Su equipo está dividido en cuanto a las opciones de algoritmos para la minería de datos. ¿Cómo puede navegar hacia resultados óptimos?

To navigate towards optimal outcomes when your team is divided on algorithm choices for data mining, focus on these steps these are must you follow: - Define Clear Objectives - Evaluate Data Characteristics - Conduct Benchmarking - Leverage Ensemble Methods - Foster Open Communication - Iterate and Validate

The algorithms in the question should be evaluated thoroughly based on the use case and the stakeholders we are implementing them for. Efficiency, scalability and data compatibility do play a major role as well. For eg if the stakeholders come from a non tech background, you must prioritise explainability of the algorithms so as to communicate your solutions without confusing them with complex jargons. In other case, if you are trying to create an end to end application for a large audience, you should consider scalability as one of the critical factors for choosing the algorithms. Sometimes even a simple regression can be used to answer some of the most complex business problems

Also consider the transparency needed from the model. Do you need a good prediction? Or an explanation of why a specific behavior happens?

This may not be as big a problem as it seems. One of the most important developments in the data world over the last 10-20 years has been the rise of ensemble modeling, where the results of many different models are combined to get a single, more reliable outcome. While ensemble modeling is generally associated with classification tasks, the concept applies to data mining as well: more perspectives are better, and combined perspectives are generally more reliable than any single perspective. If you believe that you have found a genuine, actionable insight in your data mining project, it should show up using more than one algorithm. That confirmation will give you greater confidence in the results.

Comparing options in data mining is essential for informed decisions. Define criteria like performance metrics and scalability. Conduct side-by-side evaluations using standardized benchmarks and diverse datasets. Seek team input for balanced perspectives. Document findings to facilitate transparent discussions and consensus. Rigorous comparison mitigates biases and optimizes algorithm selection for optimal outcomes.

Comparar as opções é fundamental para tomar uma decisão informada. Vamos explorar algumas vantagens e desvantagens dos algoritmos que você mencionou: Árvores de Decisão: Vantagens: Interpretáveis: As árvores de decisão permitem visualizar o processo de tomada de decisão. Lida bem com dados categóricos e numéricos. Desvantagens: Pode ser sensível a pequenas variações nos dados de treinamento. Pode não capturar relacionamentos complexos. Redes Neurais: Vantagens: Poderosas: Redes neurais podem modelar relações não lineares e complexas. Adequadas para grandes volumes de dados. Desvantagens: Caixa preta: A interpretação dos resultados pode ser desafiadora. Requer grande quantidade de dados de treinamento.

Comparison between models (and outcomes!) is absolutely essential. But it also requires a clear benchmark, which should include processing time, scalability, sensitivity to market (data) conditions and so on.

Often different algorithmic / statistical options will lead to different insights, so you may want to see all models if you have the resources (even if in the end you decide for an ensemble method strategy to optimize predictive power). Resources (programmer's time, computational power ...) may be limited, but often (especially with large databases) one option will be much more expensive than another option. So then the cheaper, often more exploratory options may be good to try first. After seeing results of such first (exploratory) results, people may understand the problem better and agree more easily on next steps.

One thing I have found useful is not to ignore the options you have. Comparing the options is more important in dataset see what brings out the metrics the most. When comparing the options I look for which algorithm brings more the exact data i am looking for. So don’t ignore that in data no algorithm is unusable all the thought are welcomed, it helps the team to push hard on thinking and it brings out the best data and ideas the enterprise need

Pilot testing is crucial in validating algorithm choices for data mining. Start by selecting representative datasets and implementing selected algorithms on a smaller scale. Evaluate performance against predefined metrics and gather feedback from stakeholders. Use results to refine algorithms and identify potential challenges early. Document findings rigorously to inform scaling decisions and ensure alignment with project goals. Pilot testing minimizes risks, builds confidence, and sets the stage for successful implementation of optimal algorithms in broader applications.

When selecting the optimal data mining algorithm, it's crucial to experiment with multiple models and evaluate their performance using metrics that align with the nature of the problem. Below are the relevant metrics to be used for each problem type. 1- Classification Problems: Accuracy, Precision, Recall, F1-Score, Confusion Matrix, ROC-AUC 2- Regression Problems: RMSE (Root Mean Squared Error), MAE (Mean Absolute Error), R² Score, MAPE (Mean Absolute Percentage Error) 3- Clustering Problems: Silhouette Score, Davies-Bouldin Index, Dunn Index, Adjusted Rand Index

Realizar testes-piloto é uma estratégia valiosa para avaliar o desempenho dos algoritmos antes de tomar uma decisão final. Aqui estão algumas etapas adicionais para conduzir um teste-piloto eficaz: Seleção da Amostra: Escolha uma amostra representativa dos seus dados. Divida a amostra em conjuntos de treinamento e teste. Execução dos Algoritmos: Execute os algoritmos selecionados na amostra de teste. Avalie métricas relevantes, como precisão, recall, F1-score e tempo de execução. Análise dos Resultados: Compare os resultados dos algoritmos. Discuta os resultados com sua equipe. Ouça diferentes perspectivas e opiniões. Use a evidência empírica para influenciar a decisão final.

Prototyping (and pilot testing) is pretty much the initial proof that the solution works. It does not mean it is the final solution, but provides enough insights to take that decision later on.

If the data you are working on is too large, then before running an algorithm on the whole data and releasing those insights onto the public, It is better to divide your data into clusters. Clustering is a very powerful tool which divides your data into different clusters so that you can view your insights in greater detail and in more understandable way. When you document your initial findings and realised that this is the maximum insight I can achieve from this data then run your chosen algorithm on whole data and analyse those findings again. Is that final findings align with my initial findings or not? Does it again needs standardisation or clustering further?

Adding to the conversation - It is super important to evaluate the different perspectives of the challenge you are trying to overcome and the correct metrics to measure. You must really evaluate what you are trying to solve at both a business level and implementation/functional level with experts. Tuning a model to metrics that make the model useable are just as important as trying to optimize accuracy or minimizing loss. Additionally, experts can provide insights that support tuning efforts of models speeding up the development process. Make sure you are listening and considering your expert's advice when choosing the algorithm to apply to improve model usability.

In navigating algorithm choices for data mining amidst team divisions, seeking expert insights proves pivotal. Begin by fostering open dialogue to understand diverse perspectives and rationale behind preferences. Encourage structured evaluations of algorithms based on project goals, data characteristics, and performance metrics. Emphasize empirical testing and prototyping to validate assumptions and mitigate biases. Leverage collective expertise to brainstorm alternatives and simulate scenarios, fostering a collaborative environment. Document insights and decisions meticulously to facilitate continuous improvement and consensus-building.

Para enriquecer nuestro análisis, buscamos la opinión de expertos externos. Invitamos a dos empleados de la empresa, uno con perfil de profesor de una universidad local con experiencia en data mining y a un analista senior. Sus perspectivas externas nos ayudaron a ver ángulos que no habíamos considerado y validaron algunos de nuestros hallazgos preliminares.

Aslında burada lazım olan gerçekten bir uzman görüşüdür diye düşünüyorum. Yapılacak olan diğer karar alma süreçleri hep detayda ve gerçekçi olmayacaktır bana göre. Benim şu ana kadar kimisti hayatımda bir yöntem kullanırken birinin tecrübesinden yararlanmak en ideal çözümdür diye düşünüyorum bu aynı zamanda para tasarrufuna ve zaman tasarrufuna da büyük katkı sağlayacaktır

Encourage knowledge sharing. Facilitate team discussions to pool expertise and insights on different approaches Seek external input. Consult industry experts or academic research for cutting-edge algorithm insights.

If team is divided, then it smells trouble. To reunite the team, let all models participate and let the best solution win. This will require time. Also, sometimes even the best choice ends up less optimal - say data changed and the initial "winner" starts losing. So run both in parallel, and see in practice. Competition is not about winning, it is about learning.

Facilitate a Workshop or Meeting: Organize a dedicated session to discuss algorithm choices, ensuring it’s a structured and focused environment. Ensure Every Voice is Heard: Encourage all team members to share their perspectives and concerns. This inclusivity fosters a sense of ownership and respect. Use Voting or Ranking Techniques: Implement methods like anonymous voting or ranking to gauge the collective preference for different algorithms. This can help quantify opinions and identify the most favored options. Foster Respect and Consideration: Create an environment where all opinions are respected and considered. Acknowledging different viewpoints can lead to a more balanced and comprehensive decision.

Start by clearly defining the project goals and understanding the business context. Assess the available data for quality, volume, and structure, as these influence algorithm suitability. Facilitate open discussions to leverage diverse expertise and perspectives on various algorithms. Consider accuracy, scalability, interpretability, and computational efficiency. Conduct exploratory data analysis, review relevant literature to narrow down potential algorithms. Perform pilot tests on sample datasets to empirically compare algorithm performance. Foster a collaborative environment, feedback and iterative refinement. Make the final decision based on a combination of empirical results, team consensus, and alignment with project objectives.

Fortunately, data mining has one major advantage when it comes to building consensus: data. The dataset has its own, objective reality, in that it is what it is and is the same for each analyst. Analyses and interpretations will, of course, vary, but if the patterns you find with one algorithm are truly valid, then there will be consensus, or at least convergence, with other approaches. So, the team doesn't need to operate on the basis of social clout or blind faith: run the algorithms and see what you find. If it's real, you'll find it using more than one approach. If the results differ, then there's a need to investigate why the methods differ in their sensitivity to to quirks of the data, but the focus should always be on the data.

Con toda esta información en mano, llegó el momento de construir consenso. Facilitamos una sesión de grupo donde cada sub equipo presentó sus hallazgos. Utilizamos técnicas de decisiones grupales como el método Delphi para llegar a un acuerdo. Fue un proceso colaborativo; no todos estaban de acuerdo al inicio, pero al final logramos un compromiso al optar por Random Forest combinado con técnicas de validación cruzada para mejorar la robustez del modelo.

Continuous learning is essential in data mining to stay ahead. Encourage ongoing education on emerging algorithms, techniques, and industry trends through courses, webinars, and conferences. Foster a culture of knowledge sharing within the team to exchange insights and best practices. Implement regular reviews of algorithm performance and feedback loops to adapt strategies dynamically. Embrace experimentation with new tools to innovate and optimize processes continually. By prioritizing continuous learning, teams can enhance expertise, adapt to evolving challenges, and maintain competitive advantage in the dynamic field of data mining.

Finalmente, adoptamos una mentalidad de aprendizaje continuo. Mantenemos un registro de las decisiones algorítmicas, los resultados y las métricas de rendimiento. Esto nos permite seguir aprendiendo y adaptando nuestras estrategias en función de las lecciones obtenidas. También organizamos sesiones mensuales de revisión para discutir nuevas tendencias en minería de datos y posibles mejoras.

When I am drowning in tasks for a data analytics project .To ensure I meet the deadlines effectively i visualize all the tasks and then I start doing one by one task I avoid jumping from one to another. To be effective I start from the easiest one to the hardest. It helps me gain time and meet the deadlines.

Consider (and plan accordingly) that the model is a living entity. It is born, grows, matures and eventually dies (when the accuracy, or whatever metric of interest degrades enough that impacts negatively the expected contribution. In other words, you start loosing money using that model). Therefore is imperative to have specific teams continuously monitoring the performance of said models and to provide early warning of degradation so recalibration, or deployments are prepared in time. All in order to protect the expected contribution of the model to the P&L.

Although the dataset is "objective" in that it consists of verifiable observations for known cases, remember that all analyses are fundamentally exercises in simplification and interpretations. This is especially true in data mining, which has an inherently higher risk of false positives than other analytical tasks. As such, it's always important to remember that data is a human creation, in that no data exists until some person decides to create methods to measure and gather it. You'll do better when you have a good understanding of the people behind the data, as well as the way it is experienced and used.

The choice of data mining algorithm depends on both the characteristics of dataset available and the desired outcome of analysis. By considering these 2 factors, one can select the most appropriate algorithm to extract the meaningful insights from the data.

Data is growing at a rapid pace. The amount of data which is being managed by any company now doubled in just 18 months. This is the harsh truth. Billions of dollars worth of money, time and resources has been spent by the company to manage data and so is our responsibility to stay ahead with the trend by investing our valuable time and resources in learning it.