¿Cómo elegir el modelo adecuado para su proyecto de ML?

It should be Data Preparation. Machine learning projects heavily relies on the quality of the data. If our data is messy, inaccurate or incomplete, even the most advanced models may struggle to perform.

Choosing the right model for your ML project involves considering factors like the nature of your data, problem type (classification, regression, etc.), and computational resources. Start with simpler models and progressively explore complex ones based on performance. Regarding data preparation, ensure it's clean, handle missing values, and consider feature scaling and encoding categorical variables as needed. Data quality significantly influences model effectiveness.

The way to prepare your data depends on the kind of data. When it comes to something like images, a deep CNN can extract features at different levels of its architecture, so, you can keep your images close to their raw form. You could do things like resizing, statistically and visually summarizing your samples, and rejecting outliers, etc. For multivariate data like signals from different sensors, you would use filtering methods that are domain-specific or aggregate these sensors to end up with something cleaner than individual sensor readings. Some of these filtering methods are engineered in tricky ways, and the deep learning model may not be able to figure out the optimal weights if you decide to submit raw data to the model directly.

Os dados são a matéria prima de um modelo de Machine Learning, sem dados não temos modelo. Então ter dados é o primeiro passo, o segundo é preparar seus dados para serem enviados para seu modelo. Dados nulos, duplicados, falta de qualidade, sem dicionário de dados, são fatores comuns de acontecer, então concentre esforços para deixar seus dados o mais perfeitos possível, antes de pensar em treinar seu modelo de ML.

To select the right ML model, first understand your data and problem(classification, regression, etc.). Choose between simple models like linear regression for smaller datasets and interpretability, or complex models like neural networks for larger datasets and complexity. Consider computational resources and training time. Select based on performance metrics aligned with project goals. Balance accuracy with interpretability; complex models are less interpretable but more accurate. Use cross-validation to avoid overfitting. Consider scalability. Research community support and existing literature. Experiment with many models to compare performance. Ensure ethical and legal compliance, regarding data privacy and model fairness.

The traditional method of model selection, presented in this article, is somewhat outdated. While understanding the types of models that are likely to perform well is valuable, human intervention in model selection is generally not necessary. Instead, we can implement a comprehensive grid search and let the pipeline automatically determine the most effective model. Focusing on a well-established pipeline and validation framework eliminates the need for manual model selection. However, this approach may not be suitable when specific requirements are present, such as explainability, package restrictions (common in finance, banking, or government), or compute or latency limitations.

already present traditional methods in this article and other contributions so I am talking about some model methods of machine learning techniques for model selection technique Cross-validation (e.g., k-fold cross-validation) Grid Search Random Search Bayesian Optimization (e.g., Optuna, Hyperopt) Evolutionary Algorithms (e.g., genetic algorithms) Ensemble Methods (e.g., bagging, boosting, stacking) Regularization Path Methods (e.g., Lasso path) Information Criteria (e.g., AIC, BIC) Performance Metrics Comparison (e.g., ROC-AUC, F1-Score, MSE) Nested Cross-Validation

Choosing the right model depends on the data, task, and goals. At a high level, for structured data, options include linear models, tree-based models, SVM or clustering algorithms. Image data naturally leans toward CNN-based models, while RNNs or CNNs are suitable for audio data. Presently, LLMs stand out for text data. In production, consider priorities: accuracy or inference speed? Can the model deploy within existing resources? If modifications are needed, can we retrain or fine-tune it with available resources? Assess interpretability's importance. Is scaling feasible to process more data within resource limits?

Everyone has perspective into model selection from contemporary to automated methods. Well, according to me methods like AutoML, grid search might not replace the need for domain expertise and a deep understanding of the problem. Such methods can be helpful for automating some aspects of the model selection process, increasing efficiency but human intuition & knowledge remain crucial for making informed decisions. Considerations such as explainability, package restrictions, and specific domain requirements often necessitate a balance between automated techniques and human expertise. Striking this balance ensures that the chosen model aligns not only with performance metrics but also with the broader goals and constraints of the application

Selecting the right ML model involves considering data characteristics, problem type (classification, regression), model complexity, computational resources, interpretability, and domain knowledge. Begin with simpler models and progress based on performance. Experiment, fine-tune, and validate using relevant metrics, aiming for a balance between accuracy and resource efficiency.

Model optimization in ML involves adjusting parameters and algorithms to improve performance. Key is identifying the right combination of hyperparameters, which can drastically affect outcomes. Techniques like grid search or random search help find optimal settings. Regularization methods can prevent overfitting, maintaining model generalizability. Additionally, feature engineering plays a crucial role; selecting, transforming, or creating features can significantly impact model efficacy. The optimization process should align with the project's specific goals, balancing efficiency, accuracy, and computational cost.

Choosing the right machine learning model for decision-making isn't enough. Unleashing its full potential requires hyperparameter tuning - adjusting its internal settings like learning rate or network layers. This iterative process is key, as it can significantly improve performance and prevent the model from underperforming. Techniques like grid search, random search, and Bayesian optimization guide this exploration, helping you find the sweet spot for your model. Mastering this optimization stage takes your ML skills to the next level, ensuring your models make the best decisions possible.

A cool approach to tuning hyper parameters is hyperopt, a Bayesian optimisation thats a lot quicker than grids. Note, Databricks has GPU hyperopt which cuts parameter tuning from hours to minutes. I think the future converges from grid->Bayes->Bayes GPU

Otimizar um modelo não tem receita de bolo. Podemos tentar usar algoritmos mais complexos para otimizar o aprendizado (Redes Neurais, Ensembles) ou utilizar diversas técnicas de regularização e transformação. Outra forma é buscar por novos dados! Esses dados podem trazer novos comportamentos que não estavam presentes no treinamento anterior. Além disso, podemos criar novos dados e ajudar na otimização do modelo. Implantar um modelo não é o fim do trabalho, ele terá que ser monitorado e analisar se não há desvios para garantir sua precisão, a otimização é um papel desse processo para melhorar e garantir uma boa performance.

Imagine your ML model as a musical instrument. You picked it out carefully, but it needs some fine-tuning to truly sing. That's where model optimization comes in – it's like adjusting your instrument's strings, knobs, and pedals to get the most beautiful, accurate melody out of your data. I found optimizing hyperparameters like learning rate and network size is like experimenting with different tunings and playing techniques. Each tweak can affect your model's performance, so finding the sweet spot requires an iterative process of trial and error.

Model evaluation is the critical encore in the machine learning symphony. After algorithms have danced with data, assessing their performance becomes paramount. Metrics like accuracy, precision, recall, and F1 score take the stage, providing insights into predictive prowess. The validation set serves as the unbiased judge, scrutinizing the model's ability to generalize beyond training data. A delicate balance is sought—avoiding overfitting while ensuring the model captures essential patterns. The pursuit of optimal performance requires iterative refinement, transforming the evaluation process into a continual tuning of the algorithmic melody for predictive excellence.

Model statistics such as F1 score, R2, MAPE, RMSE, etc., are pivotal in assessing model accuracy and detecting over-prediction or under-prediction. Evaluating the model on untrained data and navigating the delicate balance between bias and variance adds a layer of robust validation. Moreover, scrutinizing actual and predicted trends is critical; ensuring all significant peaks and dips in sales align with independent variables. Iteratively refining features guarantees a comprehensive explanation, maintaining the model's effectiveness

You've chosen the model, tuned it like a finely crafted instrument, now it's time for the applause – model evaluation. This is where your data symphony reaches its crescendo, revealing whether your model truly hits the right notes. In my opinion, evaluating performance on unseen data is like sending your model out for its first solo performance. Metrics like accuracy, precision, and recall are your critical reviewers, giving you the applause (or constructive criticism) you need.

. This phase involves using metrics like accuracy, precision, recall, and F1-score for classification problems, or mean squared error and R² for regression tasks. It's not just about high accuracy; evaluation also considers how the model performs on unseen data, indicating its ability to generalize. Techniques like cross-validation provide a more robust assessment by testing the model on multiple data subsets. It's also vital to assess the model in the context of business or real-world impact, ensuring it delivers practical, actionable results. This comprehensive evaluation guides any necessary refinements, ensuring the model is not only technically sound but also effective and reliable in practical applications.

Avaliação de um modelo é papel fundamental, sem uma boa avaliação é impossível implantar um modelo. Existem diversas técnicas para avaliarmos, para problemas de classificação pode usar: Matriz de confusão, Relatório de classificação, Curva ROC e outros. Regressões: R2, RMSE(Erro Quadrático Médio), MSE (erro quadrático médio) e outros. Clusterização: podemos usar o Elbow Method (Curva do Joelho) a ideia dessa avaliação é dizer qual o número ótimo de cluster. Por fim, temos diversas técnicas e o profissional tem que saber interpretar cada uma e traduzir de uma forma simples para as áreas de negócios.

This involves selecting the right deployment architecture, which can vary from cloud-based solutions to on-premises deployments, depending on the project's scale, security needs, and resource availability. Ensuring the model's performance remains stable in a real-world setting is crucial, which might require continuous monitoring and updating to adapt to new data or changing conditions. Effective deployment also includes setting up APIs for model access, establishing robust security measures, and ensuring scalability to handle increased loads. This phase marks the transition from a theoretical model to a practical tool, emphasizing the importance of maintaining performance, reliability, and user experience in real-world applications.

Model deployment marks the grand finale of the machine learning journey. After meticulous training and evaluation, the chosen model steps onto the real-world stage. It transitions from the development environment to practical applications, addressing live data. This process demands seamless integration into existing systems and careful consideration of latency and resource constraints. Monitoring becomes the vigilant usher, ensuring the model's ongoing performance aligns with expectations. As the algorithm takes its place in real-world scenarios, the success of deployment lies in its ability to deliver accurate predictions and valuable insights, contributing to the broader landscape of informed decision-making.

Django? SageMarker's big issue is AWS Canvas. It's dumbed down point and click all the way through and I guess sooner or later they'll be a "Canvas deployment".

You've crafted the perfect model, its performance is stellar, the applause is deafening... now what? It's time for model deployment, the final act where your ML masterpiece takes center stage in the real world! In my experience, deployment is like taking your finely-tuned instrument onto a massive concert stage. It's about integrating your model seamlessly into your application, ensuring it can handle the spotlight of real-time use. Reliability and scalability are your roadies, making sure your model doesn't crash under pressure and can reach a vast audience.

Before getting right into model fitting, it's better to gather requirements. Now this involves: -defining problem statement - data collection - data analysis - research papers: read recent research papers to know what is latest state of art available in that area. - implementation: once you have idea of what's working best, you can choose according to your requirement. Like Yolo latest model is released regularly, so it's better to know what is latest and which model variety to use. - evaluation -deploy and monitor: after implementation you can deploy your model and then monitor

Entre as questões importantes a se considerar estão em cada etapa, por exemplo: O Balanceamento dos dados é importante pois os modelos não podem tender suas previsões ("overfitting"). Recomenta-se a aplicação de "oversampling" ou "undersampling". A memória do resultado é importante pois, se uma informação anterior for importante isso pode implicar em ML já conhecidos como CNN e RNN. Por fim, o monitoramento dos modelos é de crucial importância para manutenção da confiabilidade dos resultados Uma ferramenta interessante para isso é o "What If Tool".

"Feature selection" is key to carbon offset calculations (my experience), because it gives insight into how to change the real-world environment that the feature is reporting. Thus it gives information on how to change our 𝘪𝘯 𝘴𝘪𝘵𝘶 environment, not simply the 𝘪𝘯 𝘴𝘪𝘭𝘪𝘤𝘰 model.