You’re trying to solve a machine learning problem. What’s the key ingredient you need to succeed?

Data is the essential component of your machine learning model. Therefore, before you begin make sure that your data is clean and made available in proper format. You must first clean the data by removing the missing values, outliers and duplicates. Also, make sure you apply features engineering techniques to get the relevant features for your model training. Perform exploratory data analysis to learn more about your data.

The key ingredient to succeed in solving a machine learning problem is having high-quality data. High-quality data is crucial because machine learning models learn patterns from the data they are trained on. If the data is noisy, incomplete, or biased, the model's performance will be adversely affected. Additionally, having a clear understanding of the problem you are trying to solve, selecting appropriate algorithms, and fine-tuning them are also essential components of successful machine learning projects.

As other contributors have already said if you're stuck on an ML problem you'd better hope it's not distracting you from other ways to actually solve your business problem. For this the biggest tip from my experience are simpler solutions are not only desirable but often outperform for those of us working with data sets that might not be enormous. While the headlines are dominated by the realisation that huge networks trained on giant data sets (chatGPT etc.) can perform previously unimaginable feats, most of us are working with sparse data trying to solve specific problems. For this I want to know why the simplest solve isn't the right one before I add complexity.

The key ingredient is sticking to basics and then proceed accordingly to a more complex environment: The primary step in the problem is to understand the data. Make sure that the data comes from the reliable sources, is well curated, balanced, and if possible, ensure minimal noise. Secondly, choose the models by focussing on the application at hand. In several cases, it has been observed that simple and non-parametrised networks perform at par or better than highly parametrised and computation intensive models. Thirdly, choose appropriate metrics to quantify the model's performance. Fourthly, make sure that the models are as unbiased as possible by mitigating randomization errors and performing statistical tests on the model.

While high-quality data often enhances the accuracy of Machine Learning models, the reality is that obtaining such data consistently is not guaranteed. Consequently, it becomes imperative to engage in Feature Engineering, which involves tasks such as cleaning data to eliminate missing values, identifying outliers through statistical techniques, and removing duplicate entries. Additionally, Data Scientists frequently resort to up-sampling and down-sampling strategies, particularly when faced with imbalanced datasets. Moreover, the advent of new technologies like Generative AI, such as VertexAI in GCP and OpenAI in Azure, underscores the evolving landscape of Deep Learning techniques embraced by Data Scientists in the contemporary era.

Features are key to be successful. Focus more on feature engineering and make sure you got enough data to feed to your model

The amount of data available depends on a domain specific problem. It's always nice to have a lot of data for your model. Augmentation techniques always help dynamically increase your dataset when training machine learning models. The choice of augmentation is a hyper parameter. In classification tasks, ensure that the imbalanced classes are taken care. It always helps to sample a subset of your population for training purposes!

One of the most important things while considering machine learning models is the quantity of data, the dataset should be representing all of the classes, i.e. not imbalanced , and enough to train the model.

I wish to talk about the trade-off between data quality and quantity. While a larger dataset offers more opportunities to capture diverse patterns and variations, it also introduces more noise and potential inaccuracies. On the other hand, focusing on data quality ensures that the information used for training is reliable and relevant, leading to more robust and interpretable models. Being too stringent about data quality might mean we don't have enough data to work with, limiting the model's ability to generalize effectively. So, it's all about striking the right balance between data quality and quantity after careful consideration of the specific objectives and resource constraints of the machine learning task.

In terms of data quantity, having a sufficient amount of data is important to train complex machine learning models effectively. More data allows the model to learn diverse patterns and generalize better to unseen examples. However, the amount of data needed can vary depending on the complexity of the problem and the algorithm used. It's essential to strike a balance between having enough data and avoiding overfitting, where the model memorizes the training data instead of learning generalizable patterns.

Absolutely! Data relevance ?? is key. Having a massive dataset is great, but if it's not relevant to the problem you're trying to solve, it won't help much. Make sure your data contains the features and information that are actually predictive of the outcome you're interested in. ?? This will help your model learn the right patterns and make accurate predictions.

One of the most important thing in solving a machine learning problem is having data that is directly related to the problem being addressed. Relevance ensures that the data contains the necessary information for the model to learn meaningful patterns and make accurate predictions. Irrelevant data can introduce noise, bias, or confusion, leading to suboptimal performance or incorrect conclusions. Therefore, focusing on acquiring or generating data that aligns closely with the problem domain is essential for successful machine learning outcomes. Additionally, data preprocessing techniques such as feature selection and dimensionality reduction can help enhance relevance by filtering out irrelevant or redundant information from the dataset.

Apart from having a good quality dataset with alot of data points, it is also crucially important to have relevant data that fits the problem you're trying to solve. This is where domain knowledge will also play a key role. For example, if we are looking at a demand forecasting module, there will be the straightforward parameters that you will be collecting like the items, quantities, time periods etc. as the part of the dataset, whichever parameters related to your use case. But it is also equally important to think about it in a holistic view and take other external factors like weather, seasonal changes in demand etc. Into consideration to get the best outcomes of the ML model you're developing.

Data relevance is critical in machine learning, acting as the engine's fuel. Quality, quantity, and relevance must align with project goals and best practices in data collection and preparation. Evaluating the data source, collection methodology, and alignment with project objectives ensures relevance. Questions like the origin, credibility, and impact on stakeholders guide the selection of pertinent data. Ensuring data relevance involves assessing if it directly supports the machine learning task at hand, contributing to accurate model training and achieving desired outcomes. This careful curation is key to solving machine learning challenges effectively.

Data relevance requires understanding the business context. CRISP-DM provides a structured framework to align data with business goals for valuable insights.

If I told you the secret ingredient, I'd have to end you! The secret ingredient unlocks the key to all mankind being taken over by robots and I have a duty to keep that under wraps ??

There is no such thing as a machine learning problem. There are business problems to solve, and sometimes ML is the right solution. Before you jump into how to get more or better data for your _ML problem_, first consider what the actual business problem is, talk to your stakeholders, understand their needs. If indeed ML is the right solution, you don't have an ML problem, you have a business problem which would benefit from an ML solution. Now is time to consider the contributions on the other questions in this topic: what data do you need, do you have enough of it, is it high enough quality, etc.

3 years ago, I found myself in a hackathon, buzzing with the challenge to code a bot. My initial thought? "Let’s jump on the machine learning bandwagon!" After all, ML was one of the tech world's latest craze. But here's the twist: our team wasn't exactly a bunch of ML wizards. Our experience was basic at best, and we were short on both data and time. Quickly, it dawned on us that diving headfirst into ML might not be such a great idea. We ended coding a more simple algorithm. This hackathon taught us a valuable lesson: just because something is the latest trend or has a cool factor, doesn't mean it's the right fit for every problem. Focus on what is practical and doable with your resources and time frame.

Machine Learning techniques have tremendous potential in solving various business problems. A thorough understanding of the system that emanates the data spells the success of the ML problem-solving approach. - What are the characteristics of the data when the system is at rest or perturbation? - Does the system absorb the perturbation and stay at equilibrium? Problem formulation and working with business users pave the path to success. Most problems can be treated as grey-box and linear time-invariant (LTI) or just mental models initially, and then the real characteristics of the system can be infused mathematically. A wise man said, "All models are wrong and some models are useful". Let's derive the "useful" model from the data.

There is no such key ingredient while working on ML models or use cases. In my opinion choosing right set of data, tools and technologies at every step of this cycle should align with what you are aiming to achieve at the end. “Data quantity, quality , relevance all are important”