predictions from multiple CNN architectures for image classification.
  
Successful ensembles require diversity among the base models' predictions. Common approaches include training on different subsets of features or data, varying model architectures or hyperparameters, and combining models of different types like trees and neural nets.
  
While ensembles improve robustness and predictive power, they increase model complexity, computational cost, interpretability challenges, and risks around propagating biases from multiple models. Domain knowledge is required to assess trade-offs and select appropriate ensemble configurations.
user
The passage discusses ensemble methods which combine predictions from multiple models to improve performance and generalization. Popular techniques include bagging (parallel training on data subsets), boosting (sequential focusing on misclassified instances), and stacking (meta-model over base models). Random Forests and Gradient Boosting are powerful tree-based ensembles. Ensembles in deep learning combine predictions across architectures. Diversity through differing data, architectures, or model types is key. While enhancing robustness, ensembles increase complexity, cost, non-interpretability, and bias propagation risks. Domain knowledge guides appropriate ensemble configuration based on trade-offs.
assistant